U.S. patent application number 13/052641 was filed with the patent office on 2011-07-14 for methods and systems for risk management.
This patent application is currently assigned to RealTick LLC. Invention is credited to Chenjian Xu.
Application Number | 20110173135 13/052641 |
Document ID | / |
Family ID | 38475458 |
Filed Date | 2011-07-14 |
United States Patent
Application |
20110173135 |
Kind Code |
A1 |
Xu; Chenjian |
July 14, 2011 |
Methods and Systems for Risk Management
Abstract
In one aspect, the invention comprises acquiring background data
regarding securities positions and regarding real-time pricing
data; performing calculations regarding intermediate measures of
performance of the securities; receiving configuration data for a
portfolio of securities and one or more data requests, at least one
of the data requests comprising a request for a value at risk
report regarding the portfolio; and providing a value at risk
report based on a Parkinson's volatility estimation. In another
aspect, the invention comprises displaying a tree structure display
in a first portion of a graphical user interface display; in
response to a user selecting an item from the tree structure
display, displaying a corresponding listing in a tabular display in
a second portion of the graphical user interface display; and in
response to the user selecting a listing in the tabular display,
displaying a corresponding item in the tree structure display.
Inventors: |
Xu; Chenjian; (Short Hills,
NJ) |
Assignee: |
RealTick LLC
Chicago
IL
|
Family ID: |
38475458 |
Appl. No.: |
13/052641 |
Filed: |
March 21, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11713512 |
Mar 1, 2007 |
7925561 |
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13052641 |
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60778475 |
Mar 1, 2006 |
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Current U.S.
Class: |
705/36R ; 705/35;
715/777 |
Current CPC
Class: |
G06Q 40/04 20130101;
G06Q 40/06 20130101; G06Q 40/00 20130101; G06Q 40/08 20130101 |
Class at
Publication: |
705/36.R ;
705/35; 715/777 |
International
Class: |
G06Q 40/00 20060101
G06Q040/00; G06F 3/048 20060101 G06F003/048 |
Claims
1.-46. (canceled)
47. Software comprising: software for displaying a first portion of
a graphical user interface display comprising a tree structure
display; and software for displaying a second portion of a
graphical user interface display comprising a tabular display,
wherein one or more items in said tree structure display, when
selected by a user, each have corresponding listings displayed in
said tabular display, and wherein one or more of said listings in
said tabular display, when selected by a user, each causes a
corresponding item in said tree structure to be displayed.
48. Software as in claim 47, wherein items in said tree structure
display are selected using checkboxes.
49. Software as in claim 47, wherein listings in said tabular
display are selected using highlighting.
50. Software as in claim 47, wherein said tree structure display
represents a multi-level hierarchy.
51. Software as in claim 50, wherein, for each of one or more
selected items in said tree structure display, a corresponding
listing in said tabular display comprises hierarchical properties
of said one or more selected items in said tree structure
display.
52. Software as in claim 50, wherein at least one level of said
hierarchy corresponds to one or more banks.
53. Software as in claim 47, wherein one or more items in said tree
structure corresponds to an account.
54. A method comprising: displaying a tree structure display in a
first portion of a graphical user interface display; in response to
a user selecting an item from said tree structure display,
displaying a corresponding listing in a tabular display in a second
portion of said graphical user interface display; and in response
to said user selecting a listing in said tabular display,
displaying a corresponding item in said tree structure display.
55. A method as in claim 54, wherein items in said tree structure
display are selected using checkboxes.
56. A method as in claim 54, wherein listings in said tabular
display are selected using highlighting.
57. A method as in claim 54, wherein said tree structure display
represents a multi-level hierarchy.
58. A method as in claim 57, wherein, for each of one or more
selected items in said tree structure display, a corresponding
listing in said tabular display comprises hierarchical properties
of said one or more selected items in said tree structure
display.
59. A method as in claim 57, wherein at least one level of said
hierarchy corresponds to one or more banks.
60. A method as in claim 54, wherein one or more items in said tree
structure corresponds to an account.
61. A method comprising: computing one or more net asset values for
an account based on a broker-dealer's margin rule settings;
computing one or more margin requirements for said account based on
said margin rule settings; computing account buying power based on
said one or more margin requirements and one or more net
liquidation values; and displaying on an account summary screen
computed results for said one or more net asset values, said one or
more margin requirements, and said account buying power.
62. Software comprising: software operable to compute one or more
net asset values for an account based on a broker-dealer's margin
rule settings; software operable to compute one or more margin
requirements for said account based on said margin rule settings;
software operable to compute account buying power based on said one
or more margin requirements and one or more net liquidation values;
and software operable to display on an account summary screen
computed results for said one or more net asset values, said one or
more margin requirements, and said account buying power.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 60/778,475, filed Mar. 1, 2006. The entire contents
of that provisional application are incorporated herein by
reference.
BACKGROUND AND SUMMARY
[0002] Broker-dealers today face unique challenges in risk
management. The proliferation of electronic trading has generated
record numbers of traders and volume. Various risk-taking behaviors
of traders complicate companies' credit policies and their risk
management process. Real-time risk management software is needed to
address the specific needs of today's investment companies.
[0003] Market risk has the most direct impact on a firm. Changes in
the value of investment instruments directly affect the asset value
of trading accounts. While most accounts are well funded, some of
them may encounter situations where asset value fails to cover the
risk of leverage. To deal with this problem, most firms have
established credit policies via so-called margin rules, wherein
accounts are classified based on their risk profiles, and assets
within the accounts are appropriately margined based on a defined
set of rules.
[0004] Most exchanges, clearing companies, and broker-dealers have
their own margin rules clearly defined. Recently, Standard
Portfolio Analysis of Risk (SPAN.RTM.) of Chicago Mercantile
Exchange (www.cme.com) and Theoretical Inter-market Margin System
(TIMS) of Options Clearing Corporation (www.optionsclearing.com)
have gained traction in margining future contracts and options.
Properly margining each account at the end of a trading day via a
back-office batch process is a standard practice. However, by the
time a problem has been detected by such a delayed process,
significant damage may already have been done. In today's
ever-changing markets, real-time detection of margin violations,
cross asset types and cross multiple currencies, is needed to
control the risk to the firm.
[0005] Some financial software vendors, especially those who
specialize in real-time systems, have started to introduce near
real-time risk management software to meet the demand for intraday
risk management. Leveraging the real-time quoting and trading
systems, these risk management systems scan thousands of trading
accounts to compute financial measures such as asset market values,
gains or losses and margin requirements, using real-time prices and
trading activities. Risk reports are generated intraday based on
these measures to warn risk managers of potential problems. Today's
powerful hardware and modem parallel programming models deployed in
software make it possible to complete such complex tasks in a very
reasonable timeframe. For instance, the Credit-at-Risk (CaR)
feature comprised in one aspect of the present invention is capable
of delivering such reports every minute.
[0006] One of the major challenges facing such surveillance risk
management systems is information overload. The vast amount of
information generated by real-time data on thousands of accounts
makes it impossible for risk managers to digest the important
information and act quickly, Software developers have employed many
concepts in traditional decision support systems (DSS) as
extensions to the core surveillance capability. For example, in one
aspect of the present invention, instead of furnishing full risk
reports, exception reports based on user-defined criteria may be
generated to report a manageable number of problematic items where
risk tends to concentrate. Further analytical capabilities may be
introduced via drill-down features, where users can navigate to
detailed information intuitively via the items highlighted by
exception reports. In many cases, the analytical capabilities
themselves become extremely valuable in researching problems and
recommending appropriate actions.
[0007] While risk management is becoming more and more important in
the financial industry, many broker-dealers have started to demand
instant responses to risk. For instance, some industry sectors are
moving from T+1 margin call policy, where margin calls are made
after an end-of-day margin report, to T+0 margin call policy, where
margin calls are made as soon as a margin violation is discovered.
Further, in trading with highly volatile and highly leveraged
instruments, such as futures and foreign exchanges, such real-time
risk reporting is necessary to address a risky situation within
minutes to limit losses. The availability of real-time risk
management systems is a revolutionary force gradually altering how
risk management desks operate at broker-dealers.
[0008] Another key risk facing broker-dealers is the risk-taking
behavior of their own customers, the traders. Increasingly,
professional class research and trading software, such as
RealTick.RTM. (www.realtick.com), are being deployed to retail
traders' desktops. These trading tools are the main contributors to
today's increasingly electronic trading environment and high
liquidity. Yet the impressive firepower of these trading platforms
requires equally capable risk management components to regulate the
trader's behavior.
[0009] Developed upon and extending the traditional approach for
pre-trade risk management, real-time margining, today's margin
systems embedded in the trading platform can compute margin
requirements based on portfolio risk. For instance, RealTick's
risk-based margin system scans traders' account positions and
pending trades in real-time to compute margin requirements on
current positions and pending trades, while recognizing hedged
positions such as spreads and covers for margin credits. Accurate
pre-trade and post-trade portfolio margins are computed based on
real-time price information and are compared to determine if the
account has sufficient buying power to cover proposed trades.
Furthermore, special margin algorithms are applied to keep the
number inline with regulatory requirements and exchange rules. In
RealTick's case, instruments on the Milan Stock Exchange are
margined using the TIMS method to comply with Italian regulations,
while CME futures are margined according to exchange published
rules, cognizant of its intra- and inter-commodity spread
definitions. This capability makes it nearly impossible for traders
to trade into margin call situations, while promptly adjusting
their account's buying power depending on the price movements.
[0010] The combination of pre-trade risk-based margining and
post-trade margin-based risk surveillance is powerful enough to
address credit risk imposed by traders in most cases. However,
margin rules are usually overly general, incapable of addressing
the volatility of individual instruments and the correlating asset
amounts. This situation makes the CaR method ineffective in
addressing the firm's settlement risk and assessing the firm's
capital adequacy. Broker-dealers, especially those who are
self-clearing, must employ a statistical method to project such
risk. The conventional method used for this purpose is
Value-at-Risk (VaR).
