WO1998054666A1 - Computer-implemented method and apparatus for portfolio compression - Google Patents
Computer-implemented method and apparatus for portfolio compression Download PDFInfo
- Publication number
- WO1998054666A1 WO1998054666A1 PCT/CA1998/000519 CA9800519W WO9854666A1 WO 1998054666 A1 WO1998054666 A1 WO 1998054666A1 CA 9800519 W CA9800519 W CA 9800519W WO 9854666 A1 WO9854666 A1 WO 9854666A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- compressed
- portfolio
- compression
- instruments
- financial instruments
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Definitions
- the present invention relates generally to the field of data processing, and in particular to a computer-implemented method and apparatus for compressing a portfolio of financial instruments to enable, for example, more efficient risk management processing than is otherwise achievable with an uncompressed portfolio.
- Risk management is a critical task for any manager of a portfolio of market instruments, and accurate and efficient risk measurement is at the core of any sound enterprise-wide risk management strategy. Given the relatively-complex mathematical calculations necessary to accurately measure risk, financial institutions generally use some form of computer-implemented "risk management engine.” As explained below, however, existing risk management engines may be insufficient to adequately deal with the large, complex portfolios maintained by many financial institutions.
- VaR Value-at-Risk
- VaR gives the maximum level of losses that a portfolio could incur, over some predetermined period of time, with a high degree of confidence. For regulatory purposes, for example, the time period may be set to 10 days, and the one-sided confidence interval to 99%.
- VaR can be expressed as a multiple of the portfolio standard deviation in some simple cases, such as when portfolios are normally distributed, this generally is not the case.
- VaR estimate of a large, complex portfolio over several thousand Monte Carlo scenarios could easily take several hours, if not days, for a top-of-the-line work station. Indeed, even the simple task of loading and storing large portfolios can be onerous and time consuming.
- a subject portfolio also called the "target” portfolio
- a linear portfolio might comprise 70-95% of the total portfolio positions.
- the risks embedded in option positions may be substantial.
- the next step in such an approach is to measure the risk of these subportfolios separately.
- linear subportfolio For the linear subportfolio, one could apply, for example, a "delta-normal methodology" such as that described in the above-cited RiskMetricsTM Technical Document. By assuming linearity of the subportfolio and normal distributions, this analytical method has moderate computational requirements. For the options, some basic, perhaps limited, simulation can be applied. Finally, an estimate of the risk of the target portfolio is taken as the sum of the individual subportfolio risks.
- the present invention is generally directed at providing improved tools for risk management of large and/or complex portfolios of financial instruments.
- a "compressed portfolio" is generated for a given target portfolio of financial instruments.
- the compressed portfolio is a relatively smaller and/or simpler portfolio that closely mimics the behavior of the target portfolio, but that requires orders of magnitude less computer memory to store and orders of magnitude less computational time to value.
- the compressed portfolio can be used, for example, for risk measurement analyses instead of the target portfolio, thereby providing substantial improvements in computer resource usage with little or no reduction in accuracy.
- a computer-implemented method for compressing a portfolio of financial instruments is provided. Financial instruments to be compressed are identified, and a compressed subportfolio corresponding to the set of financial instruments to be compressed is generated. The compressed subportfolio and any non- compressed financial instruments are then combined into a compressed portfolio.
- Fig. 1 is a block diagram illustrating a computer-implemented apparatus for portfolio compression in accordance with an embodiment of the present invention.
- Fig. 2 is a block diagram illustrating a particular implementation of a compression engine in accordance with the embodiment shown in Fig. 1.
- Fig. 3 is a flow diagram illustrating a general method for portfolio compression in accordance with an embodiment of the present invention.
- Fig. 4 is a flow diagram illustrating a method for portfolio compression in accordance with another embodiment of the present invention.
- Fig. 5 sets forth a notation convention applicable to a scenario-based compression technique that can be applied by an apparatus configured in accordance with the embodiments illustrated in Figs. 1-4.
- Fig. 6 illustrates an example of a set of cashflows produced by application of delta bucketing compression according to an embodiment of the present invention.
