U.S. patent application number 11/817121 was filed with the patent office on 2009-10-15 for automated risk monitoring method and system.
This patent application is currently assigned to COFFEE NATION LIMITED. Invention is credited to Peter Middelkamp, Lutz Wilhelmy.
Application Number | 20090259596 11/817121 |
Document ID | / |
Family ID | 34960579 |
Filed Date | 2009-10-15 |
United States Patent
Application |
20090259596 |
Kind Code |
A1 |
Middelkamp; Peter ; et
al. |
October 15, 2009 |
Automated Risk Monitoring Method and System
Abstract
The invention relates to an automated risk monitoring method and
a corresponding risk monitoring system for automated risk
monitoring, in the case of which control data for different
companies are transferred to a monitoring unit and evaluated, a
company specific asset distribution and a corresponding threshold
value being determined, said threshold value corresponding to the
expected value of the asset parameter for the occurrence of the
insolvency of a company, recovery rate factors being determined by
means of a standardization module of the monitoring unit, and
wherein, using a MonteCarlo module of the monitoring unit (20),
MonteCarlo asset parameters are generated for each company by means
of which the companies with the lowest expected recovery rate
factors are determined and dynamic adjustment of the portfolio
accordingly made by means of the monitoring unit.
Inventors: |
Middelkamp; Peter;
(Einsiedeln, CH) ; Wilhelmy; Lutz; (Pfaffikon,
CH) |
Correspondence
Address: |
OBLON, SPIVAK, MCCLELLAND MAIER & NEUSTADT, L.L.P.
1940 DUKE STREET
ALEXANDRIA
VA
22314
US
|
Assignee: |
COFFEE NATION LIMITED
BUCKINGHAMSHIRE
GB
SWISS REINSURANCE COMPANY
ZURICH
CH
|
Family ID: |
34960579 |
Appl. No.: |
11/817121 |
Filed: |
February 24, 2005 |
PCT Filed: |
February 24, 2005 |
PCT NO: |
PCT/EP05/50787 |
371 Date: |
September 5, 2008 |
Current U.S.
Class: |
705/36R ;
707/999.005; 707/999.01; 707/999.104; 707/E17.044; 707/E17.109 |
Current CPC
Class: |
G06Q 40/06 20130101;
G06Q 40/08 20130101; G06Q 40/025 20130101 |
Class at
Publication: |
705/36.R ;
707/104.1; 707/10; 707/5; 707/E17.109; 707/E17.044 |
International
Class: |
G06Q 40/00 20060101
G06Q040/00; G06F 17/30 20060101 G06F017/30 |
Claims
1. A method for networked monitoring and dynamic portfolio
adjustment among a plurality of entities, in which individual
entity's, in the plurality of entities, one or more asset
parameters are extracted on an entity-specific basis from control
data and the one or more asset parameters of the plurality of
entities are evaluated stochastically such that at least one
entity-specific asset distribution is determined and stored,
comprising: storing a determined threshold value based on at least
one entity-specific asset distribution, the stored threshold value
being stored with an association, if the threshold value is
equivalent to an expected value of one or more asset parameters for
the occurrence of insolvency of the individual entity;
standardizing determined entity-specific recovery rate factors in a
manner which is associated with one or more of the entity-specific
asset distribution or the stored threshold value; and generating
MonteCarlo asset parameters for individual entities, in the
plurality of entities, to determine the expected recovery rate
factors; displaying the individual entities with the expected
recovery rate factors.
2. The method of claim 1 further comprising storing, in a limited
access environment, one or more lowest expected recovery rate
factors, included in the determined entity-specific recovery rate
factors, such that an authorized user can adjust a subject
portfolio.
3. The method of claim 1 wherein one or more lowest expected
recovery rate factors, included in the determined entity-specific
recovery rate factors, are wirelessly broadcast.
4. The method of claim 3 wherein security for the encryption is
centrally managed.
5. The method of claim 3 further comprising extracting relevant
data from the broadcast fo a specific portfolio.
6. The method of claim 3 further comprising filtering the broadcast
for at least one of the determined entity-specific recovery rate or
an expected value of one or more asset parameters for the
occurrence of insolvency of the individual entity for a entity
represented in a specific portfolio.
7. A method for monitoring credit risk of a financial portfolio
comprising: extracting one or more asset parameters, on a
entity-specific basis, from control data for individual entities
represented in the portfolio; stochastically evaluating the one or
more asset parameters to determine at least one entity-specific
asset distribution; determining a threshold value based on the
least one entity-specific asset distribution; storing an
association with the threshold value, if the threshold value is
equivalent to an expected value of one or mores asset parameters
associated with an entity's insolvency; generating MonteCarlo asset
parameters for individual entities, represented in the portfolio,
to determine expected recovery rate factors; standardizing recovery
rate factors for the individual entities, represented in the
portfolio, for the at least one entity-specific asset distribution;
and displaying the expected recovery rate factors for the
individual entities, represented in the portfolio.