[0011] VaR measures the worst expected loss under normal market
conditions over a specific time interval at a given confidence
level. Since gaining popularity in the 1990s, VaR is now widely
used to report short-term market risk and to assess capital
adequacy of investment companies. Today's risk management practices
usually employ one of the three most popular methods for VaR
reporting: variance-covariance approach, historical simulation, and
Monte Carlo simulation.
[0012] One of the key features of VaR is its comparability. Given
the size of a portfolio, the greater the VaR, the greater the risk,
regardless of the content of the portfolio. This property enables
two major applications in risk management: (1) when VaR reports are
created for the same portfolio periodically, one can track the
level of risk-taking over time, and (2) when VaR reports are
created for a number of portfolios at the same time, risk levels of
these portfolios can be compared side-by-side. By tracking
firm-wide risk over time, management can obtain a strategic view of
risk-taking behavior at an aggregated level. This is an important
component of financial reporting, as well as of long-term risk
management practices. Side-by-side comparison of portfolio risk
gives risk managers a tactical tool to analyze risk distribution
within an investment domain. Being able to quickly spot areas with
the greatest risk concentration gives risk managers a means to act
promptly to avoid damages.
[0013] The risk management software described herein has gone a
step further to add analytical capabilities on top of VaR
reporting. One embodiment bundles drill-down capability into its
VaR reporting so that users can further examine risk for groups of
stocks and options with the same underlying asset, as well as for
individual positions, and rank the subsets based on risk level.
With only a few clicks, a risk manager can pinpoint key areas that
are riskiest to the portfolio. In many cases, what-if scenario
analysis tools are also included to assist the risk manager in
researching appropriate actions to mitigate risk.
[0014] Because of the computational complexity in VaR calculations,
most companies use an overnight batch process to generate the
reports. This practice is satisfactory for strategic control of the
firm risk, but not sufficient for tactical analysis, intraday risk
control, what-if studies, and risk-based trading. To enable
on-demand VaR reporting, vendors employ various ways to deal with
the complexity. An embodiment of the present invention employs an
overnight batch process to compute intermediate risk measures, such
as volatility and correlations, and real-time prices and positions
are used to generate on-demand VaR reports. The practice is so
effective that a ten thousand position portfolio can have its VaR
computed within twenty seconds.
[0015] Many broker-dealers have started to take full advantage of
technological advances in real-time risk management software. The
pre-trade risk-based margin system nearly eliminates the
possibility of having a trader's risk-taking behavior cause a
violation of the firm's credit policy. The real-time margin-based
risk surveillance makes it possible to catch credit policy
violations immediately. The on-demand VaR calculation provides risk
managers tools to spot risk concentration and take appropriate
action to mitigate risk. After deploying such software packages,
many firms see an immediate productivity gain on their risk desks.
Over time, risk managers, who are relieved from repetitive routine
work now handled by the software, spend more and more time solving
complex problems that require human intervention. Some firms also
take advantage of the analytical capability of the software to add
depth to the level of service offered to their clients. The
virtuous loop of lower risk, improved productivity, more intimate
client service, and growing business is making risk management
practice a profit generator.
[0016] In one aspect, the invention comprises a method comprising:
(a) acquiring background data regarding securities positions and
regarding real-time pricing data; (b) performing calculations
regarding intermediate measures of performance of the securities;
(c) receiving configuration data for a portfolio of securities and
one or more data requests, at least one of the data requests
comprising a request for a value at risk report regarding the
portfolio; and (d) providing a value at risk report based on the
background data, the calculations, and the configuration data,
wherein the value at risk report is based on a Parkinson's
volatility estimation.
[0017] In various embodiments: (1) the step of acquiring background
data comprises obtaining real-time data regarding positions from
one or more order management systems; (2) the step of acquiring
background data comprises obtaining real-time pricing data from one
or more market data services; (3) the step of acquiring background
data comprises obtaining high-low volatility data based on a
plurality of recent trading days; (4) the plurality of recent
trading days is approximately ten days; (5) the step of performing
calculations regarding intermediate measures of performance of the
securities comprises computing implied volatility for options based
on a Black-Scholes formula and market prices; (6) the step of
performing calculations regarding intermediate measures of
performance of the securities comprises computing multi-point risk
arrays based on option implied volatility and stock high-low
volatility; (7) the step of receiving configuration data for a
portfolio of securities comprises receiving the calculated
intermediate measures of performance; (8) intermediate measures of
performance comprise positions, volatilities, and risk arrays; (9)
the step of providing a value at risk report comprises grouping
positions by underlying securities; (10) the step of providing a
value at risk report comprises aggregating risk arrays; (11) the
step of providing a value at risk report comprises aggregating risk
arrays for futures positions within each of one or more portfolios
and applying correlation coefficients; and (12) the step of
providing a value at risk report comprises transmitting the report
to a graphical user interface for display.
[0018] In another aspect, the invention comprises software
comprising: (a) software operable to acquire background data
regarding securities positions and regarding real-time pricing
data; (b) software operable to perform calculations regarding
intermediate measures of performance of the securities; (c)
software operable to receive configuration data for a portfolio of
securities and one or more data requests, at least one of the data
requests comprising a request for a value at risk report regarding
the portfolio; and (d) software operable to provide a value at risk
report based on the background data, the calculations, and the
configuration data, wherein the value at risk report is based on a
Parkinson's volatility estimation.
[0019] In various embodiments: (1) the software operable to acquire
background data is operable to obtain real-time data regarding
positions from one or more order management systems; (2) the
software operable to acquire background data is operable to obtain
real-time pricing data from one or more market data services; (3)
the software operable to acquire background data is operable to
obtain high-low volatility data based on a plurality of recent
trading days; (4) the plurality of recent trading days is
approximately ten days; (5) the software operable to perform
calculations regarding intermediate measures of performance of the
securities is operable to compute implied volatility for options
based on a Black-Scholes formula and market prices; (6) the
software operable to perform calculations regarding intermediate
measures of performance of the securities is operable to compute
multi-point risk arrays based on option implied volatility and
stock high-low volatility; (7) the software operable to receive
configuration data for a portfolio of securities is operable to
receive the calculated intermediate measures of performance; (8)
intermediate measures of performance comprise positions,
volatilities, and risk arrays; (9) the software operable to provide
a value at risk report is operable to group positions by underlying
securities; (10) the software operable to provide a value at risk
report is operable to aggregate risk arrays; (11) the software
operable to provide a value at risk report comprises aggregating
risk arrays for futures positions within each of one or more
portfolios and applying correlation coefficients; and (12) the
software operable to provide a value at risk report is operable to
transmit the report to a graphical user interface for display.
[0020] In another aspect, the invention comprises a method
comprising: (a) implementing a variance-covariance model; (b)
calculating a Parkinson's volatility approximation with intra-day
adjustments; (c) performing a periodic batch option revaluation
based on a Black-Scholes model; (d) modeling a multi-point risk
array for intermediate measures of theoretical prices; (c)
estimating correlations based on a multivariate model; (f)
implementing quadratic regression for delta/gamma estimation; and
(g) generating a value at risk report.
[0021] In various embodiments: (1) for each of one or more
positions in the portfolio, one or more elements of a corresponding
risk array are computed based on a product of price, volume, and
risk array elements for the security in which the position is held;
(2) quadratic regression is performed on data points derived at
least in part from the risk array; (3) values of securities in the
portfolio are assumed to be log normally distributed; (4)
derivatives in the portfolio are assumed to be non-linearly
distributed; (5) zero correlation is assumed among assets in
different asset classes; (6) correlation of 0.5 is assumed among
assets within an asset class; (7) high correlation is assumed for
assets with the same underliers; (8) the value at risk report
comprises three levels: portfolio, underlier group, and position;
and (9) a {square root over (T)} rule is used to estimate daily
volatility intraday.
[0022] In another aspect, the invention comprises software
comprising: (a) software operable to implement a
variance-covariance model; (b) software operable to calculate a
Parkinson's volatility approximation with intra-day adjustments;
(c) software operable to perform periodic batch option revaluation
based on a Black-Scholes model; (d) software operable to model a
multi-point risk array for intermediate measures of theoretical
prices; (e) software operable to estimate correlations based on a
multivariate model; (f) software operable to implement quadratic
regression for delta/gamma estimation; and (g) software operable to
generate a value at risk report.
[0023] In various embodiments: (1) for each of one or more
positions in the portfolio, one or more elements of a corresponding
risk array are computed based on a product of price, volume, and
risk array elements for the security in which the position is held;
(2) quadratic regression is performed on data points derived at
least in part from the risk array; (3) values of securities in the
portfolio are assumed to be log normally distributed; (4)
derivatives in the portfolio are assumed to be non-linearly
distributed; (5) zero correlation is assumed among assets in
different asset classes; (6) correlation of 0.5 is assumed among
assets within an asset class; (7) high correlation is assumed for
assets with the same underliers; (8) the value at risk report
comprises three levels: portfolio, underlier group, and position;
and (9) a {square root over (T)} rule is used to estimate daily
volatility intraday.
[0024] In another aspect, the invention comprises software
comprising: (a) software for displaying a first portion of a
graphical user interface display comprising a tree structure
display; and (b) software for displaying a second portion of a
graphical user interface display comprising a tabular display,
wherein one or more items in the tree structure display, when
selected by a user, each have corresponding listings displayed in
the tabular display, and wherein one or more of the listings in the
tabular display, when selected by a user, each causes a
corresponding item in the tree structure to be displayed.
[0025] In various embodiments: (1) items in the tree structure
display are selected using checkboxes; (2) listings in the tabular
display are selected using highlighting; (3) the tree structure
display represents a multi-level hierarchy; (4) for each of one or
more selected items in the tree structure display, a corresponding
listing in the tabular display comprises hierarchical properties of
the one or more selected items in the tree structure display; (5)
at least one level of the hierarchy corresponds to one or more
banks; and (6) one or more items in the tree structure corresponds
to an account.
[0026] In another aspect, the invention comprises a method
comprising: (a) displaying a tree structure display in a first
portion of a graphical user interface display; (b) in response to a
user selecting an item from the tree structure display, displaying
a corresponding listing in a tabular display in a second portion of
the graphical user interface display; and (c) in response to the
user selecting a listing in the tabular display, displaying a
corresponding item in the tree structure display.
