U.S. patent application number 13/947306 was filed with the patent office on 2013-11-21 for systems and methods for determining optimal pricing and risk control monitoring of auctioned assets including the automatic computation of bid prices for credit default swaps and the like.
This patent application is currently assigned to Peak Silver Advisors, LLC. The applicant listed for this patent is Peak Silver Advisors, LLC. Invention is credited to Mukesh Chatter, Rohit Goyal, Shiao-bin Soong.
Application Number | 20130311351 13/947306 |
Document ID | / |
Family ID | 42173067 |
Filed Date | 2013-11-21 |
United States Patent
Application |
20130311351 |
Kind Code |
A1 |
Chatter; Mukesh ; et
al. |
November 21, 2013 |
Systems and Methods for Determining Optimal Pricing and Risk
Control Monitoring of Auctioned Assets Including the Automatic
Computation of Bid Prices for Credit Default Swaps and the Like
Abstract
Embodiments of the invention provide an innovative,
fully-automated system that facilitates the buying and selling of
debt-based derivatives and other assets. The techniques described
herein eliminate opaqueness, inefficiencies, and lack of risk
monitoring and provide an end-to-end, highly efficient
reverse-auction platform that considers many aspects of risk
control and other parameters. This is accomplished while computing
a true CDS price by incorporating reference entity, primary and
secondary insurance company default risks. Furthermore, the
reference entity pricing model decouples the borrower from the
entity issuing the debt and eliminates rating inflation due to
digital discontinuity.
Inventors: |
Chatter; Mukesh; (Concord,
MA) ; Goyal; Rohit; (Cambridge, MA) ; Soong;
Shiao-bin; (Littleton, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Peak Silver Advisors, LLC |
Boston |
MA |
US |
|
|
Assignee: |
Peak Silver Advisors, LLC
Boston
MA
|
Family ID: |
42173067 |
Appl. No.: |
13/947306 |
Filed: |
July 22, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12586858 |
Sep 29, 2009 |
8521566 |
|
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13947306 |
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61141124 |
Dec 29, 2008 |
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Current U.S.
Class: |
705/37 |
Current CPC
Class: |
G06Q 40/08 20130101;
G06Q 40/06 20130101; G06Q 40/04 20130101 |
Class at
Publication: |
705/37 |
International
Class: |
G06Q 40/04 20060101
G06Q040/04 |
Claims
1. A method of providing a fully automated computerized facility
for the buying, selling, or pricing of debt-based derivatives, that
comprises: providing an automated system amongst a plurality of
automated computer engines known as "SAEJ" operating on one or more
computer processors over a communication system or network that
takes into account many aspects of risk control and includes
soliciting from responding SAEJ engines of automatic real-time
price quotations, without manual intervention, in response to a
buy, sell, or pricing request received from an initiating computer
processor; inputting selected aspects of financial risk control and
other parameters to one or more of the SAEJ engines in the SAEJ
automated system in order to provide transparency and risk control;
evaluating risk controls after inputting to the SAEJ engines a
default risk evaluation of one or more of reference entities,
primary insurance companies, and secondary insurance companies;
determining, by one or more of the SAEJ engines, a real-time price
quotation; and selecting, by a controlling computer processor or
the initiating computer processor, in response to the received
price quotations, a winning bid and either determining pricing or
transacting purchase and sale of the debt-based derivative based on
the winning bid.
2. The method of claim 1 further comprising inputting, to one or
more of the computer processors of the SAEJ engines, a risk
position of failure of the underwriter of or dealer in the
derivative.
3. The method of claim 2 wherein a seller is an investment bank
acting as an underwriter and dealer and guarantor to a buyer should
a reference entity default on the debt.
4. The method of claim 3 further comprising the seller purchasing
insurance from a primary and/or secondary insurer to cover its risk
should the reference entity default, or if the seller's capital
reserve is insufficient to pay against demand, or in the event of
other major market fluctuations.
5. The method of claim 1, further comprising decoupling, by one or
more of the SAEJ computer processors, reference entity pricing from
the entity issuing the debt-based derivative and using the
decoupled pricing for determining or monitoring, by the SAEJ
computer processors, the real-time price quotation.
6. The method of claim 5 wherein the decoupling of the borrower
from the entity issuing the debt further eliminates rating
inflation due to digital discontinuities.
7. The method of claim 1 further comprising at least one of the
computer processors of the SAEJ pre-screening the debt instrument
to determine whether it meets specified criteria for a securitized
pool, giving consideration to credit score and other criteria
evaluated in the context of optimized objectives for the pool.
8. The method of claim 7 further comprising at least one of the
computer processors of the SAEJ creating a securitized pool and
specifying respective targets and constraints in advance such that
the pools are created to meet requirements, thereby creating an
optimized pool instead of one built up on an ad-hoc basis following
the acquisition of assets and prior to building the pool.
9. The method of claim 8 further comprising pre-screening assets
that do not meet minimum requirements prior to optimization of the
pool around specified targets and respective constraints.
10. The method of claim 8 further comprising at least one of the
computer processors of the SAEJ, in order to implement risk
monitoring and control, optimizing quantitative business objectives
based on targets for one or more of respective capital reserves,
how much risk is to be taken, and what is the average rating of the
debt already written.
11. The method of claim 10 wherein implementation of risk
monitoring and control further uses a coverage quality efficiencies
parameter computed by minimizing the aggregate distance to the
buyer's desired target values.
12. The method of claim 11 further comprising the SAEJ computer
processor of each dealer initiating a real-time instantaneous
reverse auction among its primary insurance companies to secure the
best price prior to incorporating it in its own optimized price
quotation computation for each potential transaction.
13. The method of claim 11 further comprising the SAEJ of each
dealer optimally computing the coverage quality efficiency
parameter and manages the real-time quotation process with
weighting of some or all parameters provided by an initiator SAEJ
(iSAEJ).
14. The method of claim 13 further comprising the iSAEJ providing
ranges for some or all of the parameters, as distinguished from a
single target value.
15. The method of claim 13 further comprising the iSAEJ analyzing
what is currently owned to identify remaining objectives and
distance in order to provide the parameters.
16. The method of 15 wherein the parameters provided by the iSAEJ
include at least one of corresponding dealer's reserve capital,
sector concentration, primary insurance coverage, and secondary
insurance coverage.
17. The method of claim 15 further comprising the iSAEJ identifying
the parameters of importance to provide for the real-time quotation
process.
18. The method of claim 17 further comprising the iSAEJ computing
variations between values offered and what is desired for each
participating dealer in order to determine each dealer's coverage
quality efficiencies parameter.
19. The method of claim 18, further comprising the iSAEJ analyzing
each coverage quality efficiencies parameter in conjunction with a
price offered by a corresponding dealer in order to compare
quotations from all dealers.
20. The method of claim 13, further comprising the iSAEJ assigning
a unique weight to each of the provided parameters.
21. The method of claim 7 further comprising at least one of the
computer processors of the SAEJ engines using analog quantification
instead of digital for one or more of credit scores, debt-to-income
ratio, price or value of the assets, and borrower income level,
thereby eliminating the impact of digital discontinuity and
avoiding rating creep-up.
22. The method of claim 7 further comprising at least one of the
computer processors of the SAEJ pre-screening based on its own
unique objectives and computing a price quotation for the
derivative instrument if it meets pre-screening criteria.
23. The method of claim 22 wherein when a request is received by
the SAEJ of a dealer, the one or more computer processors of the
dealer SAEJ consider the sector of the request for decision as to
acceptance in the asset pool in order to maintain the relative
ratio among the various sectors so that any dramatic decline in one
does not severely distress the entire portfolio.
24. The method of claim 23 further comprising at least one computer
processor of the SAEJ engines modulating the price of the
debt-based instrument for each reference entity based upon one or
more of the dealer's default coverage, the rating quality of the
primary and/or secondary insurance, the magnitude of reserve
capital diversification, the sector diversification, and the
competitive market pricing at that instant in time, coupled with
the dealers status with regard to its own unique targets and the
time left to meet the same.
25. The method of claim 24 further comprising operating an on-line
market place in which the debt instrument buyers, sellers and
entities such as the primary and secondary insurance companies,
participate in a real-time auction with comprehensive pricing
models encompassing various risk parameters associated with the
market participants used in the SAEJ calculations of an optimum
price.
26. The method of claim 25 further comprising the controlling
computer processor finding the best price quotation amongst the
SAEJ engines and sending it back to each of the responders to
determine if anyone can beat the best value offered so far, each
responding SAEJ evaluating the best price quotation received in the
first round, re-computing its best price quotation and determining
if it can beat such price, whereupon it then resubmits its
progressively lower price quotation, such that this iterative and
competitive process is automatically repeated in real-time until
only one responder is left.
27. The method of claim 24 further comprising updating the debt
default rate as a function of time, thus providing visibility at
the lowest borrower level.
28. The method of claim 27 further comprising providing one or more
of the SAEJ engines access for analyzing of risk parameters and for
examining the risk profile of the pool at the origin.
29. The method of claim 28 further comprising monitoring, by at
least one of the computer processors of the SAEJ, the quality of
the pool as a function of time after the debt-based derivative has
been underwritten.
30. The method of claim 29 further comprising automating, by one or
more of the SAEJ engines, reference entity pricing by accounting
for at least one of the default risk of the underwriter and the
potential loss of coverage in primary and/or secondary insurance,
thus providing a meaningful price.
31. The method of claim 30 further comprising identifying and
quantifying risk control parameters to maintain the parameters
within acceptable ranges as defined by the dealer according to its
own business objectives.
32. The method of claim 31 further comprising conducting, by the
controlling computer processor, a transparent on-demand,
24.times.7, real-time iterative auction between the responding SAEJ
engines to discover the price of the debt-based derivative.
33. The method of claim 31 further comprising one or more of the
SAEJ engine processors computing requisite capital reserve given an
input risk, and factoring the computed reserve in pricing or
decision to participate in any transaction.
34. The method of claim 31 further comprising at least one of the
SEAJ engines computing targets indicating optimum risk to take
given an input capital reserve.
35. The method of claim 34 further comprising at least one of the
computer processors of the SAEJ engines changing the amount of risk
under monitoring feedback control of the SAEJ.
36. The method of claim 34 further comprising at least one of the
SAEJ engines applying risk control to the price of the debt-based
derivate and determining if the capital reserve warrants this risk
and keeps the risk within narrow confines.
37. The method of claim 36 further comprising constructing a
securitized pool of assets with a disconnect between the borrower
and the lender effected by providing full visibility in the
construction of the pools.
38. The method of claim 34, further comprising one or more of the
SAEJ engine processors controlling the risk with feedback provided
on a dynamic basis to prevent runaway of the risk.
39. The method of claim 1 further comprising at least one of the
SAEJ engines constructing a securitized pool including the
debt-based derivative and optimized for one or more of credit
scores, debt-to-income ratio, value of the assets, and borrowers
income level, and using the securitized pool for generating or
evaluating price quotations.
40. The method of claim 39, further comprising one or more of the
SAEJ engine processors measuring change of debt quality over time
in the securitized pool based on change related to one or more
underlying assets.