8. The method of claim 7 further comprising storing, in a limited
access environment, a lowest expected recovery rate factor in
association with the portfolio.
9. The method of claim 8 further comprising broadcasting the lowest
expected recovery rate factor.
10. The method of claim 9 wherein access to the broadcast is
centrally managed.
11. The method of claim 7 wherein the method is at least partially
implemented in a wireless environment.
12. The method of claim 7 further comprising automatically
purchasing a security representing an interest in an individual
entity, represented in the portfolio, to adjust the portfolio's
risk exposure.
13. Computer readable media comprising computer executable
instructions that, when implemented by a computing system, direct
the computing system to: extract one or more asset parameters, on a
entity-specific basis, from control data for individual entities
represented in the portfolio; stochastically evaluate the one or
more asset parameters to determine at least one entity-specific
asset distribution; determine a threshold value based on the least
one entity-specific asset distribution for storage; store an
association with the threshold value, if the threshold value is
equivalent to an expected value of one or mores asset parameters
associated with an entity's insolvency; generate MonteCarlo asset
parameters for individual entities, represented in the portfolio,
to determine expected recovery rate factors; standardize recovery
rate factors for the individual entities, represented in the
portfolio, for the at least one entity-specific asset distribution;
and display the expected recovery rate factors for the individual
entities.
14. The computer readable media of claim 13 further comprising
centrally manage security for display of the expected recovery rate
factors.
15. The computer readable media of claim 13 store a lowest expected
recovery rate factor in association with the portfolio.
16. The computer readable media of claim 13 further comprising
automatically order a security for an individual entity to
rebalance the portfolio's risk factor.
17. A system for automatically monitoring credit risk of a
portfolio comprising: a central monitoring unit, for monitoring
control data for individual entities represented in the portfolio,
the monitoring unit being configured to stochastically evaluate one
or more asset parameters extracted from the control data to obtain
a standardized recovery rate factors, based on the expected
recovery rate factors for the individual entities, which are
associated with is MonteCarlo factors for the individual entities
represented in the portfolio.
18. The system of claim 17 wherein the central monitoring unit
includes: an extraction module configured to extract the one or
more asset parameters for the individual entities from the control
data; a statistical module configured to stochastically evaluate
the one or more asset parameters to determine the at least one
entity-specific asset distribution; a storage module configured to
store the threshold value determined from the at least one
entity-specific asset distribution; a standardization module
configured to standardize the recovery rate factors based on the at
least one entity-specific asset distribution and the threshold
value; and a MonteCarlo module configured to generate the
MonteCarlo asset parameters for individual entities represented in
the portfolio.
19. The system of claim 17 wherein the storage module stores an
association with the threshold value, if the threshold value is
equivalent to an expected value of one or mores asset parameters
associated with an entity's insolvency.
20. The system of claim 17 further comprising a wireless
transmitter configured to broadcast at least one of the expected
recovery rate factors for an individual entity, represented in the
portfolio.
Description
[0001] The present application claims priory under 35 U.S.C. .sctn.
365, to Patent Cooperation Treaty Application Number.
PCT/EP2005/050787, entitled: Automated Risk Monitoring Method and
System, filed on Feb. 24, 2005, the foregoing application is
incorporated by reference in its entirety and is commonly
assigned.
TECHNICAL DOMAIN
[0002] The invention relates to an automated monitoring method and
a risk monitoring system for automated credit monitoring of a
portfolio, wherein corporate data for different companies are
transferred to a monitoring unit for evaluation. The invention
relates, in particular, to a risk monitoring system in is which the
anticipated recovery rate factors for each company are determined
and these companies are then displayed by means of the monitoring
unit via an output and/or dynamically adjusted accordingly in the
portfolio, the financial data being transmitted via a network to at
least one financial institution.
STATE OF THE ART
[0003] After a credit or financial contract has been signed between
two or more parties, situations may occur in which at least one of
the parties is unable to discharge his contractual obligations. In
that situation, a credit failure or default is said to have
occurred. A default occurs, for example, if:
[0004] an interest or redemption payment is either not made at all
or only belatedly;
[0005] an application to open insolvency proceedings is filed
or
[0006] debt titles are exchanged for a packet of securities with a
lower value.