- Fig. 7 illustrates an example of a set of cashflows produced by application of analytical compression according to an embodiment of the present invention.
- Embodiments of the present invention are directed to providing advanced portfolio tools for reducing the substantial computational requirements of modern portfolio management.
- a "compressed portfolio” is generated for a target portfolio, and risk measurement calculations are then performed on the compressed portfolio.
- the term "compressed portfolio” contemplates a relatively small and/or simple portfolio that behaves almost identically to an original large and/or complex portfolio, but that requires orders of magnitude less computer memory to store and orders of magnitude less computational time to value.
- a compressed portfolio need not mimic an original portfolio forever and under every possible state of the world, but rather only during a specified period of interest and over a range that certain specified market factors may take during that period.
- compressed portfolios are also powerful tools enabling risk managers to better understand and actively manage their portfolios. By representing portfolio behavior in simpler terms, one can gain insight into the exposures of large portfolios and identify possible remedial actions.
- Embodiments of the present invention may be implemented, for example, using a so-called “compression engine.”
- a compression engine Given a target portfolio of financial instruments, a compression engine provides a means for creating a compressed portfolio consisting of simpler and/or fewer instruments that will replicate the behavior of the target portfolio over a range of possible market outcomes for a pre-defined period in the future.
- the computational effort to perform a risk analysis of the compressed portfolio is substantially less than that of the target portfolio.
- the compressed portfolio provides a better understanding of the market risks facing the holder.
- a general goal of such a compression engine is to preprocess a portfolio before attempting to simulate the portfolio's performance over a range of possible market scenarios.
- the product of this preprocessing stage is generally a smaller and simpler portfolio that is orders of magnitude faster to simulate, but that behaves almost identically to the original portfolio and contains the same risk.
- a compression engine in accordance with embodiments of the present invention may be used in a manner similar to the technique described above whereby an estimate of a portfolio's risk is determined by dividing the target portfolio and applying different techniques to each subportfolio.
- a principal difference, however, is that the portfolio compression techniques described herein make it possible to avoid the problematic last step where the total risk is derived simply by summing the risks of the respective subportfohos.
- the VaR of the target portfolio can be obtained by doing a single simulation of the "total compressed portfolio,” given by the sum of the individual compressed portfolios.
- portfolio compression techniques such as those described herein fully capture portfolio diversification, hedging and correlations among individual positions.
- a compression engine in accordance with embodiments of the present invention, it is possible to implement an embodiment (described further below) with a compression engine that implements two different methodologies for compressing portfolios: analytical compression and scenario-based compression.
- Analytical compression exploits the analytical properties of cashflow portfolios. This technique is perhaps best suited for fixed income portfolios without optionality, although it may be generalized to portfolios with options.
- Scenario-based compression on the other hand, is based on stochastic optimization techniques and is best suited for portfolios with options.
- the compression engine offers a robust implementation capable of handling multiple types of portfolios.
- the extensibility of such compression engines allows the ready implementation of other compression methodologies.
- Analytical compression is a practical and powerful methodology for the approximate representation of large cashflow portfolios that exploits their mathematical properties.
- the rationale behind analytical compression is relatively straight- forward.
- Risk factor space refers to the space of all risk factors including, for example, interest rates, foreign exchange rates, volatilities, index levels, and so on. Thereafter, the portfolio is fully valued under all of those scenarios.
- the analyst is interested in is the portfolio's distribution (i.e., output).
- making use of the properties of the portfolio before sampling results in more efficient calculations.
- the results of analytical compression are (1) a new, compressed representation of a target portfolio by a small number of simple instruments (e.g., bonds) that depend on a new, smaller set of risk factors, and (2) an exact process that describes the behavior of the new underlying risk factors as a function of the original ones.
- simple instruments e.g., bonds
- an exact process that describes the behavior of the new underlying risk factors as a function of the original ones.
- scenario-based compression is an especially effective technique for compressing portfolios that contain options.