41. The method of claim 39, further comprising one or more of the
SAEJ engine processors dollar-weighting at least one of the credit
scores, debt-to-income ratio, value of the assets, or borrowers
income level used in pool construction.
42. The method of claim 1 wherein constraints on the pricing and of
the optimization function are categorized as at least one of
risk-based controls, underwriter constraints, reference entity
constraints, and insurer constraints, and in which the risk-based
constraints include the existence of one or more of the requisite
capital reserve to cover the assets, the average default risk, and
the capital sufficiency index; the underwriter related constraints
including the minimum liquidity index or reserve, the minimum
diversification of reserve and the minimum floor price constraints;
the reference entity constraints includes one or more of global and
sector constraints; and the insurance constraints include one or
more of global and sector constraints.
43. The method of claim 1, further comprising decoupling, by one or
more of the SAEJ computer processors, reference entity pricing from
the entity issuing the debt-based derivative, and using the
decoupled pricing for determining or monitoring, by the SAEJ
computer processors, the real-time price quotation.
44. A system for providing a fully automated facility for the
buying, selling, or pricing of debt-based derivatives, comprising:
a controller computer processor having non-transitory storage with
instructions programming the processor to communicate with a
plurality of automated computer engines known as "SAEJ" connected
over a computerized communication system taking into account many
aspects of risk control, and to solicit from the SAEJ engines of
their automatic real-time price quotations without manual
intervention; one or more of the computer processors within the
SAEJ computer engines, the SAEJ computer engines having
non-transitory storage with instructions programming the processors
to: receive inputs of selected aspects of financial risk control
and/or other parameters to provide transparency and risk control;
evaluate risk controls after receiving a default risk evaluation of
one or more of reference entities and of any primary and/or
secondary insurance companies; and determine the real-time price
quotation by optimizing a price of a debt-based derivative; wherein
the controlling processor or an initiating computer processor
selects, in response to the received price quotations, a winning
bid and either determines pricing or transacts purchase or sale of
the debt-based derivative based on the winning bid.
45. The system of claim 44 wherein the SAEJ engine computer
processors are further programmed to decouple reference entity
pricing from the entity issuing the debt-based derivative and use
the decoupled pricing while optimizing the price.
46. The system of claim 45 wherein one of the SAEJ engines is
controlled by a seller of the debt-based derivative, and the seller
purchases insurance from a primary and/or secondary insurer to
cover its risk should the reference entity default, or if the
seller's capital reserve is insufficient to pay against demand, or
in the event of other major market fluctuations.
47. The system of claim 44 wherein at least one computer processor
of the SAEJ engines is programmed to provide a pre-screener to
prescreen the debt instrument to determine whether it meets
specified criteria for a securitized pool, giving consideration to
credit score and other criteria evaluated in the context of
optimized objectives for the pool.
48. The system of claim 47 wherein at least one of the SAEJ
computer processors is further programmed for one or more
pre-screening criteria based on its own unique objectives, and
computes a price quotation amount if the derivative instrument
meets the pre-screening criteria.
49. The system of claim 48 wherein at least one of the SAEJ
computer processors is further programmed to determine a sector of
the debt-based derivative, and determines whether to compute or
evaluate a price for the debt-based derivative based on maintaining
a relative ratio among various sectors of an asset pool controlled
by a buyer or seller associated with the SAEJ, so that any dramatic
decline in one does not severely distress the entire portfolio.
50. The system of claim 49 wherein at least one of the SAEJ
computer processors is further programmed to compute and modulate
the price of the debt-based instrument for each reference entity
based upon one or more of the dealer's default coverage, the rating
quality of the primary and/or secondary insurance, the magnitude of
reserve capital diversification, the sector diversification, and
the competitive market pricing at that instant in time, coupled
with the dealers status with regard to its own unique targets and
the time left to meet the same.
51. The system of claim 50 further comprising an on-line market
place connected to the controller processor and SAEJ, in which the
debt instrument buyers, sellers and entities such as the primary
and secondary insurance companies, participate in a real-time
auction with comprehensive pricing models encompassing various risk
parameters associated with the market participants used in the SAEJ
calculations of an optimum price.
52. The system of claim 51 wherein the controller computer
processor is further programmed to find the best price quotation
amongst the SAEJ engines and send it back to each of the responder
SAEJs to determine if anyone can beat the best value offered so
far, each responding SAEJ evaluating the best price quotation
received in the first round, re-computing its best price quotation
and determining if it can beat such price, whereupon resubmitting
its progressively lower price quotation, such that this iterative
and competitive process is automatically repeated in real-time
until only one responder is left.
53. The system of claim 47 wherein the securitized pool is created
and respective targets and constraints are specified in advance
such that the pools are created to meet these requirements, thereby
creating an optimized pool instead of one built up on an ad-hoc
basis following the acquisition of assets and prior to building the
pool.
54. The system of claim 53 wherein pre-screening filters out assets
that do not meet minimum requirements prior to optimization of the
pool around specified targets and respective constraints.
55. The system of claim 53 wherein one or more of the SAEJ engine
processors is further programmed to measure debt quality of the
securitized pool over time based on change related to one or more
underlying assets.
56. The system of claim 47 wherein at least one of the SAEJ
computer processors is further programmed to access and analyze
risk parameters beyond intermediaries, and examine the risk profile
of the pool at the origin.
57. The system of claim 47 wherein at least one of the SAEJ
computer processors is further programmed to monitor the quality of
the pool as a function of time after the debt-based derivative has
been underwritten.
58. The system of claim 57 wherein at least one of the SAEJ
computer processors is further programmed to automate reference
entity pricing by accounting for one or more of the default risk of
the underwriter and the potential loss of coverage in any primary
and/or secondary insurance, thus providing a meaningful price.
59. The system of claim 58 wherein at least one of the SAEJ
computer processors is further programmed to identify and quantify
risk control parameters to maintain the parameters within
acceptable ranges as defined according to its own business
objectives.
60. The system of claim 59 wherein the initiator, controller, and
SAEJ provide a transparent on-demand, 24.times.7 real-time
iterative auction to discover the price of the debt-based
derivative.
61. The system of claim 44 wherein at least one of the SAEJ
computer processors uses analog quantification, instead of digital,
for one or more of credit scores, debt-to-income ratio, price or
value of the assets, and borrower income level, thereby eliminating
the impact of digital discontinuity and avoiding rating creep-up.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/586,858, filed Sep. 29, 2009, titled
"Systems and Methods for Determining Optimal Pricing and Risk
Control Monitoring of Auctioned Assets Including the Automatic
Computation of Bid Prices for Credit Default Swaps and the Like" in
the name of Mukesh Chatter, Rohit Goyal, and Shiao-bin Soong, which
claims priority from U.S. provisional patent application
61/141,124, filed Dec. 29, 2008, both of which are hereby fully
incorporated by reference.
FIELD OF INVENTION
[0002] The present invention relates broadly to the field of
web-based e-commerce, including on-line computation for computing
bid prices for derivative financial instruments such as credit
default swaps (CDS), using various risk and quality factors in the
control strategy for auctioning such financial instruments over the
internet; where the term "internet" is used herein to embrace
generically all types of public and/or private communication
networks employing wireless and/or wired transmission media, and
combinations of the above, and also specifically the satellite
world wide web. More particularly, the effecting of the price and
risk control strategy for auctioned CDS and similar assets in
accordance with the invention is specifically involved with the use
of novel computer architectures containing automated engines such
as the seller automated engines (SAEJ) describe in detail in
co-pending U.S. application Ser. Nos. 11/367,907 (Publication No.
US 2007-0208630 dated Mar. 3, 2006); 11/880,980 (Publication No. US
2009-0030829 dated Jul. 25, 2007) and 11/974,808 (Publication No.
US 2009-0099902 dated Oct. 16, 2001), the entire disclosures of
which, as stated in the before-mentioned co-pending provisional
application, are incorporated herein by reference, and are
preferably used in the computers executing the procedures of the
present invention, as shown in later-described FIG. 2, though other
search engines performing similar functions may be used, as later
explained.
[0003] While the thrust of said co-pending applications and
publications is primarily directed to the on-line auctioning of
individual goods and services, the present application is more
specifically directed to derivative financial instrument-swapping,
often referred to as the before-mentioned credit default swaps
(CDS); such frequently being used as a hedge against the potential
default of, for example, a mortgage or a debt instrument;
(including bonds or secured debts), often bundled together, though
often separately owned by different and unrelated entities.
BACKGROUND OF THE INVENTION
[0004] Whereas the setup and adjustments of the computer systems,
including search engines of said co-pending applications and
publications for the holding of on-line live auctions for goods and
services, is particularly described therein, the architecture
system and set up for bundled financial instruments, including
operating a pricing and risk control strategy for
internet-auctioned credit default swaps, using various risk and
quality factors, is quite different and novel for such auctioning
by the automatic computing of such bid prices for credit default
swaps and the like. It is thus in order first to examine the unique
problems created by such bundled financial instruments as credit
default swapping, and as contrasted from individual goods and
services.
[0005] A credit default swap (CDS) is a derivative financial
transaction and instrument that, to the detriment of the current
worldwide economy, is often used to hedge against the potential
default of an obligor on a debt instrument, such as bonds or
secured debts--particularly, mortgages--and specifically where such
are bundled together with other unrelated debt instruments of
others and used as relatively new types of financial derivative
instruments for such potential default hedging.
[0006] Typically, there are three primary entities that participate
in the creation and bundling of such diverse assets for the buying
and selling of a CDS: (1) the underlying entity issuing the debt,
(2) the seller of the CDS, and (3) the buyer. The obligor of the
debt, sometimes referred to herein as the "reference entity" (see
later-described FIG. 1), is the entity for whose debt the coverage
is being bought and sold. There may be many types of such reference
entities, such as corporations that issue debt directly, entities
owning a pool of assets such as a securitized pool of mortgages,
credit card debt or auto loans; or in some instances, it may be an
index. The "buyer" (typically a hedge fund or a mutual fund)
purchases the CDS in the market from sellers, usually as a hedge
position against other assets under management. The "seller"
(usually an investment bank) acts as the underwriter and dealer and
guarantees payments to the buyer should the reference entity
default on the debt.
[0007] In some instances, the seller purchases insurance from a
primary insurer to cover its risk should the reference entity
default, or the seller's capital reserve is not sufficient to pay
against the demand, or other major market fluctuations. Secondary
insurance may also be purchased to further limit the risks of the
parties.
[0008] The buyer of a CDS is not required to own the underlying
bonds of the reference entity. The CDS provides downside protection
when markets turn negative as debt default rates for businesses go
higher, and the corresponding equity prices drop, and as a result
the bond ratings go down. Because the income stream generated by
the CDS was based on better market conditions, its value increases,
thus offsetting some of the losses.
[0009] Since the CDS market is largely over-the-counter (OTC),
there is little visibility into the risks associated with the
various entities that participate in the market. As such, there is
currently no meaningful mechanism to arrive at an accurate market
price that reflects the quality of each entity and of the
underlying debt instruments, which results in significant pricing
inefficiencies.