[0007] Banks and other financial institutions are particularly
exposed to this credit risk, but so too are almost all other
personal or institutional investors. To enable the impact of the
credit risk to be assessed, most financial institutions and other
investors therefore make systematic evaluations of credit customers
and verify the credit risk carried by the loan for which an
application has been made. For this purpose, they use various
rating systems and credit risk modules which normally provide
information on the quality or creditworthiness of a debtor to them
at regular intervals. Such information then serves as the basis for
comprehensive, statistically assured systems for risk assessment
and risk control. Especially in the area of credit portfolio
management, these systems play a very important role as the goal of
optimum portfolio development can be achieved much better and much
more easily in this way.
[0008] In previous practice, various modules were developed and are
used as the basis of systems for credit portfolio management, their
aim being to determine a probability distribution of the possible
default losses. To enable the impact of the credit risk on the
financial institution or investor to be estimated, these modules
consider in particular the probability and timing of defaults on
individual loans or credits.
[0009] The credit loss anticipated by banks for a particular credit
is made up of the probability of default (PD) on the loan
multiplied by the resulting loss or loss given default (LGD):
E(Credit Loss) PD*LGD
[0010] In addition to the probability of default (PD) and the loss
given default (LGD), the recovery rates are other main variables
which influence the degree of credit risk. In the area of risk
monitoring and risk quantification, and in particular in that of
credit risk monitoring, recovery rates show the percentage share of
a loan sum which will be repaid to the lender in the event of a
default on the loan. The loss given default is generally stated as
a percentage, equivalent to the percentage share of the nominal sum
which will be lost after default on a credit. The recovery rate on
the other hand is defined as the portion of the nominal sum which
will still be repaid after the credit default.
[0011] The relationship between the recovery rate and the loss
given default is therefore as follows:
Recovery Rate=1-LGD
[0012] At present in the existing credit risk modules, only the
probability of default is generally modelled more accurately, while
the loss given default is often indicated far less precisely. The
recovery rate is either assumed to be constant (for instance 50% of
the nominal amount) or else approximated by the historic average
for different seniority classes.
[0013] However, this simplification leads to incorrect assessments
of the credit risk and therefore also to false or imprecise
valuations of credits and loans as the recovery rate is highly
volatile and varies substantially as a function of time. What is
more, the existing models assume that the default and recovery rate
are independent of each other. However, there is an altogether
negative correlation between these factors so that the recovery
rates in times of high failure rates assume lower values and, in
particular in the event of unfavorable developments, may therefore
have further negative impacts on the credit loss. For these
reasons, the recovery rates must be regarded as parameters
dependent on the probability of default. In particular to enable a
better capital allocation, more precise price determination and
better portfolio management to be achieved, a great many other
variables would have to be taken into account for determination of
the recovery rate, e.g. the nature of the credit or the capital
structure of the failed companies. For example, the recovery rate
should also be a function of the asset values of the company.
[0014] In the risk management system disclosed in document WO
02/101500, in the first place trading and market data are
processed, corresponding risk management reports compiled and at
the same time transferred to a series of users of the system. The
risk here is in each case trade-related and placed in a
relationship with trading transactions between several trading
businesses between multiple users who may or may not be connected.
The disclosed system is used in principle to permit precise and
simple assessment of possible risks on a derivatives market.
However, the system does not disclose any determination of recovery
rates and is therefore not suitable for automated credit risk
monitoring of a portfolio.
[0015] Document US 2003/0172017 describes a method and the
corresponding system for the performance of a "Value at Risk" (VaR)
analysis on a large scale. Here the system comprises two different
component types, the controllers to process the data and the
brokers for access control to the corresponding data. Controllers
extract the input data from the input queues, process such data and
write the results into the corresponding output queues is which
serve as the input queue for the next following system component.
Brokers are responsible for the availability of and access to the
jointly used resources. Although this system represents a use of
distributed resources and therefore permits a substantial
improvement of traditional systems it does not provide a tool which
would enable recovery rates to be determined precisely and
automated credit risk monitoring of a portfolio performed.
DISCLOSURE OF THE INVENTION
[0016] One task of this invention is therefore to propose a new
risk monitoring system and a new monitoring method for automated
credit risk monitoring of a portfolio without the drawbacks of the
state of the art referred to above. In particular, a system is to
be proposed for the automated simple and rational credit risk
monitoring to take account of the fact that the Loss Given Default
(LGD) and hence the Recovery Rates are stochastic variables for
every company. The invention will also make available a monitoring
method in which the recovery rates of the individual companies are
suitably determined and standardized to enable the most accurate
possible credit risk determination, and hence optimized portfolio
management, to be achieved. In addition, the invention is intended
to produce a system and a method in which the monitored portfolios
are automatically adapted and the appropriate financial data can be
automatically notified to the relevant financial institutions.