- the technique draws on stochastic optimization methods called “scenario optimization,” described in Ron Dembo, Optimal Portfolio Replication, Algorithmics Technical Paper No. 95-01 (1997), and "optimal portfolio replication,” described in Ron Dembo and Dan Rosen, The Practice of Portfolio Replication, Algorithmics Technical Paper No. 98-01 (1997).
- Analytical compression may be implemented, for example, using embodiments of the inventions described in U.S. Patent No. 5,148,365, issued on September 15, 1992 and titled “Scenario Optimization," and recently- allowed U.S. Patent Application No. 08/866,303 titled “Method and Apparatus for Optimal Portfolio Replication.” The disclosures of these four references are incorporated herein by reference.
- scenario optimization aims to find the best possible "replicating portfolio” that replicates the behavior of the target portfolio over a range of discrete market outcomes, or scenarios.
- the replicating portfolio does not necessarily have to be made up of market-traded instruments, as long as one has good models to generate "fair market prices" for the replicating instruments.
- the mathematical underpinnings of scenario-based compression are described below with reference to particular embodiments of the present invention.
- a computer-implemented apparatus 10 for performing portfolio compression.
- Computer-implemented apparatus 10 may run under any suitable architecture providing sufficient computing power and storage capacity. It may operate as a standalone system, or may be integrated, for example, as part of a larger system of financial analysis tools.
- computer-implemented apparatus 10 includes a processor 12 for performing logical and analytical calculations.
- Processor 12 may comprise, for example, a central processing unit (CPU) of a personal computer, but may alternatively include any other type of computer-based processor capable of performing such functions.
- processor 12 could operate on a "UNIX” brand or other "POSIX"-compatible platform under "MOTIF/X WINDOWS” or "WINDOWS NT.”
- Processor 12 is coupled to a memory device 18 comprising, for example, a high-speed disk drive.
- An input device 14 is also coupled to processor 12, enabling a user to enter instructions and other data.
- Input device 14 comprises, for example, a keyboard, a mouse, and/or a touch-sensitive display screen.
- Input device 14 alternatively, or in addition, may comprise a real-time data feed for receiving an electronic representation of financial instruments.
- input device 14 could provide a connection to an electronic data network (e.g., the Internet) through a modem
- Computer-implemented apparatus 10 also includes an output device 16, such as a video display monitor and/or a laser printer, for presenting textual and graphical information to a user.
- processor 12 is capable of executing application programs written in the "C++" programming language using object-oriented programming techniques, but the present invention is not limited in this regard.
- processor 12 is capable of executing a compression engine 20 configured to perform portfolio compression.
- compression engine 20 comprises a software module including executable instructions for carrying out various tasks and calculations related to portfolio compression, but persons skilled in the art will recognize that firmware- and/or hardware-based implementations are also possible.
- Compression engine 20 can be used, for example, to analyze the risk of a large and complex portfolio, or to analyze the performance of a number of hedges against potential losses for a given portfolio.
- the portfolio compression techniques taught herein are well-suited to such uses because of the improved processing speed and efficiency they provide.
- a user may use input device 14 to enter information describing the composition of a target portfolio (i.e., the portfolio to be compressed), including, for example, the number and type of financial instruments in the target portfolio.
- information about the target portfolio could be provided through a real-time data feed.
- the information is input to compression engine 20, and may also be stored in memory device 18.
- the compressed portfolio is presented on output device 16 in the form of, for example, graphs, textual displays and/or printed reports.
- an electronic representation of the compressed portfolio may be stored in memory device 18 for later output to other tasks within computer- implemented apparatus 10, and may also be written to a portable storage device (not shown) such as a CD-ROM or one or more diskettes.
- compression engine 20 can be configured to include a number of sub-mod- ules corresponding to various tasks for accomplishing portfolio compression.
- compression engine 20 includes an instrument load module 24, a sorting module 26, a compression module 28, and an aggregation module 30.
- These various modules can be configured to pass information from one module to the next (e.g., by passing parameters comprising addresses for locations in memory device 18), or the modules may be given access to common data stores within memory device 18.