[0010] As an example, a hedge fund may wish to purchase coverage
for $10M of `AA` rated Ford Motor Company bonds from an underwriter
(seller) in the form of a CDS. The seller evaluates the Ford bonds,
the various credit agency ratings of the bonds, the status of the
company in current market conditions, and the prospects for future
growth (or lack thereof)--determining a price, typically using
internal, proprietary pricing models. A private transaction is then
consummated between the hedge fund and the investment bank having
particular timing (typically between three and ten years) and
payment terms.
[0011] The CDS market is a highly lucrative business whose original
intent was to provide entities with a way to hedge against debt
default. Over the past decade, the size of the CDS market has
multiplied many-fold, resulting in tens of trillions of dollars of
notional value for which, however, there is no reserve. To
complicate matters, buyers of the CDS instruments cannot accurately
quantify their risk exposure when purchasing such instruments, nor
is there any structured, repeatable process to accurately discover
and validate pricing at the time of purchase or resale. These same
risks plague the insurance companies providing the coverage to the
underwriter.
[0012] Because the terms of CDS transactions are confidential,
moreover, none of the buyer, seller or insurance companies has the
necessary visibility into each other's aggregated risk positions in
order to accurately assess the true value of the CDS. As a result,
the margin on an appropriate risk premium is significantly
higher.
[0013] For example, while certain pricing models are assumed to
account for the risks associated with the reference entity (which
is not always the case, as explained below), such models do not
account for the default risk or the increased risk of failure of
the underwriter itself, or those of its insurance companies. Thus,
the buyer of the CDS may have accounted for the risk associated
with the reference entity, but remains largely exposed to risks
associated with the underwriter. To further compound the exposure,
default risks associated with the primary and secondary insurance
companies are also not factored into the CDS pricing model.
[0014] Any catastrophic failure of an underwriter, accordingly,
will necessarily result in a loss of coverage for bonds held by the
numerous entities who bought the CDS assets as a hedge against the
bonds, forcing the sale of the bonds at pennies on the dollar.
Furthermore, hedge funds that bought the CDS assets as a potential
hedge to protect their position in the underlying security, or as
pure speculation, will not be able to collect the gains, causing a
panic in the market with disastrous consequences. Such effects are
multiplied when the insurer of the risks does not have sufficient
reserves, and/or the risks were not appropriately distributed among
other insurance companies, resulting in further market
collapse.
[0015] Another issue that limits the ability of the parties to
accurately price CDS assets is so-called "digital discontinuity",
and the differences in resolutions of the debt ratings at various
stages of the process.
[0016] Consider, as an illustration, a pool of mortgage-backed
securities comprising mortgages to individuals across the credit
score spectrum. Individuals having a credit score below 580 are
typically considered sub-prime borrowers; those above a score of
620 are classified as either `alt A` or `A Paper` depending upon
the quality of the documentation and the ratio of monthly payment
to income, and others are classified as "Optional ARM". Using these
groupings, mortgages may be categorized into four classes of debt.
In practice, this is analogous to using an analog-to-digital (A/D)
conversion having a 25% resolution. The mortgage broker or bank
and/or other intermediaries then create pools of mortgages having
these designations and sell them to investment banks. The
investment banks then use the debt-rating agencies and apply
another, higher-resolution digitization scheme--effectively using a
second-stage, higher bit ND converter. The investment banks then
carefully mix and match assets to create pools such that the new
pools barely meet the requirements of a quality rating, taking
advantage of the digital discontinuity.
[0017] As an example, consider a pool that can barely acquire a
rating of `AA`. Once assigned this rating (and no matter how many
sub-prime mortgages are in it), no distinction is made between it
and other debt pools that were not deliberately so packaged and are
truly `AA` rated debt. Considering that the original resolution at
the origination of a mortgage was much coarser, the refinement of
the resolution in the second stage is gated by the first stage
resolution. Once the pools are established, the investment banks
issue bonds, which in turn may be rated based on the overall credit
risk of the bank--again obfuscating the actual quality of the
underlying debt. The bond buyer may now purchase a CDS based on the
debt, but because the actual quality and risk of the debt pool has
been manipulated, the determining of a true price for the CDS is
difficult.
[0018] Even the existing reference entity default risk pricing
models suffer from serious limitations. Although they tend to work
well while the borrower and the issuer of the bond are one and the
same entity, the accuracy is significantly reduced when that is not
the case. For example: [0019] there may be a significant difference
between the debt ratings at the origin of the bond in contrast to
the current rating of the bond issued by a third party using that
debt; [0020] the impact on the overall debt quality is not well
understood when asset pools are created for the purposes of
securitization and it is very difficult to maintain a consistent
quality throughout the pool; [0021] the process of creating the
debt pools is not done concurrently with the issuance of the debt,
and therefore is sub-optimal; [0022] there is no easy way to update
the pool rating if there is deterioration or enhancements in the
end borrower's credit quality; and [0023] there is no easy way to
continuously monitor the quality of the key characteristics of the
pool, thus making any meaningful risk monitoring and risk control
difficult.
[0024] In addition to the difficulties of using conventional
reference entity risk modeling techniques to price the CDS
instrument, the risks associated with the other market participants
are virtually ignored. Thus, there are the following primary
factors impacting the CDS price and preventing its accurate
measurement: [0025] 1. Lack of identification, quantification and
monitoring of risks for each of the participants, and requisite
controls to manage such risk within an acceptable range; [0026] 2.
lack of identification and quantification of cross connectivity
parameters among the participants and the magnitude of risk each
one imparts to its other connected participants; [0027] 3.
ill-defined means to reflect the collective impact of such risks on
the final CDS price; and [0028] 4. inability to update the price
appropriately as the debt quality of the end borrowers evolve as a
function of time.
[0029] Taken together, these conditions result in a wide range of
disparity in determining a true CDS price, and a lack of any
meaningful visibility or control on the part of the market
participants to manage the risk, further jeopardizing the validity
of the price. This becomes even more troublesome when an existing
CDS owner wants to sell a CDS that is "in-the-money" --i.e., it has
appreciated in value. Such a purchase requires a cash outlay on the
part of the new buyer, thus adding another parameter to the already
complicated existing pricing process.
[0030] As such, there are numerous questions and concerns that each
entity faces when participating in the CDS market, some of which
are listed below.
CDS Buyer Limitations and Concerns
[0031] Is the CDS priced right? [0032] What is the reference entity
valuation methodology? [0033] How does it account for decoupling of
end borrower versus the issuer of the bonds or other form of debt?
[0034] How are the changes at the lowest level (at borrower's
level) reflected up the risk ladder to the CDS on an on-going
basis? [0035] What is the time lag from the occurrence of an event
at the lowest level (e.g., a mortgage default) to the bonds and
subsequently the CDS derived from the mortgage? [0036] How is the
integrity of the rating system maintained? [0037] What is the risk
of rating inflation and how is it measured? [0038] How accurately
is the dealer default risk measured? [0039] How is the dealer
default risk accounted for in price? [0040] Is the price being
accurately updated in proportion to the underlying debt quality?
[0041] How will it be priced if it need be sold when compared to a
new CDS if the existing CDS is in-money and hence cash outlay is
required on part of the new buyer? [0042] How well is the dealer
controlling its own risk? [0043] How much quantitatively measurable
risk is dealer taking? [0044] What is the magnitude of insurance
coverage in case the dealer defaults? [0045] Is the default risk of
primary and secondary insurance accounted for in the price? [0046]
What is time-weighted notional value of the aggregated CDS
portfolio? [0047] What is the overall CDS portfolio value as a
function of time and as function of expense? [0048] What is the
ratio of aggregated CDS portfolio value to its corresponding
acquisition cost? [0049] What is the ratio of aggregated CDS value
to aggregated notional value? [0050] Is the coverage being bought
optimum for the existing portfolio?
[0051] The makeup of a portfolio of CDS instruments, furthermore,
has an impact on the counterparty default risk. For example, each
reference entity has a corresponding rating, typically provided by
one of the rating agencies; but there is no comprehensive view into
the underlying components of the portfolio which is necessary to
understand the risks associated with the non-reference entities. If
most of the portfolio entities are rated `CCC`, the risk of
reference entities default is high, and therefore the risk
contribution of the underwriter and any support insurance companies
becomes progressively larger. Another fact is that most CDSs have a
nominal maturity duration of five years, but once purchased, the
duration diminishes over time, impacting its value.
[0052] Just as critical, the counterparty risk (i.e., the risks
attributed to the underwriter, and by extension primary and
secondary Insurance companies) can be higher if their CDS portfolio
is weighted towards an industry sector that is under significant
market pressure or operating in an adverse market environment.
Currently, the process to distribute the purchases to various
industry segments or to the segments where large net gains are
anticipated is largely manual.
[0053] Consider, for example, the situation in which a buyer is
heavily concentrated in CDS for mortgage-backed bonds. As these
bonds start to default, the payments become due and the burden on
the counterparty may become too big to handle, resulting in its
default. A manual approach to analyze this risk is not timely, is
highly inefficient and is a drain on the resources.
[0054] In similar vein, if the CDS instruments attributed to a
particular industry sector have a remaining duration, say around
one year, then their value as a function of time is likely to
reduce faster and they may provide less of an opportunity to trade.
If newer CDSs are more expensive (as the sector comes under
significant adverse pressure), the burden to replace the existing
CDSs increases.
CDS Underwriter--Limitations and Concerns
[0055] As with the reference entities of later-discussed FIG. 1,
there are a number of factors contributing to the overall risk
profile of an underwriter which are also not reflected in
conventional CDS pricing models: [0056] The distortion introduced
by the digital discontinuities, and lack of visibility into the
true risks due to the rating methodologies and their reflection on
the underwriter's own rating. [0057] Is the risk well understood at
the lowest unit borrower level? [0058] Each dealer has its own
methodology to compute the default risk of the reference entity.
[0059] The borrower may not be the same entity as the entity
issuing the bonds. [0060] Is there a rating inflation? [0061] Is
digital discontinuity risk well defined and accounted for in the
pricing models? [0062] How is debt default risk updated as a
function of time? [0063] Should the capital reserve be adjusted as
a function of current risk? [0064] The impact of changing market
conditions on the ratings of the reference entities. [0065] Lack of
information about the dollar-weighted average maturity duration of
the portfolio. [0066] What is the visibility depth and granularity
with respect to the underlying debt obligations? For example, if
the underlying assets have variable interest rates potentially
impacting the debt quality in more significant manner, how does it
affect the overall debt quality? [0067] How is valuation change at
the unit level of the underlying asset reflected at the CDS pricing
level? [0068] How is the asset pool constructed? [0069] As various
parameters move, how does it affect the capital reserve
requirements? [0070] What is the ratio of capital reserve to
notional value? [0071] What is the ratio of already-written CDS
value to the corresponding notional value? [0072] What is the ratio
of income generated over a time frame versus the corresponding
aggregated notional value underwritten? [0073] How is the risk
exposure defined and controlled? [0074] What is the relationship
between the aggregated income generation versus the risk exposure?