[0017] According to the present invention, this goal is achieved,
in particular, through the characteristics of the independent
claims. Other advantageous forms of implementation likewise emerge
from the dependent claims and the specification.
[0018] In particular, these aims are achieved by the invention in
that corporate data for different companies are transferred to a
monitoring unit and evaluated, one or more assets parameters being
extracted for each company by means of an extraction module in a
company-specific manner from the corporate data, while the asset
parameters of the companies are evaluated stochastically by means
of a statistical module in the monitoring unit in such a way that
at least one company-specific asset distribution is determined and
stored which, based on at least one company-specific asset
distribution, determines the threshold value and is stored in a
subordinated manner, the threshold value being equivalent to the
expected value of one or more asset parameters for the occurrence
of insolvency of a company; that by means of a standardization
module for the monitoring unit based on the particular
company-specific asset distribution and/or threshold value,
recovery rate factors can be determined and standardized, the
company-specific recovery rates factors being stored by means of a
database in a subordinated manner to the corresponding asset
distribution; that with the help of a MonteCarlo module in the
monitoring unit, MonteCarlo asset parameters are generated for each
company by means of which the companies are determined with the
expected recovery rate factors; and that the companies are
displayed with the expected recovery rate factors by means of the
monitoring unit via an output and/or dynamically adjusted in the
portfolio, financial data being transferred via a network to at
least one financial institution. This implementation variant has
the advantage that, on the basis of the company-specific asset
distributions, the particular recovery rates are determined,
standardized and stored for each company and that the companies are
then sorted out and displayed with the relevant recovery rate,
while portfolios can be dynamically adjusted. In addition,
financial data may be transferred automatically via a network to
one or more interested financial institutions without the need for
human supervision or interaction. This enables portfolio management
to be improved substantially in that the necessary adjustments can
be made in real time and automatically.
[0019] In one implementation variant, the lowest expected recovery
rate factors associated with the portfolio are stored on a database
of the monitoring unit; the users benefit from controlled access to
the recovery rate factors via a network and are able to adjust the
portfolio accordingly. This implementation variant has the benefit
that users are able to directly effect monitoring and adjustment of
managed portfolios. A central database for the storage of recovery
rate factors gives a good overview of the different values and
permits more efficient adjustment of the managed portfolios.
[0020] In a further implementation variant, portfolio data
comprising at least the lowest anticipated recovery rate factors
are transmitted to at least one authorized broadcast transmitter
and circulated by at least one broadcast transmitter in an
encrypted and unidirectional manner. This implementation variant
has the advantage, among others, that the portfolio data and, in
particular, the lowest expected recovery rate factors which have
been determined can be made accessible via the broadcast
transmitter to a larger number of users. Potentially many users on
an extremely large territory can therefore be supplied with the
requisite data in this way. The encryption of the portfolio data
which are transmitted guarantees that only users with access
authorization can indeed make use of the transmitted portfolio
data.
[0021] In yet another implementation variant, the system
incorporates receiving devices to receive the access-controlled
portfolio data by means of which access request data can be
transferred to a conditional access server via a mobile telephone
network and the access entitlement data can be transferred via the
mobile telephone network by means of the conditional access server
based on the access request data to the relevant receiving device,
the access-controlled portfolio management data being received by
the receiving devices and the encryption removed by means of the
access authorization data. This implementation variant has the
further advantage that the users' access entitlements are managed
by a centralized authentication and authorization agency. This
enables efficient access control to be guaranteed so that portfolio
management data can only be used by persons having access
authorization. In addition, in this implementation variant the
receiving devices can remain simple as the access authorization
functions do not have to be implemented on the receiver device.
[0022] In another implementation variant, the risk monitoring
system comprises one or more transaction servers to handle billing
data received via the mobile telephone network, said billing data
comprising information about the service required with the receipt
of the portfolio management data. This implementation variant has
the advantage that the services linked to the transmission and
receiving of portfolio management data are also billed centrally by
a transaction server and invoiced to the users. This enables an
efficient billing system to be set up in which the users simply pay
for the services used by them in each particular case.
[0023] In another implementation variant, the receiving devices
have receiving means to receive several DB channels. This
implementation variant has the further advantage that, in
particular, digital broadcasting systems can be used to disseminate
portfolio management data. The use of more than one DB channel
enables data to be sorted by type and/or origin and/or intended
receivers.