[0027] In various embodiments: (1) items in the tree structure
display are selected using checkboxes; (2) listings in the tabular
display are selected using highlighting; (3) the tree structure
display represents a multi-level hierarchy; (4) for each of one or
more selected items in the tree structure display, a corresponding
listing in the tabular display comprises hierarchical properties of
the one or more selected items in the tree structure display; (5)
at least one level of the hierarchy corresponds to one or more
banks; (6) one or more items in the tree structure corresponds to
an account.
[0028] In another aspect, the invention comprises a method
comprising: (a) computing one or more net asset values for an
account based on a broker-dealer's margin rule settings; (b)
computing one or more margin requirements for the account based on
the margin rule settings; (c) computing account buying power based
on the one or more margin requirements and one or more net
liquidation values; and (d) displaying on an account summary screen
computed results for the one or more net asset values, the one or
more margin requirements, and the account buying power.
[0029] In another aspect, the invention comprises: (a) software
operable to compute one or more net asset values for an account
based on a broker-dealer's margin rule settings; (b) software
operable to compute one or more margin requirements for the account
based on the margin rule settings; (c) software operable to compute
account buying power based on the one or more margin requirements
and one or more net liquidation values; and (d) software operable
to display on an account summary screen computed results for the
one or more net asset values, the one or more margin requirements,
and the account buying power.
BRIEF DESCRIPTION OF DRAWINGS
[0030] FIG. 1 depicts a preferred graphical user interface (GUI)
for tree structure navigation.
[0031] FIG. 2 depicts a preferred GUI including a populated
grid.
[0032] FIG. 3 depicts a preferred GUI illustrating navigation
facilitated by the grid.
DETAILED DESCRIPTION
[0033] Risk Manager Software
[0034] In one aspect, the present invention comprises Risk Manager
software that preferably has two major components: Credit at Risk
(CaR) and Value at Risk (VaR).
[0035] VaR is a software component whose user interface portion
resides in Risk Analyzer (the client) and whose analytics portion
resides in RiskEngine (the server). It pulls intermediate measures
out of the RiskEngine onto a user's desktop, computes the final
measures, and displays a report based on the user's selection
criteria.
[0036] CaR is a near real-time reporting tool that monitors broker
account credit risk--for example, CaR preferably computes net asset
value and margin requirement on each account based on a broker
dealer's margin rule settings; derives account buying power; and
warns risk managers when an account violates the broker dealer's
credit policy. If the account user violates the broker dealer's
margin rules severely, the risk manager may use margin calls and/or
forced liquidation.
[0037] In an embodiment, the VaR implementation of one aspect of
the present invention is real-time, while prior art VaR
implementations are overnight batch jobs. To enable real-time
responses, the VaR embodiment preferably uses a variance-covariance
model, Parkinson's volatility approximation with intraday
adjustments, every-minute batch option revaluation based on Black
Scholes model, a 19-point risk array for intermediate measures for
theoretical prices, a multivariate model to estimate correlations,
and quadratic regression for Delta/Gamma estimation.
[0038] In one aspect, the invention comprises stages of: (1)
background data acquisition; (2) pre-calculation for intermediate
measures; (3) client request and data loading; and (4) report
creation.
[0039] Background data acquisition, in certain embodiments,
comprises the following steps: (a) Risk Manager connects with order
managements systems to maintain data regarding real-time positions;
(b) Risk Manager connects with market data services to obtain
real-time pricing data for all security types; and (c) Risk Manager
loads high-low volatility data based on past 10 days' data.
[0040] Pre-calculation for intermediate measures, in certain
embodiments, comprises the following steps: (a) use a Black-Scholes
formula and market prices to compute implied volatility and Greeks
for options; and (b) use option implied volatility and stock
high-low volatility to compute 19-point risk arrays.
[0041] Client request and data loading, in certain embodiments,
comprises the following steps: (a) client configures portfolios
using Portfolio Manager and upload to server; (b) client requests
VaR (report; and (c) intermediate measures (e.g., positions,
volatilities, and risk arrays) are received by Risk Analyzer.
[0042] Report creation, in certain embodiments; comprises the
following steps: (a) positions are grouped by underlying securities
and risk arrays are aggregated; (b) risk arrays are future
aggregated across portfolio and correlation coefficients are
applied; (c) final measures are computed; and (d) the report is
sent to the user interface.
[0043] More details on the above and other embodiments are provided
below. The embodiments described herein are intended to be
exemplary only. It is not believed to be possible or practical to
describe every embodiment encompassed by the invention. Those
skilled in the art will recognize that many other embodiments, not
described herein, are encompassed by the invention and within the
scope of the appended claims.
[0044] Prior art methodologies include:
[0045] 1) Historical and Monte Carlo simulations, which are more
time consuming than variance-covariance models.
[0046] 2) Historical volatility, which is based on time consuming
regression analysis, and is incapable of intraday correction.
[0047] 3) Binomial option pricing model, which is more time
consuming than Black Scholes.
[0048] 4) On-demand option revaluation, which has higher resource
requirements than storing risk arrays.
[0049] 5) Pair-wise correlation coefficient matrix, which is 1000
times more resource consuming than multivariate models.
[0050] 6) Weighted average on Delta, which is less accurate than
quadratic regression.
[0051] A Risk-based Margin embodiment is real-time, while prior art
products are overnight batch jobs. An implementation takes an
active approach in order to identify hedged positions to improve
performance. This approach takes full advantage of the real-time
trading and pricing infrastructure built for the RealTick system,
and works as well for systems with similar features.
[0052] Competing products tend to be stand alone products, which
have problems taking advantage of a real-time quoting and trading
system.
[0053] The term "VaR" is used herein to refer to both the concept
described below and to inventive software that calculates and uses
that concept.
[0054] Value at Risk (VaR) is a projected (with a certain level of
probability) amount of money a financial portfolio may lose over a
period of time. For instance, ($100, 1 day, 95%) means a portfolio
has a 95% chance of NOT losing more than $100 during a 1 day
period.
[0055] In the United States, the 1-day 95% standard is published by
RiskMetrics.TM.. In Europe, a more conservative 10-day 99% standard
is published by the BASLE Committee on Banking Supervision.
Software of an embodiment of the present invention, referred to
herein for convenience as "Risk Manager," provides both measures,
as well as two more intermediate levels, 1-day 99% and 10-day 95%.
Any VaR reporting is based on historical data, thus its forecast is
always considered valid under "normal circumstances."
[0056] Real-Time Price
[0057] Overnight positions supplied by clearing companies via
morning files preferably are uploaded to a Risk Database every
trading day, prior to market open, by MFImporter. Trade executions
are uploaded to the Risk Database by the TradeStuffer process near
real-time. The server component of Risk Manager, RiskEngine,
queries the Risk Database for a list of securities and queries
price servers to obtain real-time price information as well as
additional attributes.
[0058] Having near real-time price loads is not only an important
feature for reporting equity risk, but also an essential element of
estimating theoretical prices for derivatives. The system assumes a
semistrong form of market efficiency, which means that all public
information is reflected in the pricing of the assets. Thus, it
uses the real-time prices to estimate hard-to-obtain economic
measures, such as the risk-free interest rate and implied
volatility of options underliers.
[0059] Volatility Estimation
[0060] The primary historical data required to compute VaR is the
historical volatility information for each security. Many methods
are used in the industry to estimate volatility. The most popular
methods include the Simple Moving Average (SMA), Exponentially
Weighted Moving Average (EWMA), and the more general GARCH method.
Preferably, Risk Manager uses Parkinson's High-Low Range Volatility
as its primary estimation method.
[0061] Parkinson's Method
[0062] The Parkinson number, or High-Low Range Volatility,
developed by the physicist Michael Parkinson in 1980, aims to
estimate the volatility of returns for a random walk, using the
high and low in any particular period. Parkinson's volatility
number is computed as follows, where H.sub.t is the period high and
L.sub.t is the period low (t being the period).
V = 1 4 ln 2 t = 1 n ( ln H t L t ) 2 n ##EQU00001##
[0063] V, Parkinson's Volatility, is an estimate of standard
deviation of the rate of return on a particular security. The rate
of return, R, is a continuously compound rate for the period
defined as follows, where P.sub.1 is the closing price in period 1,
and P.sub.0 is the closing price for the previous period. n is the
total number of observations, and t=0, 1, 3, . . . , n are
individual observations.
R = ln P 1 P 0 ##EQU00002##
[0064] Parkinson showed that the extreme value method is far
superior to the traditional method and much more sensitive to
variations of dispersion. Using the Parkinson method to estimate
volatility can be of particular importance in studies of time and
price dependence on volatility, as less data is needed to derive a
given accuracy compared to the much larger amount of data required
when using regression-based traditional methods.
[0065] Intraday Estimates
[0066] The Risk Manager standardizes all rates of return to a
compounded daily percentage. Since volatility is a measure of the
daily rate of return based on daily highs and lows, during the
trading day, this number can only be estimated. RiskEngine will
start its estimates one hour after trading begins on the security
to minimize impact of random factors in the marketplace. The
estimation formula is as follows: H and L are high and low, T.sub.D
is the total time of the trading day, and T.sub.0 is the time
period that has lapsed since the security started its trading for
the day.
V = T D T 0 1 2 ln 2 ln H L ##EQU00003##
[0067] Intuitively, this method computes the Parkinson's Volatility
using the high and low of the trading hours. Then, it applies a "
{square root over (T)} rule" to estimate the volatility of the
trading day, assuming asset prices are log-normally distributed and
serial independent.
[0068] Historical Average
[0069] The intraday estimate captures the volatility during the
trading hours, yet contains significant abnormal short-term
volatilities, such as those associated with information dispersion.
To address this problem, the Risk Database keeps 10 to 15 trading
days of Parkinson's Volatility as history. The squared average for
the past trading days and intraday number is the final volatility
number being used by the application. The following formula is used
to compute squared average, where V.sub.i are daily Parkinson's
volatilities.