[0075] How accurate is this information? [0076] Is it constant or
evolving? [0077] How is it controlled, if possible? [0078] What is
the accurate ratio of aggregated CDS value underwritten at
inception to their current aggregated value? [0079] Is there a
diversification in terms of industry sector? [0080] What is the
magnitude of liquidity or how liquid are the reserve capital
assets? [0081] What is the next level of protection if the
underwriter's reserve capital turns out to be insufficient? [0082]
What is the viability of the primary and secondary insurance
companies and their reserves?
Primary Insurance--Limitations and Concerns
[0083] There are also a number of factors contributing to the
overall risk profile of the insurers of the CDS pools, which are
not reflected in conventional CDS pricing models. For example:
[0084] The insurance company's own rating [0085] The insurance
companies capital reserves, as statutory reserve requirements do
not apply to coverage of CDSs. [0086] Where the insurance company
has invested the premiums, and the degree of overlap with the
industry sectors issuing the debt. [0087] Liquidity of the
insurance company's capital reserve. [0088] How the insurance
company's ratings are affected by a downward revision of the
underwriter's financial position? [0089] How are the payoff risks
allocated among secondary insurance companies? [0090] What is the
ratio of premiums collected versus the notional coverage
underwritten? [0091] What is the ratio of default risk to notional
value coverage? [0092] How much capital reserve compared to the
notional value insured is maintained by the secondary insurance
company in case the primary defaults? Is this sufficient? [0093]
How are the reserves invested and what kind of steps has the
secondary insurance company taken to not walk in trouble lock-step
with the underwriter and primary insurance company? [0094] What is
the overlap between a secondary insurer and multiple primary
insurers?
OBJECTS OF INVENTION
[0095] An object of the present invention, therefore, is to provide
a new and improved system and apparatus and procedure and method
for automatically determining a pricing and risk control strategy
operable in real time for on-line auction assets, more
specifically, of derivative financial instruments and the like
requiring the computation of bid prices as for credit default swaps
(CDS), using predetermined risk and quality factors.
[0096] A further object is to provide such a novel pricing and risk
control strategy for derivative financial instruments generally
that are used to hedge against the potential default of an obligor
on a debt instrument, such as bonds, or secured debts, such as
mortgages.
[0097] Still a further object of the invention is to provide an
innovative and fully automated system that facilitates the buying
and selling of debt-based and other assets that avoids the
before-described present day opaqueness and inefficiencies and lack
of risk management; providing, instead, an efficient customer
reverse auction platform that takes into account many of the
above-discussed aspects of risk control, while computing a true CDS
price by incorporating the requirements of the derivative buyer and
any primary and secondary parties that provide insurance of default
risk.
[0098] Still another object is to provide such a novel system
wherein the pricing model de-couples the borrower from the entity
issuing the derivative, through eliminating the inflation of the
rating of the instrument due to digital discontinuities.
[0099] An additional object of the embodiments of the invention is
to provide CDS market participants with a novel transparent and
automatic technique for creating a securitized pool of assets
(later more fully defined) wherein a reverse auction process is
used to discover in real time the true CDS price in the market
among the various participants and fully automated risk control
mechanism maintains dealer risk within a specified range and with
full visibility provided subsequent to the transaction to enable
any changes at the borrower level to be accounted for and reflected
in the current CDS pricing. Given the complex interdependencies and
cross coupling of many variables associated with the entities
involved, another objective of the invention is to enhance the
accuracy of a reference entity valuation model and true price
discovery, herein defined, along with providing clear visibility
into the existing debt quality in real-time, subsequent to the
transaction.
[0100] Another object is to facilitate the identification and
quantification of each variable for each of the entities and their
covariance and to assess the relative magnitude of impact of each
such connection on the other entities and an overall risk
measurement and analysis, while simultaneously eliminating or
substantially minimizing the impact of digitization and ratings
creep-up. As a result, overall risk profiles are quantified and
compared to acceptable risk exposure bounds uniquely specified by
each entity which permits the derivation of a true CDS price and
the construction of securitized pools by pro-actively controlling
and optimizing the acquisition of the assets in a manner consistent
with the requirements of the bond issuers further up in the
chain.
[0101] Other and further objects will be explained hereinafter and
are more particularly delineated in the appended claims.
SUMMARY OF INVENTION
[0102] In summary, from perhaps the most generalized aspects of the
invention in determining a pricing and risk control strategy for
auctioned assets such as credit default swap instruments, using
various risk and quality factors, the invention provides an
innovative, fully-automated system that facilitates the buying and
selling of such debt-based derivatives and other assets. The
techniques described herein eliminate prior opaqueness,
inefficiencies, and lack of risk monitoring, and provide an
end-to-end, highly efficient reverse-auction platform that
considers many aspects of risk control and other parameters. This
is accomplished while computing a true CDS price by incorporating
reference entity, primary and secondary insurance company default
risks. Furthermore, the reference entity pricing model decouples
the borrower from the entity issuing the debt and eliminates rating
inflation due to digital discontinuity in the market.
[0103] Preferred techniques and best modes for practicing the
invention are hereinafter described.
DRAWINGS
[0104] The invention will now be described in connection with the
accompanying drawings, FIG. 1 of which is an entity diagram suited
for over-the-counter (OTC) operation heretofore providing little
visibility into the risks associated with the various entities
participating in the market; and
[0105] FIG. 2 is a block diagram of a CDS automatic real time
auction process in accordance with the present invention,
incorporating the previously mentioned SAEJ automatic engines of
the earlier cited patent applications and publications, disclosures
of which, as before stated in said provisional application, are
deemed to be incorporated herein by reference.
INTRODUCTION TO DESCRIPTION OF INVENTION
[0106] As is evident, this invention involves a multi-source,
inter-dependent, multi staged, multi variable problem in which each
stage contributes its own uncertainties. These, moreover, are then
further attenuated and/or amplified as they progress through the
stages. As described above, the problems underlining the invention
are compounded by the digital nature of the ratings and the
resulting discontinuities which give rise to a higher band of
uncertainties. It also makes the process highly susceptible to
manipulation, as pools that barely meet the standards for an AA
rating, may have very different profiles, that are today treated
the same as those that are actually at the top of the AA rating
tier.
[0107] Considering that the CDS and other similar instruments have
now become key components of the global financial infrastructure,
this lack of transparency and ability to meaningfully quantify the
risk inherent in such instruments has created an intense urgency to
address this problem.
[0108] It may be first in order to define terms that are used
throughout the description of the invention and its improvement in
operation over the prior art.
DESCRIPTION OF PREFERRED EMBODIMENT(S) OF THE INVENTION
[0109] As before explained, the CDS entity diagram of FIG. 1
illustrates a buyer 1 operating through an underwriter/dealer 2,
interfacing with a number of reference entities (shown as three)
through reference entity portfolio targets (buckets for).
Similarly, primary and secondary insurance companies, shown as 3
and 4 interface at their respective portfolio targets 5 and 6. As
before described, FIG. 2 illustrates the auction process in
accordance with the invention, later more fully described.
[0110] It is first in order to explain how the present invention
improves upon current referenced entity pricing models.
[0111] When a borrower, (mortgage or other debt instrument) is
considered for addition to the pool, it is pre-screened to
determine whether it meets the criteria for that pool. For example,
using the criteria above, if the borrowed amount is above $1 M, it
is rejected from consideration. If, however, a borrower has a
credit score higher than 570 and meets the remaining screening
criteria, its debt is evaluated in the context of optimized
objectives. The optimization algorithms synergistically move the
average numbers in a direction to match the objectives as laid out
in advance. Thus, the pool building process is done using a
"Correct by Construction" methodology instead of an existing trial
and error methodology.
[0112] An alternative embodiment of this invention improves the
above by having the seller of the assets, [for example, mortgage
brokers or banks] initiate an auction in which those constructing
the pool participate and compete with each other to acquire the
assets. Each participant provides one or more pre-screening
criteria based on its own unique objectives and computes a bid
amount to acquire the asset. At the completion of the auction, the
entity providing the best bid "wins" the auction and purchases the
asset for its pool. This real-time, iterative auction, as explained
in said publications, ensures that true price discovery occurs.
Coupled with real-time optimization of pool construction by each
buyer, this technique ensures that the pools are created in a
transparent manner and based on the entities criteria, while
avoiding rating inflation. As the buyers (typically investment
banks) of such clean and transparent securitized pools with
unambiguous rating issue bonds against such pools, it will create
further confidence for the buyer of the bond and for the dealer
underwriting CDS, thus creating an end-to-end seamless, and
transparent process free of artificial distortions. Furthermore, as
the debt quality evolves over time, the risk profile of the pool
can be updated for each such change at the underlying asset, and
the quality deterioration or enhancement can be measured. As an
example, if the credit score of the borrower deteriorates from 790
to 530, such an event is reflected in the aggregated debt quality
immediately, as are any changes in the valuations of the underlying
assets. Thus, the debt default rate is updated as the market
conditions change.
DEFINITION OF TERMS
[0113] Initiator: An `initiator` is the entity requesting an
auction for whose benefit others compete. The initiator may be a
buyer or a seller depending on the function being performed. For
example, if a fund is purchasing a new CDS from a dealer, then the
initiator is the buyer of the CDS. Alternately, if a fund wishes to
sell an existing CDS, the seller is the initiator.
[0114] Responder: A `responder` is an entity responding to the
request from an initiator and competing with other responders.
Using the example above, a dealer responding to the request from a
fund to purchase a CDS acts as a responder, and is also a seller of
the coverage. On the other hand, when a hedge fund wants to sell an
existing CDS and a dealer competes with others to buy it, the
dealers act as responders in the capacity of a buyer.
[0115] SAEJ: Seller automated engines, as described in earlier
mentioned co-pending U.S. patent application Ser. Nos. 11/367,907,
11/880,980 and 11/974,808, and their respective publications, the
entire disclosures of which are incorporated herein by
reference.
[0116] iSAEJ: Initiator's SAEJ or SAEJ working as an
`Initiator`
[0117] rSAEJ: Responder's SAEJ or SAEJ working as `Responder`
[0118] Diversification index: A measurement of the diversification
of the assets within a certain asset pool ranging from 1 to 10, 1
being the highest concentration of assets and 10 being the highest
diversification of assets. As an example, if no more than 10% of
the dollar-weighted assets are invested in any industry sector, the
diversification index is 10. On the other hand, if 50% of the
assets are in one sector, its index is 5.
[0119] The ideal diversification is to invest amount A.sub.i.sup.T
to sector i. For an arbitrary allocation, the "distance" to the
ideal allocation is:
d = i = 1 N A i A ( A i - A i T A i T ) 2 ( 1 ) ##EQU00001##
[0120] A distance of zero means perfect diversification, and a
distance of 1 means full concentration on one sector. Thus the
diversification index can be defined as
D=k(1-d), (2)
where k is used as a scaling factor.
[0121] Liquidity Index: A measurement of how quickly an entity has
access to capital. The liquidity index ranges from 1 to 10, one
being least liquid and 10 being the highest liquidity.