[0024] In yet another implementation variant, the receiving devices
have configurable filters to extract relevant portfolio data for a
specific portfolio from the DB data stream and/or to display and/or
store relevant portfolio data for a specific portfolio. This
implementation variant offers, as one of its advantage, the
possibility of separating, in the receiving device, the received
data by means of configurable filters into relevant portfolio data
and irrelevant accompanying data. Thanks to the configurability of
the filters, the nature, scope, number or quantity of relevant
portfolio data can be adjusted individually for each user and
configured according to his particular needs.
[0025] In a further implementation variant, appropriate securities
can be bought and sold via a financial institution by means of the
receiving device on the basis of the portfolio data in the course
of portfolio management. This implementation variant offers as one
of its advantages the possibility of achieving further optimization
of portfolio management in that the calculated recovery rate
factors and the relevant MonteCarlo asset parameters are used to
determine in a dynamic manner the securities which are to be bought
or sold and to enable the particular transaction to be performed
directly via a link to the financial institution.
[0026] In another implementation variant, the receiving devices
have filters to extract portfolio data, corresponding portfolio
management applets and/or URLs or other addresses to load relevant
portfolio management applets from the DB data stream of the
broadcast transmitter. This offers the advantage that such
portfolio management applets or URLs can be broadcast without
encryption, for example as program-accompanying data, together with
the data stream of the broadcast transmitter, while the user gains
easy access to such data via the receiving device.
[0027] In yet another implementation variant, the receiving devices
contain in each case a cost calculation module by means of which
the billing data can be transferred at least partially at regular
intervals, during and/or after access to the access-controlled
portfolio management data, from the receiving device to the
transaction server. This implementation variant has the further
benefit that the costs arising from the services on offer can be
billed automatically and directly to the users.
SHORT DESCRIPTION OF THE DRAWING
[0028] The implementation variants of the present invention are
described below on the basis of examples. The examples of
implementation are illustrated by the following attached
figure:
[0029] FIG. 1 is a block diagram which illustrates the system in
accordance with the invention in schematic form.
FORMS OF IMPLEMENTATION OF THE INVENTION
[0030] FIG. 1 shows in schematic form an architecture which can be
used to implement the invention. In this example of implementation,
the reference numbers 10, 11 and 12 refer to databases containing
company-specific data. These company-specific data comprise, in
particular, controlling data which may be used to assess the credit
risk of any company, for example external data such as the size of
the company, the time since its foundation, activity branch, number
of employees and also internal data such as the balance sheet, cash
flow, credit liabilities and any other kind of relevant data. These
company-specific control data can be updated actively at regular
intervals and also made available by means of a suitable mechanism
in real time.
[0031] The company-specific control data are transferred as shown
in FIG. 1 via a communication network 50 to a monitoring unit 20.
The communication network 50 comprises for example a GSM or UMTS
network or a satellite-based mobile telephone network and/or one or
more fixed networks, for example the Public Switched Telephone
Network (PSTN), the Internet and WWW (World Wide Web) or a suitable
LAN (Local Area Network) or WAN (Wide Area Network). In particular,
it also comprises ISDN and XDSL connections. In FIG. 1, the
reference number 51 likewise refers to a communication network and
reference numbers 60, 61 and 63 to financial and/or banking
institutions. The communication network 51 may, in particular, be
of the same type as the communication network 50 or alternatively
of a different type and linked to communication network 50 by means
of suitable protocols and transmission devices. The term "Financial
and/or Banking Institutions" 60, 61 and 63 refers, in particular,
to banks but also to other investment and/or financial
establishments. In particular, the financial and/or banking
institutions 60, 61 and 63 may also be companies which are active
in online banking or companies which offer services for online
buying and selling of securities.
[0032] The monitoring unit 20 comprises an extraction module 204 by
means of which one or more asset parameters can be extracted from
the control data for the companies concerned. These assets
parameters may comprise all the asset parameters which are used for
the valuation of a business. In addition, the monitoring unit 20
comprises a statistical module 201 by means of which asset
parameters, which are extracted from the extraction module 204, can
be stochastically evaluated and the corresponding company-specific
asset distribution then stored. The statistical module 201 can use
various current or innovative models and algorithms for the
stochastic evaluation of the asset parameters and to determine the
company-specific asset distribution.