V = i = 1 n V i 2 n ##EQU00004##
[0070] Other Economic Measures
[0071] While theoretical values of stocks, bonds, and futures may
be computed using volatility data, derivatives require non-linear
models, including Black-Scholes and pseudo-American. Thus, other
economic measures, such as risk free interest rates and underlier
volatility, must be estimated. Risk Manager preferably uses 5% as
the risk free interest rate, regardless of the holding period, in
order to simplify the process, but it can be adjusted as needed.
The underlier volatility is estimated using a Black-Scholes model
(i.e., Newton-Raphson estimation for implied volatility). The
detailed process is explained in the Risk Array for Derivatives
section below.
[0072] Intermediate Measures
[0073] The VaR calculation is computationally intensive, especially
when derivatives are involved. There are several approaches on the
market that make the process manageable. Some systems, such as
RiskMetrics.TM., use delta-gamma estimation to compute option
theoretical values. The drawback is that the method can deal only
with situations when underlier movement is relatively small, thus
undervaluing the risk on derivatives on volatile securities. SPAN
and TIMS, on the other hand, use a full valuation model, which is
the most resource consuming. To limit the burden on user systems,
they conduct most of the complex computations on the server side in
an overnight batch job and store the resulting theoretical prices
based on different scenarios in Risk Arrays. This way, client
desktops only need to act as reporting tools that apply basic
arithmetic to the Risk Arrays to form final reports. Risk Manager
preferably uses a method similar to those of SPAN and TIMS for
options valuation, but it improves and expands on that approach by
estimating intraday volatility and conducting Risk Array
computations every two minutes, due in large part to the advantages
of using Parkinson's method.
[0074] When an account is flagged as TIMS-compliant, Risk Manager
preferably triggers a calculation that applies TIMS methodology,
using an extension library to the above-described software, which
in an embodiment is part of the server side of Risk Analyzer.
[0075] Risk Array for Fundamental Assets
[0076] The Risk Array preferably stores price indices relative to
the current trading price in a 19 element array. The middle
position is 1, representing the current price itself, and other
positions are theoretical prices as a ratio to the current price
based on different scenarios. Table 1 illustrates a Risk Array for
a stock. Since volatility is the standard deviation of return, we
use the standard deviation symbol .sigma. to represent
volatility.
TABLE-US-00001 TABLE 1 Position Value = e.sup.z.sigma. Volatility
(.sigma.) Z Probability -9 0.832 2.5% -7.36 10 day 1% -8 0.878 2.5%
-5.22 10 day 5% -7 0.916 2.5% -3.49 -6 0.943 2.5% -2.33 1 day 1% -5
0.960 2.5% -1.65 1 day 5% -4 0.974 2.5% -1.05 -3 0.984 2.5% -0.65
-2 0.990 2.5% -0.4 -1 0.994 2.5% -0.25 0 1.000 2.5% 0 1 1.006 2.5%
0.25 2 1.010 2.5% 0.4 3 1.016 2.5% 0.65 4 1.027 2.5% 1.05 5 1.042
2.5% 1.65 1 day 95% 6 1.060 2.5% 2.33 1 day 99% 7 1.091 2.5% 3.49 8
1.139 2.5% 5.22 10 day 95% 9 1.202 2.5% 7.36 10 day 99%
[0077] In the above example, we assume the stock has a daily
volatility of 2,5%, and Z is the standard normal random variable.
At position -5, for instance, the theoretical value of the stock is
0.96 times current price. Since Z -1.65 at this point, there is a
N(Z<-1.65)=5% chance that the stock value will be below this
number, where N(x) is the standard normal cumulative
distribution.
[0078] For a 1 day forecast, positions -6 to 6 are sufficient.
However, when we need to expand the forecast to 10 days, we need to
apply the {square root over (T)} rule (i.e., .sigma..sub.10=
{square root over (T)}.sigma., where T=10). Since
Value=ez.sigma.this is equivalent to applying the {square root over
(T)} rule to Z. Thus, for a 10 day forecast, positions -9, -8, 8,
and 9 are used.
[0079] Risk Array for Derivatives
[0080] Risk Arrays for derivatives preferably have the same format
as those of stocks. Table 2 shows a Risk Array for an option.
TABLE-US-00002 TABLE 2 Position Value = e.sup.z.sigma. Volatility
(.sigma.) Z Probability -9 0.113 1.24% -7.36 10 day 1% -8 0.239
1.24% -5.22 10 day 5% -7 0.407 1.24% -3.49 -6 0.562 1.24% -2.33 1
day 1% -5 0.671 1.24% -1.65 1 day 5% -4 0.778 1.24% -1.05 -3 0.856
1.24% -0.65 -2 0.907 1.24% -0.4 -1 0.939 1.24% -0.25 0 1.000 1.24%
0 1 1.050 1.24% 0.25 2 1.085 1.24% 0.4 3 1.144 1.24% 0.65 4 1.244
1.24% 1.05 5 1.403 1.24% 1.65 1 day 95% 6 1.596 1.24% 2.33 1 day
99% 7 1.959 1.24% 3.49 8 2.569 1.24% 5.22 10 day 95% 9 3.419 1.24%
7.36 10 day 99%
[0081] The key difference is that Volatility and Z are now for the
underliers rather than the derivatives themselves. The values
preferably are now based on a more complex Black-Scholes model,
which makes them non-linear.
[0082] Let's assume the Black-Scholes model has the following form,
where P=Option Price, P.sub.u=Underlier Price, R.sub.f=Risk Free
Interest Rate, T=Time to Expiration, and V=Underlier
Volatility:
P=BS(P.sub.u, R.sub.f, T, V)
[0083] The system first uses Current Option Price to solve for
Implied Volatility (V.sub.0) using Newton-Raphson estimation. Then
it computes the Risk Array for the option, where i=-9, -8, . . . ,
0, . . . , 8, 9, and R.sub.ui is the Risk Array for the
underlier:
R i = BS ( P u R ui , R f , T , V ) P u ##EQU00005##
[0084] The simulated price index points now carry exactly the same
probability as those of the underliers, representing perfect
correlation. This allows us to easily construct the market value
Risk Arrays for a group of positions with the same underlier.
[0085] Greeks
[0086] The following Greeks preferably are computed for each
option, while their definitions are extended to cover all
securities.
[0087] Delta: Change of option price per unit value change of the
underlier. It is the first derivative of the Black Scholes
formula.
[0088] Gamma: Change of Delta per unit value change of the
underlier. This is the second derivative of the Black Scholes
formula.
[0089] Vega: Change of option price per percentage change of the
underlier's annualized volatility.
[0090] Theta: Change of option price per day reduction in
time-to-expiration.
[0091] Lambda: Percentage change of option price per percent change
in underlier value.
[0092] These Greeks are computed and reported to users. Delta and
Lambda are available for aggregation to a group of assets with the
same underlier (i.e., the "Underlier Group").
[0093] Value at Risk Measures
[0094] VaR for Underlier Group
[0095] For each position in a portfolio, a Market Value Risk Array
preferably is computed as follows, where R, is the Risk Array for
the security and mv.sub.i, is Market Value:
mv.sub.i=pricevolumeR.sub.i
[0096] Then a portfolio is first divided into Underlier Groups
(groups of positions that have the same underlier). A Market Value
Risk Array then is aggregated as follows:
UMV.sub.i=.SIGMA.mv.sub.i
[0097] Intuitively, since all positions in an underlier group have
the same underlier, their market value movements are perfectly
correlated. Since each position of the Risk Array represents one
possible scenario of underlier movement, the Underlier Group Market
Value Risk Array contains the possible market value of the group
for each scenario. The Z value and the cumulative probability of
each scenario is exactly the same as the Risk Array of the
underlier. Therefore, for the underlier group,
VaR 1 Day 95%=MIN(UMV[-5, -4, . . . , 4, 5])-UMV[0]
VaR 1 Day 99%=MIN(UMV[-6, -5, . . . , 5, 6])-UMV[0]
VaR 10 Day 95%=MIN(UMV[-8, -7, . . . , 7, 8])-UMV[0]
VaR 10 Day 99%=MIN(UMV[-9, -8, . . . , 8, 9])-UMV[0]
[0098] In the meantime, the simulated 18 scenarios allow us to
estimate two other important risk measures, the Delta and the
Lambda (elasticity), for the underlier group.
[0099] We take the middle three data points (UMV-1, PR-1),
(UMV.sub.0, PR.sub.0) and (UMV.sub.1, PR.sub.1) to conduct a
quadratic regression: UMV(PR=aPR.sup.2+bPR+c, and thus,
a = UMV 0 ( PR 1 - PR - 1 ) + UMV - 1 ( PR 0 - PR 1 ) + UMV 1 ( PR
- 1 - PR 0 ) ( PR 0 - PR - 1 ) ( PR 0 - PR 1 ) ( PR 1 - PR - 1 )
##EQU00006## and ##EQU00006.2## b = UMV 0 - UMV - 1 PR 0 - PR 01 -
a ( PR 0 + PR - 1 ) ##EQU00006.3##
[0100] Delta is the first derivative, or the slope, at current
market value UMV.sub.0. Therefore,
delta u = UMV PR = 2 aPR 0 + b ##EQU00007##
[0101] The following risks in underlier groups are addressed by
provision of the above measures:
TABLE-US-00003 TABLE 3 Type of Risk Measure Remedy Volatility
Volatility: standard deviation of annualized Hedge with derivatives
continuously compound underlier price change Sensitivity Delta:
market value change per $1 underlier price Hedge with option change
spreads Lambda: percentage market change per 1% underlier price
change Concentration Market Value as a percentage of the portfolio
Diversification Overall VaR All of above
[0102] VaR for Portfolio
[0103] There is no perfect way to aggregate VaR to a portfolio,
especially when the portfolio contains multiple types of assets and
derivatives. Risk Manager preferably makes the following
assumptions during its aggregation process.
TABLE-US-00004 TABLE 4 Assumption Implication Remedy Moderately
Stock values are log normally If a portfolio is not diversified
distributed, while derivatives are non- diversified or its linearly
distributed. The value of an non delta-normal aggregated portfolio,
if fairly component diversified, should be normally weighted
heavily, distributed, examining key underlier groups is required.