- the present invention is not limited to any particular implementation.
- Fig. 3 contains a flow diagram describing a general embodiment of a method for portfolio compression that may be implemented using, for example, the apparatus illustrated in Fig. 1.
- a target portfolio containing a collection of financial instruments is first sorted into compressible and non-compressible instruments (Step 100). This step may be accomplished, for example, by instrument load module 24 and sorting module 26 of the embodiment illustrated in Fig. 2.
- a compressed subportfolio is generated for the compressible instruments (Step 110) using, for example, compression module 28.
- the compressed subportfolio and the non-compressible instruments are combined into a single compressed portfolio (Step 120) using, for example, aggregation module 30.
- compressible does not necessarily connote any particular characteristic of a financial instrument. Rather, the determination of whether a financial instrument is compressible can be user-driven. A given portfolio manager, for example, may be willing to accept a lower degree of confidence with respect to a compressed portfolio than another portfolio manager, and therefore may consider a particular financial instrument to be compressible where the latter porfolio manager would not.
- Fig. 4 contains a flow diagram showing a method for portfolio compression in accordance with another embodiment of the present invention. This method may be implemented, for example, using an apparatus such as that illustrated in Fig. 1, although any other suitable computing apparatus may be used.
- a target portfolio of instruments 38 is to be compressed.
- instruments 38 are first input to a load instruments routine 40.
- Instruments 38 may be received, for example, as a collection of data packets defining the composition of the target portfolio.
- Electronic representations of the financial instruments in the target portfolio can be loaded from an external storage medium (e.g., a data warehouse, a database, a set of comma separated values (.csv) files).
- an external storage medium e.g., a data warehouse, a database, a set of comma separated values (.csv) files).
- the size of each incremental load can be set through a parameter passed, for example, to compression engine 20 through a GUI (graphical user interface) or a configuration file, and would typically be based on limitations of memory device 18.
- sort and divide routine 42 After confirming the validity of the information conveyed in the received data packets using appropriate edit routines (not shown), the information describing the instruments in the target portfolio is subjected to a sort and divide routine 42 where instruments 38 are first divided into subportfohos according to a set of predefined user preferences, or "key attributes." Key attributes may include, for example, information such as a counterparty, a discount curve, and so on.
- sort and divide routine 42 implements a new portfolio hierarchy representing a desired level of portfolio aggregation that a portfolio manager, for example, wishes to use for overall risk analysis. These subportfohos may then be further sorted or subdivided according to the set of compression techniques, if any, that will later be applied to them.
- Such further processing is desirable where, for example, a single compression technique is not ideal for all of the different types of financial instruments in the target portfolio.
- Some subportfohos generated by sort and divide routine 42 may consist of instruments for which compression is undesirable or unnecessary, and such subportfohos are immediately migrated to a temporary storage location (e.g., a location within memory device 18 corresponding to a cash account) for later aggregation with compressed subportfohos.
- a temporary storage location e.g., a location within memory device 18 corresponding to a cash account
- the institution might not be able to compress certain subportfohos and still maintain a desired level of accuracy, or the available current compression techniques might not be well-suited for the type of instruments in a particular subportfolio.
- the remaining subportfohos are then passed to a cashflow generation routine 44 if they contain instruments with fixed cashflows, or directly to a compression routine 48 if they do not.
- Cashflow generation routine 44 generates cashflows for the instruments in an input subportfolio based on the financial description of each such instrument. For example, the cashflows of a fixed rate bond are generated from the maturity date, notional, and coupon rate.
- the output from cashflow generation routine 44 is a set of cashflows on specific dates in the future corresponding to the input instruments, and this output is passed to a first aggregation routine 46. Those instruments that are already represented by their cashflows are passed directly to aggregation routine 46. In aggregation routine 46, all of the cashflows that are discounted with a common interest rate curve are then aggregated (i.e., netted) into a single synthetic bond that pays the netted cashflows of all instruments at the specified times.