[0033] The monitoring unit 20 additionally comprises a storage
module 211 for the determination and associated storage of a
threshold value based on at least one company-specific asset
distribution. This threshold value corresponds in each case to the
expected value of one or more asset parameters. The monitoring unit
20 also comprises a standardization factor 203 to determine and
standardize the recovery rate factors based on the particular
company-specific asset distribution and/or on a threshold value
determined in advance. The aggregated distribution of the loss
given default determined from the individual risks and hence the
corresponding recovery rate factors can be determined in a variety
of ways. When considering the probability distribution of possible
credit default losses, a distinction is normally made between the
expected loss--EL and the unexpected loss--UL. Additionally the
default losses L are defined, corresponding as a rule to the sum of
the expected and unexpected losses. The expectation value EL
corresponds to the statistical mean value of the default losses and
can be estimated readily by a variant of the elementary
Tschebyscheff inequation:
P ( L ~ .gtoreq. EL + UL ) .ltoreq. EL EL + UL ##EQU00001##
[0034] The Tschebyscheff inequation is a very general approach
which does not take account of any assumptions as to the
fundamental probability distribution. In particular, no assumptions
are made as to possible diversification effects. However, the
result corresponds in every case to a valid upper limit for the
insolvency probability. Nevertheless, the actual risks are
considerably overestimated in this determination.
[0035] Other possibilities for the determination of the recovery
rate factors are known from structural models. In those models the
default of a company is triggered by a process of asset value
change for the company concerned. The risk of default therefore
depends to some extent on the variance of the is company value.
Failure or default occurs if the asset V at the time of debt
repayment T is less than the liabilities X of the company. The
outpayment function is therefore the lower of the nominal amount of
the liabilities and of the asset values:
min{X, V}
[0036] From this basic equation, an explicit formula can then be
derived to calculate the probability of failure of loans which
carry a default risk. This can also be used to calculate the spread
between risk-free and risky loans.
[0037] The credit risk components (default probability and recovery
rate) both depend in this model on the volatility of the company
value and its debt level or leverage. The two components are
frequently also subdivided into business risk and financial risk.
The recovery rate is therefore an endogenous variable and depends
upon the residual value of the company. In addition, the
probability of default and the recovery rate are negatively
correlated.
[0038] This relationship between the default probability and
recovery rate can be investigated more accurately by means of the
following determination. We may assume that the assets of a company
follow a geometrical Brownian movement:
dV=.mu.Vdt+.sigma.VdB.sub.t
[0039] in which .mu. is the drift coefficient, .sigma. the
volatility of the company value and Bt a standard Brownian
movement. It follows that the logarithm described in the assets at
time t
log V t = log V 0 + ( .mu. - .sigma. 2 2 ) t + .sigma. B t
##EQU00002##
[0040] has a normal distribution with a mean value
log V 0 + ( .mu. - .sigma. 2 2 ) t ##EQU00003##
and variance .sigma..sup.2t.
[0041] Default occurs if the assets of the company fall short of
its liabilities at the point in time t. The probability of failure
(PD) is therefore given by the following expressions:
PD = P ( V t < X t ) = P ( log V t < log X t ) = P ( log V 0
+ ( .mu. - .sigma. 2 2 ) t + .sigma. B t < log X t ) = P ( log V
0 X t + ( .mu. - .sigma. 2 2 ) t .sigma. t < - ) = .PHI. ( - log
V 0 X t + ( .mu. - .sigma. 2 2 ) t .sigma. t ) = .PHI. ( - d 2 )
##EQU00004##
[0042] in which .PHI. is the distribution function of the standard
normal distribution and d is defined using the Black Scholes option
price model.
[0043] The expected recovery rate in the event of default is now
determined by the ratio between the assets and liabilities V/X at
the point in time t. If default occurs, i.e. V.sub.t<X.sub.t the
expected recovery rate will be
RR = E ( V t X t V t < X t ) = V t X t .mu. t .PHI. ( - d 1 )
.PHI. ( - d 2 ) = E ( V t X t ) .PHI. ( - d 1 ) .PHI. ( - d 2 )
##EQU00005##
in so far as d is likewise defined by analogy with the Black
Scholes model:
d 1 = .PHI. ( - log V 0 X t + ( .mu. + .sigma. 2 2 ) t .sigma. t )
##EQU00006##
[0044] The expected recovery rate in the event of default can
therefore be written as:
RR = E ( V t X t ) .PHI. ( - d 1 ) PD ##EQU00007##
[0045] However, in this case the default is modelled in such a way
that it is only possible at the end of the maturity period.
Therefore, further parameters must be introduced to take account of
the fact that a failure or default occurs when the assets reach a
lower limit value. As a result, the recovery rates can be defined
as exogenous parameters without reference to the asset value of the
company.