Average Among different asset classes, zero Same as above.
correlation correlation is assumed. Within the same asset class, a
correlation coefficient of 0.5 is used. Assets with the same
underliers and futures with the same root symbol are considered
highly/perfected correlated.
[0104] The system preferably first uses Underlier Group Market
Value Risk Arrays to estimate standard deviations by applying the
following formula, where P(x) is the probability of value x and u
is the arithmetic average:
.sigma.= {square root over
(.SIGMA.P(x.sub.i)(x.sub.i-u).sup.2)}{square root over
(.SIGMA.P(x.sub.i)(x.sub.i-u).sup.2)}= {square root over
(.SIGMA.P(x.sub.i)x.sub.i.sup.2-u.sup.2)}
[0105] Since we assume that the portfolio is diversified, market
value of the portfolio, as the sum of market values of all
underlier groups, is normally distributed. Therefore, the standard
deviation of the portfolio market value is calculated as follows,
where .delta..sub.i is the standard deviation of an underlier group
market value and p.sub.ij is the correlation coefficient between
underlier group i and j:
.sigma. = i = 1 n j = 1 n .sigma. i .sigma. j .rho. ij
##EQU00008##
[0106] Thus, VaR values are computed as follows based on an
assumption of normal distribution of the portfolio market
value:
VaR 1 Day 95%=1.65.sigma.
VaR 1 Day 99%=2.33.sigma.
[0107] VaR 10 Day 95%=1.65 {square root over (10)}.sigma.
Val 10 Day 99%=23 {square root over (10)}.sigma.
[0108] To assist users in comparing portfolio risk levels, the
system also provides VaR as a percentage of the portfolio net asset
value, which includes cash equivalents in the portfolio.
[0109] Reporting
[0110] Although VaR calculation is a complex process, the reporting
of VaR preferably is based on simple spreadsheet-like data grids.
Users have the freedom to arbitrarily define portfolios, choose VaR
measure sets, and navigate through the reporting hierarchy.
[0111] Arbitrary Portfolio Definition
[0112] A portfolio, generally defined, is a set of investment
assets. To assist the user with efficiently creating specific
portfolios, the Risk Manager preferably provides a Portfolio
Manager feature. It allows users to arbitrarily create portfolios
that tie into a Firm-Bank-Branch-Customer-Deposit hierarchy. It
also offers the flexibility to add a user's own positions, which
also can be used as a tool to analyze what-if scenarios associated
with VaR reporting. A VaR report may contain multiple portfolios,
allowing users to compare risk exposures on different portfolios or
to create hypothetical portfolios to assess impact of certain
trading activities.
[0113] Reporting Hierarchy
[0114] A VaR report has three levels: portfolio, underlier group,
and position. The report is presented in a spreadsheet format,
while users can navigate through the reporting hierarchy. More
details on a preferred GUI are provided below.
[0115] Market Efficiency and Normality of Rate of Return
[0116] Certain levels of market efficiency are assumed in the
design philosophy.
[0117] 1. It is assumed that the fundamental securities, such as
stocks, bonds, foreign exchanges, and futures exhibit a random
walk, with a short-term growth expectation of 0. Thus, prices of
these fundamental securities exhibit a log normal (delta-normal)
distribution.
[0118] 2. It is assumed that the price movement is serial
independent in the short-term (i.e., the time autocorrelation
approaches 0). Therefore, the rate of return on these securities
are independently identically normally distributed (iind).
[0119] 3. It is assumed that the prices of derivatives fully
capture market expectation on holding cost (risk free interest
rates), underlier volatility during the holding period, and
rationality of time premium (no early exercise). Thus, a
Black-Scholes model is valid for computing theoretical values of
derivatives.
[0120] All of the above assumptions are commonly made by industry
practitioners and academia.
[0121] Square-Root T Rule
[0122] The term " {square root over (T)}" rule refers to the
following: Given .delta. as the standard deviation of an identical
independent random variable, the sum of the T experiments have a
standard deviation of {square root over (T)}.sigma.. In a time
series, if .sigma. is the standard deviation for one period, and
assuming iid, {square root over (T)}.sigma. is the standard
deviation of T periods.
[0123] The {square root over (T)} rule is preferably used in
several ways in Risk Manager. It is used to estimate daily
volatility intraday. For instance, for an 8 hour trading day, if on
hour 5 we observe a volatility of V, we project the whole day
volatility to be {square root over (8/5)}V. When we report the
annualized volatility, we use {square root over (225)}V, where V is
the daily volatility (there are typically 225 trading days per
year). And finally, we use {square root over (10)}V as 10 day
volatility for VaR calculations, and to estimate 10 day standard
deviation of portfolio market value, where V is the daily
volatility and .delta. is the daily standard deviation of portfolio
market value.
[0124] Short-term time series for financial securities have a very
low autocorrelation. Thus, the {square root over (T)} rule provides
an excellent approximation. However, long-term autocorrelations of
time series on financial securities are much larger. Therefore, a
{square root over (T)} rule may result in significant error for a
long time horizon.
[0125] The annualized volatility {square root over (225)}V does not
suffer from such problems because the V we use is a square average
of 10-15 trading days. A horizon of 20-30 time periods is
considered a short-term time series.
[0126] Correlation of Assets
[0127] The Risk Manager system preferably does not carry
correlation values for any asset classes. The correlation
computation is purely rule based.
[0128] 1. Derivatives with the same underlier and the underlier
itself are perfectly correlated. This is the basis of Risk Array
computation. This assumption is made due to the fact that an
underlier's price movement is the dominant factor for its
derivative's price movement. This is a strong assumption, however,
since in reality derivative prices are also influenced by interest
rates, expectation of volatility, and sometimes, liquidity.
[0129] 2. Correlation coefficients between securities in the same
asset (class, i.e., stock vs. stock and bond vs. bond), are
estimated at 0.5. Studies show that the vast majority of the
correlation is between 0.4 and 0.7. They also show that the market
value standard deviation of a moderately diversified portfolio is
not very sensitive to the correlation coefficients, especially when
a portfolio is reasonably diversified. In an embodiment, this
strong assumption is made to maintain a reasonable level of system
performance. Those skilled in the art will recognize that the
invention is not limited to a correlation of 0.5. Indeed, one could
use a correlation coefficient matrix without deviating from the
present invention or the scope of the appended claims.
[0130] 3. Correlation coefficients between futures contracts with
the same root are assumed to be 0.95. Futures contract value is
influenced by both its underlying commodity and interest rates, but
the commodity value dominates the influence. The forward interest
rates between the holding periods of two futures contract also
influence the prices. The strong assumption is made that underlying
commodity price movement has 95% of the influence on a futures
contract.
[0131] 4. Zero correlation is assumed for securities in different
asset classes. For most cases, this assumption is reasonable.
However, in some cases there is a high correlation (for instance,
an S&P Mini futures contract has a very high correlation with
an S&P based index fund and statistically significant
correlation with any stocks). Preferably, Risk Manager does not
recognize such correlations and will treat them as independent
assets.
[0132] Diversification of Portfolio
[0133] Most of the strong assumptions made in Risk Manager's VaR
calculation are insignificant in a fairly diversified portfolio. A
portfolio is more diversified when: [0134] Investments are in
multiple securities [0135] Investment are in multiple industry
sectors [0136] Investment are in multiple asset classes [0137]
Investments include both long and short positions [0138]
Investments include futures or derivatives for hedging purposes
[0139] In most cases, if the portfolio holds positions in more than
8 non-correlated sectors, the portfolio is considered moderately
diversified.
[0140] If, the portfolio is less than moderately diversified,
underlier groups within the portfolio should be individually
examined. The underlier groups that create the most VaR within the
portfolio should be scrutinized. We recommend against the use of
portfolio VaR on a non-diversified portfolio to assess overall
risk.
[0141] The following description "Credit Risk Based Margin in
RealTick" describes a software component which preferably is part
of the server side (RiskEngine) of the Risk Analyzer. RealTick may
use it to conduct real-time pre-trade margin calculation on the
user's trading account. RiskEngine preferably uses it to
periodically compute margin requirements on over 20,000 trading
accounts and flags risky accounts based on the calculations.
[0142] An embodiment comprises a margin engine with the following
characteristics:
[0143] 1. Pre-trade: blocks risky behavior preemptively.
[0144] 2. Real-time: uses real-time data to assess risk.
[0145] 3. Portfolio-based: examines the entire portfolio (account)
to determine credit worthiness and margin requirements.
[0146] 4. Cross security types: handles portfolios with mixed
investment vehicles (cash equivalents, stocks, indexes, mutual
funds, bonds, futures, options, FX, etc.).
[0147] 5. Currency aware: margins securities traded in different
exchanges/currencies.
[0148] Risk-Based Margining
[0149] Measures
[0150] The preferred measure used for risk-based margining is
real-time Buying Power: [0151] Buying Power=Net Liquidation
Value-Margin Requirements [0152] Net Liquidation Value=Cash Trade
Day Balance+Net Market Value for Investments [0153] Margin
Requirements=Initial Margin Requirements for Day
Positions+Maintenance Margin Requirements for Overnight
Positions
[0154] Real-time Net Liquidation Value, portfolio Margin
Requirements, portfolio Buying Power, and incremental Margin
Requirements are computed for incoming orders. An embodiment will
report an account's real-time net liquidation value
(RISK_NET_IQ), real-time margin requirements on current positions
(RISK_MARGIN_REQ), real-time margin requirement including pending
orders (RISK_MARGIN_REQ_PENDING), and real-time buying power
(RISK_EXCESS_EQUITY) via an Account Summary screen. Should a trade
be rejected due to insufficient buying power, a message box will be
displayed to notify a user of the trade rejection.
[0155] Margin Rules
[0156] Account Manager Pro (AMPro) and Web Account Manager (WAM)
provide GUI and batch update methods to set up margin rules for
their accounts. See AMPro & WAM User Manuals. Note that a
separate feature, Suitability Rules, such as not allowing short,
naked, etc. for specific accounts, supersedes margin rules.
[0157] Currently, AMPro is used by TAL supporting staff, while WAM
is used by broker dealers. The functionality of the two
applications is very similar, but there are subtle differences.