[0122] [As an example, the capital reserve of a primary insurance
company may use the following liquidity ratings for different
classes of assets:
TABLE-US-00001 Type of Deployment Liquidity Rating 24 hours or
less/money market 10 Short-term government treasuries 9 Domestic
equity 8 7 day commercial paper 7 28 days money market 6 Foreign
equity 5 3-Month cash enhancement products 4 6-Month cash
enhancement products 3 Private Equity 2 Venture Capital 1
[0123] To compute a dollar-weighted average of liquidity rating to
compute the liquidity index when all the assets are not in just one
class, the following formula may be used:
L = i A i L i A , ( 3 ) ##EQU00002##
where A is the total asset, A.sub.i is the asset invested in asset
class i and L.sub.i is the liquidity rating for class i.
[0124] There can be more nuanced variations of this definition. For
example, if L.sub.i is the liquidity duration of the asset class i,
L can be interpreted as the dollar-weighted average liquidity
duration.]
[0125] As a first step towards creating a transparent process not
subject to current or prior art inadvertent or deliberate rating
creep-ups, embodiments of the invention use a consistent, analog
rating system having a much higher resolution than the current
conventional digital approaches. Further, risks are classified as
either linear or non-linear, and accounted for accordingly.
[0126] Even in instances in which the digital rating techniques are
used, the disclosed techniques can use non-linear true default
rates based on dollar-weighted average to assess the asset
pool.
[0127] The asset pool is created using a set of parameters which
are used to pre-screen each asset. For example, a pool may be
created using the following parameters:
TABLE-US-00002 Minimum acceptable credit core 570 Maximum borrowed
amount $1M Minimum borrowed amount $150,000 Dollar-weighted average
credit core 768 Dollar-weighted average default risk 3% Average
dollar-weighted debt to income ratio 1:3.5 Dollar-weighted average
interest rate 6.3% Dollar-weighted average maturity duration 17
years Size of the pool $100M
[0128] The following are examples of current parameters and targets
used by buyers to identify and quantify risk and pricing associated
with CDSs, the underlying debt, and the other entities in the
market. In some prior implementations, entities may use the risk
monitoring and control techniques described below as an input into
the pricing strategies as part of a market-wide auction, whereas in
other cases such methodologies may be implemented independently and
used solely to quantify the risk exposure of an individual
entity.
Current Status
[0129] Dollar-weighted average rating of the reference entities for
which CDS already owned [0130] Current dollar-weighted aggregated
remaining maturity duration [0131] The diversification index of CDS
already owned [0132] Industry sector by sector weight [0133] The
dollar-weighted aggregated remaining maturity duration on a sector
by sector basis for the CDS already owned [0134] The
dollar-weighted aggregated reference entities ratings on a sector
by sector basis for the CDS already owned [0135] Time weighted
aggregated notional value outstanding [0136] In-money value of each
CDS at any instance in time [0137] Aggregated in-money value at any
instance in time [0138] Ratio of aggregated purchasing cost to
aggregated notional value [0139] Ratio of aggregated purchasing
cost to aggregated in-money value [0140] Ratio of aggregated
present CDS value to aggregated notional value [0141] As this
number goes higher, the CDS owner may want to cash out a part of it
to maintain it at a pre-defined level.
Targets
[0141] [0142] Desired dollar-weighted average rating of the
reference entities [0143] Desired dollar-weighted aggregated
maturity duration objective [0144] The desired diversification
index by industry and/or seller [0145] Desired dollar-weighted
aggregated maturity duration on a sector by sector basis for the
CDS already owned [0146] Desired dollar-weighted aggregated
reference entities ratings on a sector by sector basis for the CDS
already owned [0147] Desired time-weighted aggregated notional
value [0148] Maximum allowed aggregated in-money value at any
instance in time [0149] Maximum allowed ratio of aggregated
purchasing cost to aggregated notional value [0150] Maximum allowed
ratio of aggregated purchasing cost to aggregated in-money value
[0151] Maximum allowed ratio of aggregated present CDS value to
aggregated notional value
[0152] The following are examples of current parameters and targets
used by underwriters to identify and quantify risk and pricing
associated with CDSs, the underlying debt, and the other entities
in the market.
Present Status (e.g., the Existing Portfolio)
[0153] Underwriter's own rating as per the rating agency [0154]
Ratio of capital reserve to notional value underwritten [0155] If
reference entities are sub-classified in separate buckets according
to their respective ratings, then the ratio of capital reserve to
the notional value of that category in which reference entity under
consideration resides [0156] Ratio of capital reserve to notional
value of those entities rated a notch above default [0157]
Dollar-weighted aggregated average default rate associated with the
reference entities at the origin of issuance [0158] Ratio of
aggregated value of the CDS at issuance to the corresponding
aggregated notional value [0159] Ratio of dollar-weighted average
default rate of the reference entities to current capital reserve
[0160] Ratio of present aggregated market value of CDS to their
corresponding notional value [0161] Dollar-weighted average rating
of the of the reference entities underwritten for CDS [0162] The
dollar-weighted average rating difference between the reference
entity and the borrower at the origin of Issuance. [0163] The
aggregated value of the underlying assets at the origin of issuance
[0164] The dollar-weighted average maturity duration of the CDS
portfolio [0165] Current dollar-weighted average rating of each
sector [0166] Diversification index of reserve [0167]
Diversification index by industry sector in case one of the sectors
came under stress [0168] Liquidation index of the reserve [0169]
Rating of each of the primary insurance [0170] Primary insurance
diversification [0171] Dollar-weighted average rating of all the
primary insurance companies taken together [0172] Rating of each of
the secondary insurance [0173] Dollar-weighted average rating of
all the secondary insurance companies taken together [0174]
Secondary insurance diversification
Targets
[0174] [0175] Minimum guaranteed underwriter rating [0176] Minimum
guaranteed ratio of capital reserve to notional value underwritten
[0177] If reference entities are sub-classified in separate buckets
according to their respective ratings, then the minimum guaranteed
ratio of capital reserve to their notional value for the category
the reference entity under consideration falls [0178] Minimum
guaranteed ratio of capital reserve to notional value of those
entities rated a notch above default [0179] Worst case
dollar-weighted average default rates associated with the reference
entities [0180] Minimum requisite dollar-weighted average rating of
the debt at the origin of issuance [0181] Maximum permissible
dollar-weighted average rating difference between the reference
entities and their corresponding borrowers at the origin of debt
issuance [0182] Minimum guaranteed dollar-weighted average rating
of the aggregated pool of the reference entities underwritten for
CDS [0183] Minimum guaranteed dollar-weighted average rating of
each sector [0184] Minimum guaranteed diversification index [0185]
Minimum guaranteed diversification index by industry sector in case
one of the sectors came under stress [0186] Minimum guaranteed
liquidation index of the reserve [0187] Such as Money Market, able
to liquidate in 30 days etc. or more likely a blended value, using
the definition above, it could 8 [0188] Minimum primary insurance
diversification [0189] Maximum permissible percentage provided by
any single insurance company such as no one to provide more than
20% of the coverage [0190] Minimum secondary insurance
diversification [0191] Maximum permissible percentage provided by
any single insurance company such as no one to provide more than
25% of the coverage [0192] Minimum guaranteed rating of each of the
primary insurance [0193] Minimum dollar-weighted average rating of
all the primary insurance companies taken together [0194] Minimum
guaranteed rating of the secondary insurance [0195] Minimum
dollar-weighted average rating of all the secondary insurance
companies taken together
[0196] The following are examples of current parameters and targets
used by insurers (primary and secondary) to identify and quantify
risk and pricing associated with CDSs, the underlying debt, and the
other entities in the market.
Present Pool Status
[0197] Insurance company's own rating [0198] Ratio of capital
reserve versus the notional value insured [0199] Diversification
index of reserves [0200] Liquidity index of reserves [0201] Current
rating of the underwriter under consideration [0202]
Dollar-weighted average default rates of the underwriter [0203]
Dollar-weighted average rating of all the underwriters combined
together [0204] Diversification of underwriters [0205] Highest
concentration of notional value in any single insurance company
Targets/Constraints
[0205] [0206] Minimum guaranteed rating of the insurer [0207]
Minimum guaranteed Ratio of Capital Reserve versus the Notional
Value insured [0208] Minimum rating required of each underwriter
[0209] Minimum dollar-weighted average rating required of all the
underwriters combined together [0210] Diversification of
underwriters [0211] Dollar-weighted average default rates of the
underwriter [0212] Diversification of capital reserve [0213]
Liquidation index of capital reserve [0214] Maximum notional value
supported by a single secondary insurance company
Risk Monitoring and Optimized Risk Control
[0215] To assist in determining the appropriate pricing and risk
allocation, certain statistics are calculated, in accordance with
the invention, based on the underlying debt, assets in the pool,
and characteristics of the entities themselves. The following are
examples of current parameters and targets used by insurers
(primary and secondary) to identify and quantify risk and pricing
associated with CDSs, the underlying debt, and the other entities
in the market. Examples of these statistics include:
RequisiteCapitalReserve=(AverageDefaultRisk*NotionalValue)
Differential Risk Exposure, which can be defined in more than one
way, including:
DifferentialRiskExposure=k.sub.1(CurrentMarketValue-ValueAtOrigin)/Notio-
nalValue
DifferentialRiskExposure=k.sub.2(CurrentAverageDefaultRisk-DefaultRiskAt-
Origin)
DifferentialRiskExposure=k.sub.3(AssetValueAtOrigin-CurrentAssetValue)/N-
otionalValue
where k.sub.1, k.sub.2 and/or k.sub.3 may be constants, linear, or
non-linear functions to account for additional influences or to
attenuate/enhance any non-linear effects. From a risk monitoring
perspective, if this number is positive, it indicates an increased
risk trend, with the value identifying the magnitude of such risk.
If this number is negative, the risk exposure is decreasing.
Corresponding to each of the above definitions, "Capital
Sufficiency Index" can be defined as follows:
CapitalSufficiencyIndex=k(DifferentialRiskExposure)/(ExistingCapitalRese-
rve/NotionalValue)
[0216] "Average Default Risk" is a dealer's own view of the risks
associated with the reference entity whereas the "Current Market
Value" may be a better indicator of the market sentiments.
Similarly, "Current Asset Value" may be unique to the dealer;
however, the "Current Market Value" is a broader index of the
market sentiment. Any difference between these may indicate certain
limitations of the dealer's own methodologies or an early indicator
of future market conditions. In addition to using a dollar-weighted
average, the mean or mode may also be used. Similar calculations
may also be performed on sector by sector basis.
[0217] For securitized pools, the Differential Risk Exposure may
also be evaluated as:
DifferentialRiskExposure=k(CurrentAverageCreditScore-AverageCreditScoreA-
tOrigin)/NotionalValue
[0218] There could also be other parameters depending on the
context; these are shown for exemplary purposes only.