[0046] The estimate of the non-observable asset values of the
company is not very easy nor is its volatility which represents the
main problem in all company value models. Frequently therefore
implementation of these models is very difficult in the case of
companies which are not traded on the stock market. To avoid this
problem, the determination can be further refined using what are
known as Reduced Form Models.
[0047] In the Reduced Form Models, the defaults follow a stochastic
intensity process so that, depending on the timing, a particular
probability of an unforeseeable default occurs. The default
probability and recovery rate vary stochastically as a function of
time. The Reduced Form Models are not based on the value of the
company so which therefore does not have to be explicitly
estimated. The failure of a company tends to be regarded rather as
an "unpredictable" and "sudden" event. The probability of default
and the recovery rate are modelled here as a function of a
creditworthiness assessment or a rating. In general, an exogenous
Recovery Rate which is not dependent on the probability of default
is assumed to exist.
[0048] The assets of every company and hence also the Recovery Rate
may be assumed to be dependent on the systematic risk factor X. If
the values of X are small the default rate rises above their
average value, while the Recovery Rate falls below that value.
Every company is now also dependent on another company-specific
non-systematic factor X. The value of the firm Aj of a particular
business can therefore be written as follows.
A.sub.j=pX+ {square root over (1-p.sup.2)}X.sub.j
[0049] where X and X.sub.j have a standard normal distribution and
Aj N(0,1)- is likewise distributed. The parameter p shows the
sensitivity of the asset value to the systematic risk factor.
[0050] In this model world, default of a company occurs if the
asset level falls below a threshold value. Let D be the default of
company j and PD its probability; in that case
D j = { 1 , if A j .ltoreq. .PHI. - 1 ( PD j ) 0 , otherwise
##EQU00008##
[0051] In the case of a large diversified portfolio based on the
rigid law of large numbers with a fixed realization x of the
systematic risk factor X, the following expression applies to the
conditional default probability of a business DF.sub.j;
DF j = P ( A j < .PHI. - 1 ( PD j ) X = x ) = = P ( px ) + 1 - p
2 X j .ltoreq. .PHI. - 1 ( PD j ) ) = .PHI. ( .PHI. - 1 ( PD j ) -
px 1 - p 2 ) ##EQU00009##
[0052] Manifestly, small or negative values of X lead to an
increase in corporate default. As to the Recovery Rate of a
business j. this also depends on the systematic risk factor X and
on a non-systematic factor Z.sub.j:
RR.sub.i=.mu..sub.j+.sigma.qX+.sigma. {square root over
(1-q.sup.2)}Z.sub.j
[0053] Z.sub.j follows the standard normal distribution and is
independent of X. Therefore RR.sub.j is also in a standard
distribution with a mean value p and a variance .sigma..sup.2. Once
again q is a sensitivity factor and the following expression
therefore applies to the corporate value of firm A.sub.j or the
Recovery Rate RR.sub.j.
corr(A.sub.j,R)=p and corr(RR.sub.j,X)=q.
[0054] The monitoring unit 20 likewise comprises a MonteCarlo
module 202 to generate MonteCarlo parameters for each company. This
MonteCarlo module 202 can be used to determine companies in which
the corresponding recovery to rate factors can be expected. Based
on the determinate recovery rate factors and the threshold value
chosen in each case, companies can therefore be determined with a
particularly positive or particularly negative influence on the
portfolio which is to be managed.
[0055] The monitoring unit 20 additionally comprises output
elements 21 to display companies with the expected recovery rate
factors via an output and/or for dynamic adjustment in the
portfolio. Here financial data are transferred via the
communication network 51 to at least one financial institution 60,
61 and 63. The output elements 21 may, in particular, comprise
optical output elements such as computer monitors, television
screens or other displays etc. but also acoustic output elements
such as loudspeakers etc. Additionally the output elements 21 may
comprise physical interfaces to permit the transmission of data, in
particular financial data via the communication network 51. By
means of the output elements 21 direct and dynamic adjustments can
likewise be made to the portfolio. Based on the specified recovery
rate factors and MonteCarlo asset parameters, the securities to be
bought and sold can be determined and the corresponding
transactions performed dynamically in real time by means of the
output elements 21.
[0056] The monitoring unit 20 may likewise comprise a database 211
on which the smallest expected recovery rate factors can be stored
in a manner associated with the portfolio. Via the communication
network 51, users can then gain controlled access to the recovery
rate factors on database 211 and adjust the corresponding
portfolio. For access control purposes any existing or innovative
techniques and methods can be used in particular RADIUS and/or
other similar identification and access control methods.
[0057] In FIG. 1, the reference numbers 521 or 522 denote receiver
devices. Mobile communication devices are particularly suitable for
the implementation of the method in accordance with the invention.