Certain intraday changes to the margin rules must be conducted by
TAL staff upon request. Broker dealers must work with TAL staff to
determine proper protocols and procedures to handle such cases.
[0158] An embodiment obtains the margin rule setup from AMPro and
pass it on to a Margin Engine to compute appropriate margin
requirements based on these rules.
[0159] The margin rule features preferably are as follows:
[0160] 1. Group rules in Margin Rule Sets and assign them to
accounts.
[0161] 2. Can be based on security types.
[0162] 3. Can be based on symbols.
[0163] 4. Can be based on symbol wildcard pattern matching: /ESH5
and /ESM5 can both match to /ES*, for example.
[0164] 5. For equities and futures: initial long, initial short,
maintenance long, maintenance short and cut-off price for
marginable stock.
[0165] 6. For municipal and corporate bonds: market value percent
and face value percent; for government bonds: long-term bond
percent and short term percent.
[0166] 7. For options: underlier percent, and underlier minimum
percent for deeply out of money options.
[0167] 8. Each margin rule can be a percentage of the market value
or a fixed amount.
[0168] 9. Optional enhancement: currency (may be overwritten by
quote currency).
[0169] Margin Requirements
[0170] The following investment types preferably are supported for
margining:
[0171] Cash Equivalents:
[0172] 1. Money market funds--typically treated as cash.
[0173] 2. Foreign Exchange (spot)--typically 2-10% of margin.
[0174] Bonds:
[0175] 1. Government bonds--typically 5% of face for short term and
10% for long term.
[0176] 2. Municipal bonds--typically 15% of market value or 10% of
face, whichever is higher.
[0177] 3. Corporate bonds--typically 30% of market value or 15% of
face, whichever is higher.
[0178] Equities:
[0179] 1. Stocks--typically $5+ to be marginable, long/short
initial/maintenance are house specific.
[0180] 2. Mutual funds--all marginable and rules are similar to
stocks.
[0181] 3. Indexes--similar to mutual funds.
[0182] Derivatives:
[0183] 1. Stock options--long positions are not marginable, 25%
underlier value for naked, 10% for deeply out of money naked, and
margin breaks for recognized hedging positions.
[0184] Futures:
[0185] 1. Futures--fixed dollar amount for depending on the
contract; calendar and inter-commodity spreads receive margin
credits depending on house rules.
[0186] Optional:
[0187] 1. Other: Margined at 100% net liquidation value--default
setting.
[0188] Cash and Equivalents
[0189] Cash balance (trade day balance) includes beginning cash
position plus all payment and proceeds from longing and shorting
securities. No margin is accessed on cash.
[0190] Money market funds and FX spot are margined based on margin
rules similar to equity.
[0191] Equity Positions
[0192] Long Stock
[0193] Stock is purchased and cash is paid. Stocks with prices
greater than or equal to $5 are marginable.
[0194] Examples
[0195] 1. Long marginable equity position (30% margin
requirement)
Long 100 C at $35; MMR=100*35*30%=1050
[0196] 2. Long non-marginable equity position (100% margin
requirement)
Long 100 LU at $4; MMR=400
[0197] Marginable stock positions preferably are used as collateral
to obtain margin loans. Non-marginable stock positions have no
impact on account margin. The $5 cutoff on marginable stocks is
configurable in AMPro. The 30% margin requirement in this example
is configurable as an initial margin requirement or a maintenance
margin requirement depending on whether it is an overnight position
or a day position.
[0198] AMPro's margin rule management capability can handle more
complex scenarios. A list of stocks may be created as the
hard-to-borrow list so that they are not marginable. Although
embodiments can handle all of the scenarios, a broker-dealer may
need to create such a setup.
[0199] Short Stock
[0200] Stock is sold short and cash is received.
[0201] Examples
[0202] 1. Short marginable equity position (30% margin
requirement)
Sell short 100 C at $35; MMR=100*35*30%=1050
[0203] 2. Short non-marginable equity position (100% margin
requirement)
Sell short 100 LU at $4; MMR=400
[0204] Cash proceeds are included in the cash balance, which in
turn provides buying power, and market value is negative, which
reduces buying power. Taking advantage of this offset, margin
requirements on these positions are computed consistent with long
positions. By the same token, the $5 cutoff and the 30% initial or
maintenance margin requirements numbers are configurable in
AMPro.
[0205] Stock Options
[0206] Risk-based margin for options preferably is designed to
follow the following philosophy:
[0207] 1. Option price (full premium) has two components: intrinsic
value (in-the-money)+time premium.
[0208] 2. Time premium is always required at 100%, as it will go
away with time and volatility change.
[0209] 3. If in-the-money, the intrinsic value provides a matching
stock position with additional value for margining.
[0210] 4. In case of an option-to-option matching, additional
margin is required for maximum loss that may occur at the
expiration date, based on an option payoff matrix.
[0211] 5. Unlike SPAN or TIMS, the system of an embodiment does not
automatically include stock volatility into its computation.
However, broker dealer may research stocks on their own and set
margin requirement based on a pattern matching on option roots.
[0212] Hybrid Stock Options
[0213] Hybrid stock options require the delivery of cash in
addition to shares of stock on the settlement date. Corporate
actions, including mergers & acquisitions and stock splits,
give rise to these derivatives. To effectively calculate margin
requirements on hybrid equity options, an effective strike price is
required. The effective strike price is calculated as follows:
Effective Strike Price=(Strike-Cash Settlement)*Basis
Value/Settlement Quantity
[0214] Default values: Cash Settlement (0), Settlement Quantity
(100), and Basis Value (100)
[0215] Example
[0216] Underlying Stock Symbol (Symbol 1): TYC
[0217] Root Symbol (Root): TNY
[0218] Option Symbol (Ticker): TNYGC
[0219] Strike Price (Strike): 15.00
[0220] Cash Settlement per Share (Settlement Cash): 0.19050
[0221] Settlement Quantity/Lot Size (Shares): 31
[0222] Basis Value: 100
[0223] TNYGC Effective Strike
Price=(15.00-0.19050)*100/31=47.77
[0224] Basis value will not always be 100. For example, a 3-for-2
stock split will change the basis value of the stock option to 150.
(A 2-for-1 stock split will ordinarily increase the number of
outstanding options by 2 and the basis value will remain 100). If
Settlement Quantity=0, then the formula preferably is calculated
without the settlement quantity:
[0225] ** Use Effective Strike Price in All Calculations for
`Strike Price`
[0226] All of the following formulas take into consideration hybrid
options.
[0227] Uncovered (Naked) Option
[0228] Long Option
[0229] An option is purchased and cash, its premium, is paid.
[0230] Example
[0231] 1. Long Call Option
Long 1 C March 35 Call at $2; Margin=2*100=200
[0232] 2. Long Put Option
Long 1 C March 35 Put at $2; Margin=200
[0233] Outright long option positions are margined at 100% of the
premium. No AMPro margin rule is required, but AMPro can overwrite
the built-in rules.
[0234] Uncovered (Naked) Short Options
[0235] An option is sold short and cash, the premium, is
received.
[0236] Examples
[0237] 1. Short Call Option
Short 1 C March 35 Call; Margin=ORQ calculation is specified in the
ORQ Requirement
[0238] 2. Short Put Option
[0239] Short 1 C March 35 Put; Margin=ORQ calculation is specified
in the ORQ Requirement
[0240] Following is a description of the algorithms.
[0241] A) if the options are deeply out-of-money (minimum
requirements):
[0242] 10% of the underline stock plus 100% of premium. For
instance, if C is trading at 20, situation 1) will require $200
plus the time premium
[0243] B) otherwise (normal naked requirements)
[0244] 25% of the underline stock plus 100% of premium. For
instance, if C is trading at 36, in 1) $900 plus the time premium
is required.
[0245] C) for deeply in the money options:
[0246] If the intrinsic value (in-the-money value) is greater than
B, the full premium is required. (Full premium=market
price=In-The-Money+time premium). For instance, if C is trading at
50 while March 35 Call is trading at 16, the full 1600 premium is
required. (Because ITM=15>25%*50.)
[0247] The 10% deeply out-of-money option requirement and the 25%
option requirement are both configurable in AMPro margin rules.
[0248] Covered Option
[0249] Covered Write Call
[0250] Stock is purchased and a call is sold.
[0251] 1. In-the-money short call covered by marginable long
stock.
Short 1 C March 32.5 Call @$3.5; Margin=time premium=100
Long 100 C at $35; Margin=30%*32.5*100=975
[0252] 2. In-the-money short call covered by non-marginable long
stock.
Short 1 LU March 3.5 Call @$1; Margin=time premium=50
Long 100 LU at $4; Margin=100%*3.5*100=350
[0253] 3. Out-of-money short call covered by marginable long
stock.
Short 1 C March 32.5 Call @$1; Margin=time premium=full
premium=100
Long 100 C at $30; Margin30%*30*100=900
[0254] No margin rule is required in AMPro.
[0255] Covered Write Put
[0256] Stock is Sold Short and a Put is Sold.
[0257] 1. In-the-money short put covered by marginable short
stock.
Short 1 C March32.5 Put @$3; Margin=time premium=100
Short 100 C at $30; Margin=30%*32.5*100=975
[0258] 2. In-the-money short put covered by non-marginable short
stock.
Short 1 LU March 4.5 Put @$1; Margin time premium=50
Short 100 LU at $4; Margin=450 (Some house rules require $500 as
minimum)
[0259] 3, 3) Out-of-money short put covered by marginable short
stock.
Short 1 C March32.5 Put @$0,5; Margin=time premium=50
Short 100 C at $35; Margin=30%*35*100=1050
[0260] No margin rule is required in AMPro.
[0261] Synthetic Put (Short Hedge-Married Cull)
[0262] Stock is shorted and a call is purchased to cover the short
stock.
Examples
[0263] 1. Marginable short stock and in-the-money long call.
Short 100 C at $35; Margin=30%*32.5*100=975
Long 1 C March32.5 Call @$3; Margin=time premium=50
[0264] 2. Non-marginable short stock and in-the-money long
call.
Short 100 LU at $4; Margin=100%*3.5*100=350
Long 1 LU March 3.5 Call @$1; Margin=time premium=50
[0265] 3. Short stock and out-of-money long call.