[0219] The objective of the dealer is to quantitatively define the
risk exposure unique to its own business model and dynamically
optimize its activities in the market to maintain a consistent (or
managed) risk exposure within acceptable parameters. Thus the
real-time optimized risk control can be accomplished using the
following techniques:
[0220] Assume that the capital reserve amount is fixed in absolute
dollar terms, based on the absolute amount and the corresponding
known notional value in advance, derive an acceptable average
default risk. CDSs are then issued such that the average default
risk is maintained until the capital reserve bucket has been
filled. Once the capital reserve bucket has been filled, the asset
values and/or market prices are monitored using the capital
sufficiency index to keep the capital reserve amount constant. This
may be accomplished by purchasing additional coverage insurance
from primary and/or secondary insurance companies, covering the
additional exposure by hedging on the other side of the trade,
and/or selling some of the existing CDS in the open market to keep
the risk within acceptable range.
[0221] If the average default risk goes down from that origin of
CDS, then the Dealer can increase the Notional Value ceiling and
underwrite more CDS or redeploy that surplus capital somewhere
else.
[0222] In instances in which the capital reserve is fixed as
percentage, the optimization process ensures that the requisite
capital reserve is maintained, and, as the notional value
increases, the capital reserve is added. Alternatively, additional
CDSs could be written with an average default risk such that the
ratio is maintained. If the ratio decreases then the additional CDS
can be sold with somewhat higher risk, and in return receive higher
premium.
[0223] Another element of the risk control is sector allocation. A
dealer can reduce its risk using diversification such that various
percentages of its asset pool are assigned to different sectors. As
an example, assume the following target asset diversification:
TABLE-US-00003 Technology 15% Industrial 8% Mortgage 12%
Pharmaceutical 16% Utilities 10% Energy 17% Finance 18% Consumer
Cyclical 4%
The Auction Process of FIG. 2
[0224] When a CDS request is received by the dealer, the SAEJ
engine, FIG. 2, determines the sector the request is associated
with. The determination of whether to accept or not to accept the
CDS into the asset pool is based on maintaining the relative ratio
among the various sectors so that any dramatic decline in one does
not severely distress the entire portfolio.
[0225] The baseline premium for the reference entity is computed
and modulated based on many factors including the dealer's default
risk, coverage and rating quality of the primary and secondary
insurances, magnitude of reserve capital diversification, sector
diversification, competitive market pricing at that instance in
time, dealer's status versus its own unique targets, the time left
to meet the targets, and so on.
[0226] One aspect of the invention provides an on-line market place
in which CDS buyers, sellers and support entities such as the
primary and secondary insurance companies participate in real-time
auctions as described in said co-pending application publications.
Comprehensive pricing models encompassing various risk parameters
associated with the market participants are built and an optimum
price is calculated. Initially, the risks associated with each of
the reference entities is modeled by the underwriter using its
pricing models.
[0227] As an example, a CDS buyer can initiate a real-time
on-demand, 24.times.7, auction with a well defined and quantified
request to an `Auctioneer`, as shown in FIG. 2. The auctioneer then
transmits the request to a network of participating CDS responders.
A pre-configured SAEJ at each responder evaluates the request in
the context of its own business objectives and current status,
computes a bid using the price optimization techniques defined
below, and responds in real-time with a bid.
[0228] The auctioneer then examines the bids so received, finds the
best bid, and sends it back to each of the responders to see if
anyone can beat the best value offered so far. Each responder SAEJ
evaluates the best bid received in the first round, re-computes its
bid, and determines if it can beat such price. If so, the SAEJ
re-submits its bid, each one being progressively lower than the
previous one, with the winner of the last round not needing to (but
may) re-submit. Those responders which cannot beat the best bid
offered in the previous round drop out of the auction. This
iterative and competitive process is repeated in real-time until
only one responder is left. That responder is then declared the
winner and a transaction is initiated between the initiator and the
winning responder. In case of a tie, there are multiple ways to
resolve it including a random number generator. The process may be
conducted asynchronously or in real-time.
[0229] In some cases, only those responders who meet the minimum
price requirements (pre-screen) as set by the initiator are allowed
to participate. In other cases, all responders participate
regardless of whether they meet the pre-screen as requested by the
initiator or not, because some may be close enough to the
pre-screen criteria and be acceptable to the initiator if its price
is much better.
[0230] In other instances, a holder of a CDS may decide to sell it.
In this case, the CDS owner is an `Initiator`, and the buyers may
be entities that own additional such instruments, or they may be
the original dealer providing the protection. Such prospective
buyers act as the responders in this case. A similar process is
adopted as described above and the true market price of the
underlying CDS instrument (derivative) is discovered and determined
(determination is consummation of transaction versus discovery
where the there is only a single responder left with the highest
bid).
[0231] Additional features of this process are described below. A
SAEJ at the Initiator (also referred as iSAEJ) acts as an active
participant in the process based on various market approaches. Each
such approach outlined below considers the variables described
above, and performs an optimum price discovery in conjunction with
appropriate risk control.
[0232] In this scenario, the reference parameters noted below are
specified by the initiator to the auctioneer for subsequent
communication to the underwriters (responders) at the auction
request: [0233] Name of the reference entity [0234] Required CDS
duration [0235] The dollar amount of purchase/Number of units
desired [0236] List of Sellers to exclude [0237] The list of
Reference parameters as outlined under earlier, or a desired subset
related to the Underwriter, Primary Insurance Company, and
Secondary Insurance Company, but not their values
[0238] Each responder SAEJ (rSAEJ) evaluates the request and places
a bid for the price as per the criterion established. The
evaluation and subsequent decision to participate in the auction
and the price the responder is willing to pay is a function of
one's own unique goals in the context of risk control and profit
optimization. To keep risk within the acceptable range as uniquely
defined by its dealer, each rSAEJ computes the bids based on its
current status and the distance to its targets based on the
following parameters: [0239] (Aggregated Premium
Collected/Aggregated Notional Value) [0240] (Requisite Capital
Reserve) [0241] (Dollar-weighted Average Rating) [0242]
(Dollar-weighted Average Default Risk) [0243] (Market Value of CDS
Underwritten So Far-Aggregated Premium Collected)/(Aggregated
Notional Value) [0244] (Dollar-weighted Average CDS Maturity
Duration) [0245] (Dollar-weighted average Primary Insurance
Companies Rating) [0246] (Coverage distribution among Primary
Insurance Companies) [0247] (Coverage distribution among Secondary
Insurance Companies) [0248] (Dollar-weighted average Secondary
Insurance Companies Rating) [0249] (Aggregated Notional Value as a
Target) [0250] If there are Securitized Pools underneath, then
optimize: [0251] (Dollar-weighted Average Credit
Score)/(Corresponding Aggregated Notional Value) [0252] (Aggregated
Asset Value/Corresponding Aggregated Notional Value) [0253]
(Aggregated Debt of the Borrowers/Corresponding Aggregated Income
of the Borrowers)
[0254] A dealer may further enhance this system by implementing
different sub-strategies within overall strategy. As an example, a
dealer may be willing to have higher short-term default risk but a
lower long-term default risk. Another example is sub-categorization
by the default risk such as Dollar-weighted average of those
Reference Entities who are a notch above the default` to the
`Capital Reserve`; or `Dollar-weighted average of those Reference
Entities who are a notch above the default` to the `Aggregated
Notional Value.`
[0255] The best bid received by the auctioneer is sent back to each
rSAEJ and the next round of bidding starts in which either a
participant beats the previous best bid or exits the auction. The
process is repeated until there is only one bid left. In case of a
tie, winner can be picked-up either using random number generator
or variations thereof. The final results are provided to the buyer
arranged in the order by best price. Each price is also coupled
with additional information for the reference parameters included
in the buyer's request. Another alternative for the underwriters is
to provide remaining parameters beyond what was requested, such as
those not used during or that have changed since the previous
round(s); and may include both current and target numbers.
[0256] Once the buyer has determined their optimal pricing for the
asset, the initiator can either choose to ignore all the parameter
details above and make a decision to sell the asset based solely on
the best price or select certain parameters and compute additional
metrics to determine how to proceed. For example, if more than one
parameter is selected then the `iSAEJ` computes the distance
between the desired values for various reference parameters and the
target values provided by each responder. In some instances, the
parameters may be weighted to give preference to certain parameters
over others. The lower the difference between the desired value and
the value provided by the responder the closer it is to the desired
goal. In an ideal case the distance is zero for each variable.
However, this distance has to be seen in the context of the quoted
price from responders. Consider, for example, a price quoted by an
underwriter of 2%/year, assuming the initiator is concerned with
only one variable and the distance from ideal is normalized
distance of `1`. A second underwriter quotes a price of 1%,
however, its distance from the ideal is `1.1`. In this case from
the Initiator's perspective, the second alternative is superior
than the first one.
[0257] Thus, a "Coverage Quality Efficiency" (CQE) can be computed
by first calculating the normalized actual distance from the target
values using the following formula:
d = i w i ( r i - R i T R i M ) 2 ( 4 ) ##EQU00003##
where the index i denotes reference parameter i, r.sub.i is the
bidder's reference parameter value, and w.sub.i is the weight the
buyer assigns to reference parameter i; R.sub.i.sup.T is the
buyer's reference parameter target, and R.sub.i.sup.M is the
maximum possible distance to target. Here the maximum distance
possible from target indicates the lowest possible match; for
example, if the lowest rating is `CCC` and the highest is AAA, then
the maximum distance possible could be `6` (B, BB, BBB, A, AA). The
CQE is then calculated as:
q = 1 - p 1 - d . ( 5 ) ##EQU00004##
[0258] Using the example above, the CQE of the first underwriter is
1-0.02/(1-1/6)=0.976. For second underwriter, it is
1-0.01/(1-1.1/6)=0.988. Using this approach, the second underwriter
is accepted as the winner and it is notified via the auctioneer. If
there is more than one variable under consideration, the `CQE` may
be computed by squaring the unique absolute distance for each
variable and multiplying it by the square of its weight. Each
computed CQE is then summed and the square root is taken, thus
determining the normalized distance.
[0259] The actual distance from to overall target is then computed
using the constraints, which are assumed to be less than or equal
to the desired value. In situations in which the reference
parameters are better than expected, a distance (penalty) is still
calculated. In these situations, the distance formula is modified
to:
d = i w i ( R i T - r i R i M ) ( 6 ) ##EQU00005##
such that the distance becomes negative when the reference
parameter value is better (higher) than the target, and the
smallest distance is -1 and the biggest distance is still 1.
[0260] Various options are then available.