Mobile communication devices 521/522 denote, in particular, all
possible Customer Premises Equipment (CPE) comprising, for example,
all IP capable devices such as mobile telephones, PDAs or laptops.
The receiver devices 521/522 may, however, also be devices produced
specially for the purposes of this invention. Moreover, receiver
devices 521/522 may also be implemented as software components of a
personal computer (PC). In particular, the receiver devices 521/522
are equipped with a physical interface by means of which programs
distributed by the broadcast provider 52 and/or data can be
received via broadcast channels for example via the broadcast cable
network or via a broadcast receiving antenna as radio waves via an
aerial interface. Broadcasting systems with such broadcast
transmitters and broadcast receivers include, for example, digital
audio broadcasting (DAB) and digital video broadcasting (DVB).
[0058] The broadcast transmitter 52 can distribute programs and/or
data on one or more channels which can be received by the receiving
devices 521/522. Here the receiving devices 521/522 may, for
example, simultaneously receive more than one DB channel by means
of suitable receiving facilities. These programs and/or data may,
in particular, comprise portfolio data which are determined by
means of the monitoring unit 20 and transmitted via the
communication network 51 to the broadcast transmitter 52. The
portfolio data may, for example, comprise the lowest expected
recovery rate factors corresponding to companies in a portfolio. To
receive relevant portfolio management data, the receiver devices
521/522 may, in particular, also include configurable filter
systems by means of which relevant portfolio data can be extracted
from the DB data stream for a specific portfolio. By means of
configurable filter facilities in the receiver devices 521/522,
portfolio data relevant to a particular portfolio can likewise be
displayed and/or stored. The receiver devices 521522 can likewise
be used to buy or sell securities directly via a financial
institution 60, 61 and 63 on the basis of the portfolio management
data.
[0059] The receiver devices 521/522 may likewise have one or more
further physical network interfaces which can also support more
than one different network standard. These physical network
interfaces may, for example, be interfaces with local wireless
networks, in particular WLAN (Wireless Local Area Network) 802.11,
Bluetooth and/or GSM (Global System for Mobile Communication), GPRS
(Generalized Packet Radio Service), USSD (Unstructured
Supplementary Services Data), WCDMA (Wideband Code Division
Multiple Access), UMTS (Universal Mobile Telecommunications System)
and also Ethernet, Token Ring and/or other Wired LAN (Local Area
Networks) and hence the Internet and WWW (World Wide Web). The
reference number 51 in FIG. 1 therefore denotes the different
physical networks. The basic principle is that the method and/or
system in accordance with the invention is not tied to a specific
network standard in so far as the characteristics in accordance
with the invention are present, but can be implemented with one or
more desired networks, in particular also because the receiving
devices 521/522, for example mobile communication appliances,
switch transparently between the different networks. In that
regard, the mobile communication appliances 521/522 can, in
particular, support the specifications of the standards for
seamless changeover from voice and data carrier services such as
UMA (Unlicensed Mobile Access) for seamless transition between
WLAN, GSM/GPRS or Bluetooth, SCCAN (Seamless Converged
Communication Across Networks) or Bluephone.
[0060] In addition, the receiver devices 521/522 can include means
of receiving access-controlled portfolio data so that, by means of
the receiver devices 521/522, access request data can be
transmitted and corresponding access authorization data received.
By means of the received access authorization data, the
access-controlled portfolio data can be decoded. The encryption of
the portfolio data can be based upon all known or innovative
encryption procedures and techniques. In FIG. 1, the reference 511
is to a conditional access server. This conditional access server
511 can include the authorization data of the individual users and,
based, in particular, upon received access request data from mobile
communication devices 521/522, give user authorization. All current
or innovative authorization procedures and methods can be used for
this purpose.
[0061] In FIG. 1, the reference 512 is to a transaction server for
billing data received via the communication network 51 including
information provided upon reception of the portfolio management
data. The billing data can in particular be transferred via a
mobile telephone network to the transaction server 512. All current
or innovative billing methods or mechanisms can be used, in
particular those which are associated with pre-paid cards or fixed
user subscriptions. The receiver devices 521/522 can, in
particular, include suitable cost acquisition modules by means of
which the billing data can be transferred to a transaction server
512. This transfer can take place during and/or after access to the
access-controlled portfolio management data on a regular or
non-recurrent basis.
[0062] Attention is called to the fact that the use of the present
invention is not confined to a risk monitoring system for the
automated credit risk monitoring of a portfolio. The applications
are particularly versatile and comprise all risk monitoring tasks
in which specific recovery rate factors play a role.
* * * * *