Short 100 C at $30; Margin=900 (as if no cover)
Long 1 C March32.5 Call @$1; Margin=time premium full
premium=100
[0266] In a synthetic put, a long in-the-money option position
provides a margin credit to offset the short stock position's
margin requirements. Therefore, a short stock position's margin
requirement is based on the strike price of the option rather than
the stock's market price. In the meantime, the intrinsic value of
the option provides margin relief to the portfolio. No margin rule
is required in AMPro.
Synthetic Call (long hedge-married put)
[0267] Stock is bought and a put is purchased. No specific
algorithm is required, as this is a non-conventional trading
strategy. The following example is to illustrate an algorithm that
may be used.
[0268] Examples
[0269] 1. Marginable long stock and in-the-money long put.
Long 100 C at $30; Margin=30%*32.5*100=975
Long 1 C March32.5 Put @3; Margin=time premium=50
[0270] 2. Non-marginable long stock and in-the-money long put.
Long 100 LU at $4; Margin=100%*5*100=500
Long 1 LU March 5 Put @$1.5; Margin=50
[0271] 3. Long stock and out-of-money long put.
Long 100 C at $35; Margin=30%*35*100=1050
Long 1 C March 32.5 Put @$0.5; Margin=full premium=50
[0272] In a synthetic call, along in-the-money put position
provides additional margin as it extends the value of the
combination beyond the stock's market value even if the stock price
drops. Therefore, the margin requirements are computed on the
strike price of the option rather than the stock's market price. In
the meantime, the intrinsic value of the option provides margin
relief to the portfolio. No margin rule is required in AMPro.
[0273] Straddle
[0274] Long Straddle
[0275] A put and a call with the same underlying stock, strike
price, and expiration date are bought together. No specific
algorithm is required for this strategy. The long straddle may be
treated as two unrelated long option positions, thus their premium
is traded as the margin requirement.
[0276] Example
[0277] 1. Long put and call.
C trading at 35
Long 1 C March 32.5 Call; Margin=premium
Long 1 C March32.5 Put; Margin=premium
[0278] When the stock stays at 32.5 on expiration, both options
will have a value of 0. Thus, the maximum loss can be their market
value, and the margin requirement should be set so. No margin rule
is required in AMPro.
[0279] Short Straddle
[0280] A put and a call with the same underlying stock, strike
price, and expiration date are sold together.
[0281] Example
[0282] 1. Short put and call
C trading at 35
Short 1 C March 37.5 Call; Margin=0
Short 1 C March 37.5 Put; Margin=ORQ Naked
[0283] The straddle structure guarantees that only one side of the
straddle will have a down side risk at any time. Thus, the margin
requirement is the naked ORQ on the leg that is in the money. No
margin rule is required in AMPro.
[0284] Calendar Spread
[0285] Long Calendar Spread
[0286] An option is sold, and the same type option with the same
underlying symbol, strike price, and LONGER expiration date is
bought.
[0287] 1. Long Call Calendar Spread
Short 1 C March 35 Call; Margin=the difference of the pair's market
value (premium).
Long 1 C June 35 Call; Margin=0
[0288] 2. Long Put Calendar Spread
Short 1 C March 35 Put; Margin=the difference of the pair's market
value (premium).
Long 1 C June 35 Put; Margin=0
[0289] Before the expiration of either option in a calendar spread,
their risk is perfectly hedged. No additional margin is required
for the short. However, the net market value of the pair is
withheld as they may go away with time. After the first option
expires, the account ends up with a long position of the remaining
option and the long side of the premium becomes the margin
requirement.
[0290] Short Calendar Spread
[0291] An option is bought, and the same type of option with the
same underlying symbol, same strike price, and LONGER expiration
date is sold.
[0292] Short Call Calendar Spread
Long 1 C March 35 Call; Margin=0
Short 1 C June 35 Call; Margin=Naked ORQ
[0293] 2. Short Put Calendar Spread
Long 1 C March 35 Put; Margin=0
Short 1 C June 35 Put; Margin=Naked ORQ
[0294] Before the expiration of either option in a calendar spread,
their risk is perfectly hedged. After the first option expires, the
account ends up with an uncovered short position of the remaining
option. Thus, margin must be assessed to address the risk.
[0295] Under the current ORQ Requirement, this type of spread is
NOT recognized as a spread. Thus, the short position is considered
naked, taking the most conservative approach.
[0296] Bear/Bull Spread
[0297] A pair of options of the same type with the same underlying
stock and expiration date are longed and shorted, respectively.
[0298] 1. Bear Call Spread
Long C March 37.5 Call
Short C March 32.5 Call
[0299] 2. Bear Put Spread
Long C March 37.5 Put
Short C March 32.5 Put
[0300] 3. Bull Call Spread
Long C March32.5 Call
Short C March37.5 Call
[0301] 4. Bull Put Spread
Long C March32.5 Put
Short C March37.5 Put
[0302] The maximum loss for the BEAR Spread strategy is the
difference of the strike prices, which is the ceiling for its ORQ.
If the difference between their strike prices is greater than the
ORQ for the naked option, the naked ORQ should be used. There
should be no downside risk for a BULL spread. This part of the
requirement addresses the maximum loss upon expiration.
[0303] In the meantime, the difference in time premium is required
in addition.
[0304] Strangle
[0305] Long Strangle
[0306] A long strangle is an option strategy in which an
out-of-the-money call and an out-of-the-money put of the same month
and stock are purchased. Margin is the full premium paid. No
specific algorithms.
[0307] Short Strangle
[0308] A short strangle is an option strategy in which an
out-of-the-money call and an out-of-the-money put of the same month
and stock are sold. ORQ should be the same as a short straddle.
[0309] Butterfly
[0310] Long Butterfly
[0311] Long butterfly will never lose money upon expiration. Thus,
no additional margin requirement will be accessed. However, it can
and will lose its time premium while it approaches expiration. Thus
the margin requirement is the net sum of time premium of each
position.
[0312] Example
XYZ trading at 49
Long 1XYZ June Call @45 Price=5: Time premium=100
Short 2 XYZ June Call @50 Price=1: Time premium=-100*2=-200
Long 1 XYZ June Call @255 Price=0.5: Time premium=50
Margin Requirement=|100-200+50|=50
Note: Time premium=Full premium (price)-Intrinsic Value
(In-The-Money)
[0313] Short Butterfly
[0314] The margin requirement for a long butterfly is the net
premium paid. The margin requirement for a short butterfly is the
difference of the lower two strikes. The premium received from a
short butterfly may be applied to meet the margin requirement. This
is based on the following payoff table (Table 5), which assumes
that the left wing has the lowest strike price.
TABLE-US-00005 TABLE 5 Payoff Table (Stock Price) Long Butterfly
Short Butterfly Stock Price <= Left Strike 0 0 Left Strike <
Stock Price <= Stock Price - Left Left Strike - Stock Body
Strike Strike (+) Price (-) Body Strike < Stock Price <=
Right Strike - Stock Stock Price - Right Right Strike Price (+)
Strike (-) Right Strike < Stock Price 0 0
[0315] It is clear that the maximum loss on a short butterfly is
the difference of the lower two strike prices (i.e. the margin
requirement for short butterfly). In addition, the net different of
time premiums is required for both long and short butterflies.
[0316] Example
XYZ trading at 49
Short 1XYZ June Call @45 Price=5: Time premium=100
Long 2 XYZ June Call @50 Price=1: Time premium=-100*2=-200
Short 1 XYZ June Call @55 Price=0.5: Time premium 50
Margin Requirement=|100-200+50|+(50-45)*100=550
[0317] Note: Time premium=Full premium (price)-Intrinsic Value
(In-The-Money)
[0318] Asymmetric Butterfly
[0319] An asymmetric butterfly has two different sized wings,
balanced by the opposite difference in strike prices. This strategy
has no specific algorithm and is processed as two separate spreads.
An example:
Long 5 Jun 40 Call of XYZ
Short 10 Jun 45 Call of XYZ
Long 10 Jun 47.5 Call of XYZ
[0320] Other Spreads
[0321] If a spread is neither a calendar spread nor a bull/bear
spread, the following generic formula applies:
[0322] Formula A:
|Short Strike Price-Long Strike Price|.times.Short Settlement
Quantity.times.Short Number of Contracts
[0323] Formula B:
Long Market Value of the Spread-Short Market Value of the
Spread
[0324] The margin requirement for the spread is the greater of the
two.
[0325] Collar
[0326] A collar is an option strategy in which stock is purchased,
an out-of-the-money call is sold, and an out-of-the-money put is
purchased. No specific algorithm for this strategy. All positions
are processed independently.
[0327] Conversion
[0328] Treated as covered call.
[0329] Reversal
[0330] Treated as naked put.
[0331] Futures
[0332] Futures margin requirements are governed by two sets of
setup options: AMPro margin requirements for outright, and
file-based parameters for spreads.
[0333] All margin requirement features in AMPro are available for
futures. As a general practice, single stock futures requirements
typically are set up as a percentage of the contract value, say
20%. Other future contracts typically are set up as fixed amount.
In many cases, a regular expression pattern matching can be applied
to the symbols. These rules are house rules. If the broker-dealer
wants to use exchange default rules, they must request TAL to set
up the house rule identical to exchange rules.
[0334] A file containing future spread match parameters is required
to process future spreads. Following is an example of a typical
file:
TABLE-US-00006 TABLE 6 Root1 Root2 Months Ratio Release Remark /ES
3; 6; 9; 12 95 E-mini S&P 500 /NQ 3; 6; 9; 12 95 E-mini
NASDAQ-100 /GE 85 Eurodollar /GLB 85 1 Month LIBOR /S 1; 3; 5; 7;
8; 9; 11 80 Soybeans /BO 1; 3; 5; 7; 8; 9; 10; 12 75 Soybean Oil
/FDAX.EUX 95 DAX Index /L.LIF 90 Short Sterling /ES /NQ 0.5 85 /ES
/ER2 1 85 /NQ /EMD 1.5 85 /NQ /ER2 2 85 /SM /BO 0.6667 50 /FESX.EUX
/FDAX.EUX 2.5 70 /L.LIF /I.LIF 1.3333 50 /FGBL.EUX /FGBM.EUX 1 90
Root1: Required field. For calendar spreads, it is the root symbol
for both legs. For inter-commodity spreads, it is the root of the
first leg. Root2: Required field for inter-commodity spread. It is
the root for the second leg of an inter-commodity spread. Blank for
calendar spread. Months: Blank for inter-commodity spreads. For
calendar spreads, if left blank, it means all months. It is a
semicolon-delimited list of month numbers that can form a calendar
spread. Ratio: If left blank, it means a default of 1. This is the
ratio between the contract numbers of the two legs in a spread.