TABLE-US-00004 TABLE I (Options 1-5) Option 1 This tells every
company to set up targets for its capital reserve -- how much risk
is it willing to take -- what is the average rating of the debt
already written-how much risk is a company to take -- and
everything is evaluated and decided in this context. Before the
present invention, risk control, as distinguished from risk
monitoring, was totally absent i.e. zero. There was not even
suitable risk monitoring let alone control. The present invention
introduces active control using appropriate mathematics with the
automatic engine system of the type disclosed said prior
applications and publications. It is a control system with feedback
on a dynamic face in forming where every transaction is so that the
company keeps in the middle, so to speak, given the capital reserve
system and the method for optimizing quantitive business objectives
and targets of product and seller services together with the
synchronizing of product promotions and advertising. These may
include one or more search engines, as described in said
publications based on Internet research. Option 2 The second
processing option is similar to Option 1, above, and introduces the
following modifications: the iSAEJ` provides its evaluation
criterion (including the unique weight assigned to each parameter)
to the auctioneer which shares it with each Underwriter's `rSAEJ`;
Each rSAEJ pre-screens the request based on its own business
objectives and distance to the target; assuming the asset passes
pre-screening, each rSAEJ` computes the CQE and places the bids;
the underwriter now knows how much he needs to drop his price to
win the bid as the lowest price bid in this instance may not
necessarily win if it has relatively higher distance from what is
desired. In essence, the bid made by the underwriter is more
informed as the price discovery process is iterative; In an
alternative scenario, both price and CQE are sent as part of the
bid and the auctioneer picks two winners, one for price and another
for CQE, and the iSAEJ evaluates each bid. Option 3 A third option
is similar to Options 1 and 2 in that the iSAEJ provides the
requisite information about the reference entity and the desired
attributes of the requisite parameters, however there are certain
variations. For example, a buyer may not provide the name of the
reference entity and instead specifies a sector, and the responder
selects the reference entity within the sector. In such cases, each
participating underwriter maintains multiple parameter "buckets"
into which each asset will be placed. As an example, an underwriter
may maintain ratings buckets and sub-buckets, each containing the
relative value of the variances for that specific parameter. More
specifically, an underwriter may have multiple insurance companies
(primary insurance) in one bucket, each rated differently. Each
sub-bucket then contains the specific ratings and corresponding
characteristics of the parameters related to that insurance
company. [[Another example of how to assign assets to buckets is to
use the relative portfolio size of each reference entity ratings is
kept along with corresponding minimum pool reserve. An underwriter
may have $10B worth of notional value for `AAA` rated reference
entities, and guarantees a minimum reserve capital of $500M against
it, resulting in a 5% reserve capital. On the other hand, for `CCC`
rated reference entities, the underwriter provides coverage for
$100B worth of notional value but only 6% minimum guaranteed
reserve ratio, although the proportionate risk is much higher. A
minimum guaranteed reserve implies certain additional collateral to
the initiator if the reserve ratio falls below that threshold. As
reference entity ratings evolve, the CDSs are reclassified, and the
buckets adjusted. Once the parameters are received by the
underwriter's `rSAEJ` via the auctioneer, each `rSAEJ` creates a
customized response to the extent permissible by its own
constraints. Consider an example in which a buyer specifies the
requirements as outlined in Option 1 or 2, but in addition it
provides the following parameters: Name of the Reference Entity and
current rating: IBM, AA Notional Value $10M Minimum Dealer Rating
AA Duration 5 years Minimum Capital Reserve Ratio for the
corresponding rating bucket 4% Minimum Primary Insurance Rating AA
Minimum Primary Insurance Capital Reserve Ratio 3% Minimum
Secondary Insurance rating A Minimum Secondary Insurance Capital
Reserve ratio 1% Current dollar-weighted average default rating of
the reference 2 or below entity sub-category Diversification Index
of Reserves for AAA rated Reference 8 or better entities bucket
Liquidity index of Reserves for AAA rated Reference entities 5 or
bucket better Aggregated CDS value outstanding--- than Capital
Reserve Given these parameters, and assuming the dealer rating
meets or exceeds the buyer's requirements, each rSAEJ examines its
asset buckets as follows: Duration: usually static, except that 5
year may have different weight assigned to it than 2 years when
computing the risk profile and the corresponding impact on price
Select `AA` rated bucket of reference entities Select the desired
rated primary insurance company, although there may be more than
one such company, and in such cases may optimize the relative ratio
of business assigned to each Select the desired rated secondary
insurance company, and again there may be more than one such
company and the ratio of the total business allocated to each may
be optimized Compute an optimized floor price using the reference
entity base price, subject to its distance from the targets. No bid
is made below this amount, thus extracting the maximum possible
price from the buyer. In addition to the optimization performed by
the responder, each underwriter may perform its own optimization
based on its targets. For example, an underwriter may want to
maintain a portfolio with blended weighted average rating of `A`, a
weighted average duration of 4 years, a pool capital reserve of 6%,
a primary insurance rating of `AA`, a secondary insurance rating of
`A`, and a not-to-exceed default rating number of 5. Furthermore,
there may be sub-targets for each category. For example, the AAA
rated bucket may maintain higher capital reserve and higher primary
and secondary insurance coverage. These criteria are used to
analyze the impact on targets if the underwriter wins the auction,
filter incoming requests, and to dynamically assess premiums while
simultaneously minimizing or staying within the risk parameters.
Thus an underwriter may define its risk objectives and configure
its rSAEJ to maintain $300B in total CDSs written at the end of Q1
having a ratio of annualized premium collected to notional value of
1.5%. Individual bucket targets may also be specified, such as
$100B in the "AA" bucket, and relative ratios (e.g., $100B/total
portfolio) to the other ratings. Once the `rSAEJ` has determined
that the asset has passed the pre- screening criteria and has
computed the optimal price, it iteratively bids such that it does
not drop below the optimized price floor. Other approaches that may
be used include bidding a price as close as it can to the optimal
price along with a corresponding CQE. In some cases, the CQE may be
the only determining factor as to which entity wins the auction.]]
Option 4 A fourth option builds on the techniques described above
by allowing both the `iSAEJ` and `rSAEJ` to be dynamically
optimizing the prices during the auction. For example, an `iSAEJ`
may fix some variables (e.g., an `AA" rating and a 5 year
duration), but may allow for variability of the primary insurance
company rating between a rating from `A` to `AAA` and for the
secondary insurance company from `AA` to `AAA`.. Given these
parameters, the initiator may request underwriters to optimize
their price around these variables. Such flexibility accounts for
the weights assigned to the parameters, as they may be small
relative to other parameters. Option 5 This is similar to Option 4,
except that the underwriter requests the auctioneer to conduct an
auction among the insurance companies (primary and/or secondary)
instead of selecting them based on the risk buckets. Thus, three
sets of concurrent auctions occur, in which the result of an
auction among secondary insurance companies and the results from an
auction among primary insurance companies are used by the
underwriter to compute its bid price. Unlike Option 3 above in
which the premiums charged by the primary and secondary insurance
companies are known, this option allows the primary and secondary
insurance company can bid against each other to offer the best
prices to the underwriter. As a result, various embodiments of the
invention facilitate the quantitative and transparent measurement
of the risk contributions from various entities under many
scenarios. The two primary sources of risk, the reference entity
the underwriter is considered during the pricing process and in a
real-time auction using these techniques and systems.
Optimization Mathematical Formulation for the Underwriter
[0261] In addition to determining the proper pricing for buyers of
CDSs, the underwriters also benefit from using the models,
techniques and systems described herein. From the perspective of
the underwriter, a buyer specifies its requirements, and the
bidders in the auction calculate the optimal prices and
combinations of reference entities, primary insurance companies,
and secondary insurance companies while maximizing its own profit
and adhering to its own defined constraints.
[0262] The buyer's requirements (CDS duration, minimum reference
entity rating, minimum underwriter capital reserve ratio, etc.) may
be used as filtering criteria that determine what assets are
offered from the underwriter's portfolio. If the buyer is flexible
about certain reference parameters, the related filtering criteria
can be relaxed or even removed.
[0263] The expected utility gain as described in said such
co-pending applications, for participating in an auction, is a
function of many variables, and can be expressed as:
E(.DELTA.u)=u(p,y,z,p.sub.y,p.sub.z,.rho.,v,d,F,T,t) (7)
where: [0264] p: bid price for the chosen reference entity, a
decision variable. [0265] y: vector spanning over eligible primary
insurance companies, decision variable. Element value 1 means the
corresponding primary insurance company is chosen, 0 means it is
not chosen. [0266] z: Vector spanning over eligible secondary
insurance companies, decision variable. Element value 1 means the
corresponding secondary insurance company is chosen, 0 means it is
not chosen. [0267] p.sub.y: Insurance premium vector for all
eligible primary insurance companies, assumed to be known given the
reference entity is chosen. [0268] p.sub.z: Insurance premium
vector for all eligible secondary insurance companies, assumed to
be known given the reference entity is chosen. [0269] .rho.: Bid
response function, which models the market condition and buyer's
response behavior and is a function of the price, and/or CQE.
[0270] v: CDS notional value. [0271] F: Reference entity risk
profile vector. [0272] T: Target vector. Targets are underwriter's
objectives for various reference parameters, risk monitoring
parameters, underwriter's profit etc. [0273] t: Accumulated target
value vector before participating the particular auction.
[0274] If the buyer doesn't specify the reference entity, the
underwriter may select the reference entity to bid on. A new
decision variable x can be introduced to indicate which reference
entity should be used to maximize the underwriter's utility. While
there may be many utility functions which can vary from underwriter
to underwriter, the objective is to maximize the underwriter's
profit, minimize the overall distance to desired targets, and risk
control within specified parameters. In one embodiment, the
optimization function may be formulated as:
max p , y , z ( E ( .DELTA. u ) ) ( 8 ) ##EQU00006##
[0275] Some of the constraints used to bound the optimization
formula may be categorized as risk-based controls, underwriter
constraints, reference entity constraints, and insurer constraints
(both primary and secondary). Examples of risk-based constraints
include the existence of the requisite capital reserve to cover the
assets, average default risk, capital sufficiency index, and
differential risk exposure. Underwriter related constraints include
minimum liquidity index of reserve, minimum diversification index
of reserve, and minimum floor price constraints. Reference entity
and insurance constraints include both global and sector
constraints, as described above.
[0276] The following provides an example of the optimization
approach described above. The underwriter's utility function may be
expressed as an expected profit or gain from the CDS auction
as:
E(.DELTA.u)=.rho.(p-yp.sub.y-zp.sub.z-C) (9)
where `C` is the corresponding "Incremental Requisite Capital
Reserve` contribution for each CDS. Each underwriter has its own
way to estimate C. The optimization problem may be reduced to:
max p , y , z ( E ( .DELTA. u ) ) . ( 10 ) ##EQU00007##
with examples of constraints listed below in TABLE II.
TABLE-US-00005 TABLE II Differential risk exposure constraint (for
risk control) (11) K L .ltoreq. V t K t + vk V t + v - K 0 .ltoreq.
K H ##EQU00008## K.sub.L: Lower bound of differential risk exposure
target. K.sub.H: Higher bound of differential risk exposure target.
V.sub.t: Cumulated notional value up to time t. K.sub.t: Average
dollar-weighted default risk to time t. v: Newly requested CDS
notional value. k: Newly requested CDS default risk. K.sub.0:
Default risk at origin. Dollar amount weighted CDS duration (12) D
L .ltoreq. V t D t + vd V t + v .ltoreq. D H ##EQU00009## D.sub.L:
Lower bound of weighted global duration target. D.sub.H: Higher
bound of weighted global duration target. V.sub.t: Cumulated
notional value up to time t. D.sub.t: Weighted duration up to time
t. v: Newly requested CDS notional value. d: Newly requested CDS
duration. Dollar amount weighted reference entity diversification
index (13) S L x .ltoreq. V t S t x + vs x V t + v .ltoreq. S H x
##EQU00010## S.sub.L.sup.x: Lower bound of weighted reference
entity diversification index target. S.sub.H.sup.x: Higher bound of
weighted reference entity diversification index target. V.sub.t:
Cumulated notional value up to time t. S.sub.t.sup.x: Weighted
diversification index up to time t. v: Newly requested CDS notional
value s.sup.x: Newly chosen reference entity diversification index.