Release: Required field 0-100. This is the percentage of outright
margin requirement to credit back when a spread is determined.
Remark: Optional field that can contain any string for
documentation purposes.
[0335] Outright Futures
[0336] An outright position can be long or short on a number of
contracts. AMPro contains a set of rules (Margin Rule Set)
governing the margining. The lookup algorithm is detailed in the
AMPro User Manual. Here is a summary:
[0337] Example: Long 10/ESH5
[0338] 1. The rule set must set to security type 3--Futures,
and,
[0339] 2. Exact match: If the rule with/ESH5 tag is found, it will
be applied. Otherwise,
[0340] 3. Commodity pattern: If the rule with/ES* tag is found, it
will be applied. Otherwise,
[0341] 4. Date pattern: If the rule with/*H5 tag is found, it will
be applied. Otherwise,
[0342] 5. Blanket pattern: If the rule with/* tag is found, it will
be applied. Otherwise,
[0343] 6. Full contract value will be charged.
[0344] If a rule is found, it will say if it is a fixed amount or a
percentage of the contract value. The outright requirement will
thus be computed.
[0345] Calendar Spreads
[0346] To illustrate the algorithm, the following positions are
assumed:
[0347] Long 3/ESH5
[0348] Short 2/ESM5
[0349] Assume the margin rule says: IMR for/ESH5 is 1000. IMR
for/ESM5 is 1100.
[0350] Calendar spread rule found:
TABLE-US-00007 TABLE 7 /ES 3; 6; 9; 12 95 E-mini S&P 500
[0351] Thus, the positions are computed as follows:
Long 2/ESH5: spread margin=1000*2*(1-95%)=100
Short 2/ESM5: spread margin=1100*2*(1-95%)=110
Long 1/ESH5: outright margin=1000
Total margin=1210
[0352] Inter-Commodity Spreads
[0353] To illustrate the algorithm, the following positions are
assumed:
[0354] Long 8/ESH5
[0355] Short 10/NQH5
[0356] Assume the margin rule says: IMR for/ESH5 is 1000. IMR
for/NQH5 is 600.
[0357] Inter-commodity spread rule found:
TABLE-US-00008 TABLE 8 /ES /NQ 0.5 85
[0358] Thus, the positions are computed as follows:
Long 5/ESH5: spread margin=1000*5*(1-85%)=750
Short 10/NQH5: spread margin=600*10*(1-85%)=900
Long 3/ESH5: outright margin=3000
Total margin=4650
[0359] Other Business Rules
[0360] Pending Orders
[0361] Pending orders are orders that are submitted to the trading
system but not yet filled. Limited orders are the most common form
of pending orders. The following example illustrates how the system
deals with pending orders.
Cash 3200
Long 100 XYZ @100-Limit price=98: Margin=30%*98*100=2940
Buying power=3200-2940=260 OK
[0362] If a pending order is (1) an stock, bond, mutual fund, and
index, and (2) the limited price is within 5% of the market price,
it can be used to cover other derivative positions. For
instance,
Cash 5000
Long 100 XYZ @100-Limit price=98: Margin=30%*98*100=2940
Short 1 XYZ Call @100-market order at $2: Covered margin=200
Buying power=3200-2940-200=60
[0363] If the pending XYZ buy I executed, the following positions
are safe.
Cash -6600
Long 100 XYZ @98: Margin=30%*98*100=2940
Short 1 XYZ Call @100 price=$2: Covered margin=200
Buying power=(-6600+9800)-2940-200=60 OK
[0364] If the pending XYZ buy is not executed due to cancellation,
the following positions also are safe:
Cash 3200
XYZ trading at 100
Short 1 XYZ Call @100 price=$2: Naked
margin=25%*100*100+200=2700
Buying power=3200-2700=500 OK
[0365] If the pending order is not an equity order, or the limited
price is more than 5% away from the market price, it cannot be used
to cover other positions. The following two examples illustrate the
scenarios:
Cash 2000
Long 100 XYZ @100-Limit price=50: Margin=30%*50*100=1500
Short 1 XYZ Call @100-market order at $2
[0366] If a cover is allowed, the short call will be charged 200
margin and the trade would go through. Then, after the buy XYZ
order is canceled, we have:
Cash 2000
XYZ trading at 100
Short 1 XYZ Call @100 price=$2: Naked
margin=25%*100*100+200=2700
Buying power=2000-2700=-700 Bad--margin call
[0367] Therefore, the following computation is required so that the
short option order is rejected:
Cash 2000
Long 100 XYZ @100-Limit price=50: Margin=30%*50*100=1500
Short 1 XYZ Call @100-market order at $2: Naked margin=2700
Buying power=2000-1500-2700=-2200 Bad--reject
Similarly, the following example illustrates that pending option
trades cannot be considered a cover:
Cash 2000
Long 1 XYZ Call@100-limit price=$1, market price=$2: Margin=100
Short 1 XYZ Call @100-market order at $2: Naked margin=2700
Buying power=2000-100-2700=-800 Bad--reject
[0368] The reason for not allowing the long call limited order to
be a cover is apparent. If it is canceled in the future, the
account will not be in good standing.
[0369] There is one exception to the above rules. If an order is
determined to liquidate an existing position, it will have a zero
margin charge.
[0370] Compound Orders
[0371] The following four types of compound orders are supported in
an embodiment:
[0372] 1. Order Cancel Order: A group of orders are submitted
together; one of the orders is executed, and the rest of the orders
are canceled. Margin requirement for this type of compound is the
greatest margin requirement of individual orders.
[0373] 2. Order Trigger Order (Sequential Order): A group of orders
is submitted and one order goes live. When the live order is
executed, it triggers another order, etc., until all orders are
executed. User can cancel any pending order before it is executed.
The margin requirement for this order is the greatest margin
requirement for the sequence of executions.
[0374] 3. All or None: A group of orders is submitted. The lead
order is a limit or market order, and all other orders are
conditional market orders that are triggered only when the lead
order is executed. Therefore, either all orders are executed or
none of them are executed. The margin requirement of this type is
the overall margin requirement for the group.
[0375] 4. Basket Orders. A group of independent orders is
submitted. Margin is computed as if they are submitted one by one.
However, if the account buying power is insufficient, the entire
basket is rejected. An embodiment will not allow partial submission
of the basket.
[0376] Currency Conversion
[0377] Currency conversion is fully handled. Each user account has
a Home Currency. Each security has a Quoted Currency on its
real-time price. In an embodiment, all other currency
specifications, especially in the AMPro margin rules, are
overwritten with these two currency figures. Thus, all margin and
risk calculations are conducted on the Home Currency, while an
exchange rate is applied if the security is quoted in a different
currency. The exchange rates preferably applied are overnight
exchange rates. However, this may be modified to, e.g., 10 minute
refreshes.
[0378] Optimization
[0379] An objective of risk-based margin is to achieve the lowest
possible margin requirement for a portfolio. While every effort is
made, there is no way to guarantee that the optimum is achieved. In
complex cases, to achieve the optimal low margin requirements
requires significant computing power--not feasible for a real-time
application. However, an embodiment should achieve an optimal
margin requirement in most cases, and "good enough" requirements in
highly complex cases. The following is the high-level pseudo
code.
[0380] 1. Divide a portfolio into two parts: (a) stock and option
part (b) futures part.
[0381] 2. Divide stock and option part into groups by
underlier.
[0382] 3. Each position in the underlier group is matched with
other position to look for match for
[0383] a) Box spreads
[0384] b) Long butterfly
[0385] c) Short butterfly
[0386] d) Spreads
[0387] e) Covers
[0388] f) Straddles
[0389] g) Hedges
[0390] h) Nakeds
[0391] 4. Divide future positions into groups by root.
[0392] 5. For each root group, match one position to another to
form Calendar Spreads.
[0393] 6. Match each futures position to another to form
Inter-Commodity Spreads.
[0394] 7. Sum up margins.
[0395] Portfolio Manager
[0396] Portfolio Manager is a GUI dialog box used in an embodiment
(Risk Analyzer). It allows a user to define portfolios of
investment positions by arbitrarily pulling together accounts
predefined in an account hierarchy. RiskAnalyzer can then produce
Value-at-Risk reports using the portfolio definitions.
[0397] Because a typical account hierarchy contains thousands of
items, a challenge is to allow a user to select and remove many
items from a large and complex tree structure without getting lost
through browsing.
[0398] The Portfolio Manager preferably comprises two main
components: (1) a tree control with checkboxes for an entire
account hierarchy; and (2) a grid control of applicable hierarchy
properties for selected items. When an item is selected in the tree
control, it is added to the grid with its hierarchical properties.
When an item is selected in the grid, an instant search is
performed on the tree control and a corresponding item is displayed
to user.
[0399] The following is an illustrative example:
[0400] (1) Left pane 110 contains a large tree structure with many
levels. See FIG. 1.
[0401] (2) While navigating within the tree in left pane 110, user
selects items via the checkboxes 120. Each selected item is added
to the right side grid 130 for easy viewing. User can easily lose
his view of all checked items on the left because the tree
structure is so large. See FIG. 2.
[0402] 3) Each time the user clicks a row on the right grid 130,
the software will instantly find the corresponding item on the tree
structure in left pane 110 and take the user there. This way, the
user doesn't need to browse through the large tree structure again
to find selected items. See FIG. 3.
[0403] It will be appreciated that the present invention has been
described by way of example only and with reference to the
accompanying drawings, and that improvements and modifications may
be made to the invention without departing from the scope or spirit
thereof.
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