Dollar amount weighted primary insurance company rating (14) R L x
.ltoreq. V t R t x + vr x y V t + v .ltoreq. R H x ##EQU00011##
R.sub.L.sup.x: Lower bound of weighted primary insurance company
rating target. R.sub.H.sup.x: Higher bound of weighted primary
insurance company rating target. V.sub.t: Cumulated notional value
up to time t. R.sub.t.sup.x: Weighted rating for primary insurance
companies up to time t. v: Newly requested CDS notional value
r.sup.x: Eligible primary insurance company rating vector. y:
Primary insurance company selection vector.
Optimized Parameter Requirements Determination for the Buyer
[0277] Using these techniques, the buyer may set targets for
various parameters characterizing its CDS portfolio. For example,
it may specify average reference entity rating, average primary
insurance company rating, and average secondary insurance company
rating.
[0278] When a decision is made to buy additional CDS, its requisite
parameters characteristics are determined by minimizing the overall
distance to the targets according to the following formula:
d = i w i ( V t R i t + vr i V t + v - R i T ) 2 ( 15 )
##EQU00012## [0279] V.sup.t: Cumulated notional value up to time t.
[0280] R.sub.i.sup.t: Average value of reference parameter i at
time t. [0281] R.sub.i.sup.T: Target value of reference parameter
i. [0282] v: Notional value of next CDS. [0283] r.sub.i: Reference
parameter i of next CDS.
Implementations
[0284] Various embodiments of the invention may be provided as an
article of manufacture having a computer-readable medium with
computer-readable instructions embodied thereon for performing the
methods described in the preceding paragraphs. In particular, the
functionality of a method of the present invention may be embedded
on a computer-readable medium, such as, but not limited to, a
floppy disk, a hard disk, an optical disk, a magnetic tape, a PROM,
an EPROM, CD-ROM, or DVD-ROM or downloaded from a server. The
functionality of the techniques may be embedded on the
computer-readable medium in any number of computer-readable
instructions, or languages such as, for example, FORTRAN, PASCAL,
C, C++, Java, C#, Tcl, BASIC and assembly language. Further, the
computer-readable instructions may, for example, be written in a
script, macro, or functionally embedded in commercially available
software (such as, e.g., EXCEL or VISUAL BASIC).
Key Advantages
[0285] Using the methods and systems described herein, the various
embodiments of the invention provide CDS market participants with
an innovative, transparent, and automated technique for creating a
securitized pool of assets based on various constraints and
optimization parameters. As part of this process, reference entity
pricing models are substantially enhanced and dealer default risks
are identified, quantified, and built into the pricing model. A
reverse-auction process, as described in said co-pending
applications and publications is used to discover the true CDS
price in the market among the various participants. A
fully-automated risk control mechanism maintains dealer risk within
a specified range, and full visibility is provided subsequent to
the transaction to enable any changes at the borrower level to be
accounted for and reflected in the current CDS price.
[0286] More specifically, the above-described process provides the
following benefits and advantages over conventional CDS pricing and
trading methodologies. [0287] Buyers can automatically and
quantitatively analyze their unique requirements in the context of
what is currently owned, their objectives and the distance to their
objectives from what is already owned. [0288] The buyer receives
not only the price but also the parameter values deemed to be
important by the buyer. [0289] The buyer can quantitatively
evaluate each CDS price offered in the context of other relevant
parameters such as corresponding dealer's reserve capital, sector
concentration, primary insurance coverage, secondary insurance
coverage etc. [0290] The buyer may assign a unique weight to each
parameter. [0291] The buyer can compute the variations between the
unique parameters values offered versus what is desired for each of
the participating dealers and reduce such variations to a single
parameter (the CQE) to facilitate comparison among participating
dealers. [0292] The buyer can quantitatively analyze each CQE in
conjunction with the corresponding price offered by each of the
participating dealers to make a fair comparison. [0293] The buyer
can quantitatively assess the magnitude of risk coverage provided
by the primary and secondary insurance companies in the event of
dealer default [0294] The buyer can automatically optimize its own
CDS pool at the best price possible at the level of risk deemed
acceptable to meet its unique requirements without requiring any
manual intervention once configured. [0295] The automated nature of
the process eliminates the manual effort, substantially improves
the efficiency of the process and is much more cost effective than
is currently feasible. [0296] Rating inflation is eliminated
providing the buyer with a known risk exposure. [0297] The impact
of digital discontinuity on the buyer is eliminated or largely
minimized. [0298] For the debt which contains securitized pools
underneath, the buyer immensely benefits from the significantly
enhanced construction process of such pools [0299] For the debt
which contains securitized pools underneath, the buyer immensely
benefits from the significantly enhanced construction process
relying on dollar-weighted analog credit score [0300] For the debt
comprised of securitized pools, the buyer benefits from the
dollar-weighted income level of the borrowers within the pools,
debt-to-income ratio of the borrowers, and the use of the true
asset value at the origin of the asset. [0301] The buyer no longer
is solely reliant on intermediary ratings of the debt quality.
[0302] The buyer has visibility into the quality of debt as a
function of time [0303] The requisite parameters of the pool and
the respective targets and constraints are specified in advance and
the pools are created in a manner to meet those requirements
thereby creating an optimized pool instead of being built on ad-hoc
basis post the acquisition of assets prior to building the pool.
[0304] A pre-screen filters out assets that not meet minimum
requirements prior to optimization of the pool around the targets
in conjunction with respective constraints. [0305] Analog
quantification is used (instead of digital quantification) for
credit scores, debt to income ratio and to value the assets, which
eliminates the impact of digital discontinuity and avoids rating
creep-up. [0306] A common rating system is used instead of multiple
inconsistent rating systems with varying degree of resolutions.
[0307] Computation of a dollar-weighted default risk using a
non-linear default curve instead of conventional digitized rating
systems. [0308] Pools are constructed using a live auction in
real-time, resulting in a true price discovery of the asset. [0309]
The process enables securitization of disparate assets while
simultaneously evaluating the quality of each debt and true price
discovery at a macro level. [0310] Debt default rate is updated as
a function of time thus providing full visibility at the lowest
borrower level. [0311] The dealer can demand a dollar-weighted
analog credit score to eliminate the uncertainty caused by digital
discontinuities of conventional rating schemes. [0312] The dealer
has access to and can analyze risk parameters beyond the
intermediaries and examine the risk profile of the pool at its
origin. [0313] The dealer can monitor the debt quality of the pool
as a function of time after the CDS has been underwritten. [0314]
The system fully automates reference entity pricing by accounting
for the default risk of the underwriter and the potential loss of
coverage by its primary and secondary insurance companies, thus
providing a more meaningful price. [0315] Identification and
quantification of risk control parameters and the ability to
maintain the parameters within acceptable ranges as defined by the
dealer according to its own business objectives. [0316] Risk
control parameters such as industry sector distribution provide an
additional level of risk control beyond the primary parameters such
as capital reserve or dollar-weighted average default rate etc.
[0317] Risks due to failure of the primary and/or secondary insurer
are accounted for and included in risk monitoring and risk control.
[0318] Dynamic risk control proactively optimizes pricing on
transactions to keep the parameters such as capital reserve,
dollar-weighted average default rates and others within the
targeted range both at specific instants and over time. [0319] The
risk monitoring and control capabilities enable dealers to maintain
an acceptable risk exposure that is consistent with its capital
resources and business strategies. [0320] Risk control has a
significant impact on the magnitude and direction of modulation of
the Reference Entity Price by the price optimization process.
[0321] The usage of continuous analog data instead of digitized
data allows for linearization of data and eliminates the digital
discontinuities due to averaging of the ratings of the reference
entities. [0322] Risks of rating inflation due to manipulation of
ratings is eliminated or minimized. [0323] To minimize the
magnitude of inefficiency in the conventional Over The Counter
(OTC) manually negotiated process, a real-time, on-demand,
24.times.7, iterative, reverse auction is held among the dealers
interested in writing the CDS for the benefit of the buyer. Each
dealer's SAEJ computes its own optimum bid and at the end of each
iteration, the auctioneer sends the best bid of each round to each
of the participants, which may then re-bid until a winner is
determined. In case of tie, a random number or other mechanisms can
be used to break the tie. [0324] In an alternative embodiment, the
SAEJs can instead bid using a `CQE`(Coverage Quality Efficiency), a
parameter computed by minimizing the aggregate distance to the
buyer's desired parameter values, which in turn may be used to
modify the bid price within constraints while maximizing its net
gain. [0325] In Option 1, the buyer communicates its desired
parameters, some of which may be used to pre-screen the dealer
based on, for example, their capital reserve to notional value
ratio or their own ratings. The dealers compete based on an
optimally computed price as determined by their SAEJ, and results
are presented to the buyer. The buyer's SAEJ may select the best
price provider as winner or, alternatively, compute a CQE for each
result and select the dealer with the best CQE. [0326] In Option 2,
the process includes weighting some or all of the parameters, and,
like Option 1, each dealer's SAEJ optimally computes a CQE and
manages the real-time iterative bidding process. [0327] Option 3
further includes consideration of the many insurance options
available to the dealers and capital reserves for each reference
entity. This option provides dealers with pricing flexibility while
still trying to meet the buyer's requirements. In some embodiments,
only those dealers meeting the pre-screen criteria are allowed to
bid. In an alternative embodiment, in addition to the pre-screening
criteria, the CQE may be used as a determining factor. [0328] In
Option 4, buyers provide ranges for some or all of the parameters,
as opposed to a single target value. [0329] In Option 5, the dealer
initiates a real-time, instantaneous reverse auction among its
primary insurance companies to secure the best price prior to
incorporating it in its own optimized bid computations for each
potential transaction. Similar auctions may also be initiated for
secondary insurance.
CONCLUSION
[0330] A transparent on-demand, 24.times.7 real-time iterative
auction among many underwriters is thus conducted for this complex
multi-variable problem of the invention to discover the true price
of the CDS. The underwriter benefits from the reduced exposure to
the primary and secondary insurance companies and also provides
less risk to those insuring it, thus receiving a better price,
benefiting all the parties involved. The primary and secondary
insurance companies can optimize their risk/reward ratio while
implementing the requisite diversification across many vectors
including the number of underwriters and the number of insurance
companies and their respective ratings.
[0331] The technology, however, can also be applied in other
applications where the source of the debt and the offer of the debt
are decoupled, such as mortgage holder and those holding the
mortgage-backed bonds.
[0332] This technology can also be applied to a broad range of
applications such as auction rate securities or other similar
instruments. It is well suited to securitized portfolios
constructed with credit cards, auto loans, student loans or a
combination thereof.
[0333] Further modifications will also occur to those skilled in
this art and are considered to fall within the spirit and scope of
the invention as defined in the appended claims.
* * * * *