- This synthetic bond is denoted an "aggregated cashflow instrument," or ACL
- ACL aggregated cashflow instrument
- the output of aggregation routine 46 is thus a subportfolio containing one or more ACIs, as well as instruments that cannot be represented by fixed cashflows.
- the input to aggregation routine 46 contained only real financial instruments traded by the institution, the output contains synthetic, non-traded instruments.
- the input subportfolio and the output subportfolio behave identically. Since it is quite common in fixed-income portfolios to have a very large number of instruments that either generate only fixed cashflows (or can be represented for valuation purposes as generating only fixed cashflows), the potential savings that results from this processing is enormous.
- aggregation routine 46 can be executed recursively as a target portfolio is incrementally loaded.
- compression routine 48 receives subportfohos from aggregation routine 46 and/or sort and divide routine 42.
- subportfohos are generally compressed into a reduced, simpler set of instruments.
- each set of fixed cashflows instruments whose value depends on a single interest rate curve, is compressed to at most two cashflows.
- compression routine 48 may be implemented to execute recursively as instruments are incrementally loaded.
- Compression routine 48 may be configured to perform an extended type of analytical compression to deal with options, although it may be more advantageous to implement a configuration of compression engine 20 that also is capable of performing scenario-based compression on subportfohos with options, since scenario-based compression generally results in compressed subportfohos that contain options as well.
- compression routine 48 is preferably configured to be extensible, thereby allowing for the integration of other compression routines.
- the input to compression routine 48 is a subportfolio that may be subjected to one or more available compression techniques.
- the particular techniques applied may be dictated, for example, by a user or by characteristics of the portfolio to be compressed.
- the compressed instruments might appear to be real traded instruments, but they do not necessarily have to be traded for purposes of risk management.
- all of the subportfohos, both compressed and non-compressed are passed to a second aggregation routine 50 to be combined into a single compressed portfolio 52. Compressed portfolio 52 can then be used, for example, as the basis for various risk assessment analyses of the target portfolio.
- sort and divide routine 42 is configured to divide the input target portfolio
- Each key attribute is associated with a particular feature or characteristic of a financial instrument, and serves as a sort key on which the collection of financial instrument information can be sorted.
- the judicious use of key attributes allows a user to refine the contents of each subportfolio to a level consistent with that user's particular risk management reporting objectives.
- a list of key attributes can be passed to sort and divide routine 42 using, for example, a GUI or a configuration file. The use of key attributes provides a convenient way to tailor the operation compression engine 20 to particular uses.
- key attributes can be used to cause sort and divide routine 42 to generate subportfohos that are particularly directed to the performance of credit risk reporting.
- an input portfolio can be partitioned based on attributes such as (a) the legal entities that were the counterparties in the associated transactions, and (b) the jurisdictions where the transactions were booked.
- attributes such as (a) the legal entities that were the counterparties in the associated transactions, and (b) the jurisdictions where the transactions were booked.
- Application of these key attributes will result in the input portfolio being divided into subportfohos associated with different legal entities, and further being divided into subportfohos associated with different jurisdictions.
- These subportfohos could be further divided based on instrument type (e.g., option, fixed income). It should be noted, however, that such a sorting approach is presented by way of example only.
- the most advantageous key attributes for any particular implementation will vary, for example, in accordance with the particular reporting needs of a given institution or a particular type of risk analysis.
- sort and divide routine 42 may apply an additional set of key attributes to further sort the instruments into subportfohos based on whether or not they will be compressed and, if so, the particular compression methodology(s) that will be applied. In other words, those instruments that will eventually be compressed are separated from those instruments that will not be compressed, and the instruments that will eventually pass through one or more of the compression routines are sorted into subportfohos based on the compression technique or combination of techniques that will be performed on them.
- a user may also specify the types of compression techniques that will be applied.
- compression engine 20 By configuring compression engine 20 to include a function library for compression that is both flexible and extendible, users can be given the ability to vary the composition of the resulting subportfohos (in essence, a list of compressible instruments) according to the compression methodology desired.
- the input subportfohos to cashflow generation routine 44 are comprised of instruments that either generate only fixed cashflows or can be represented for valuation purposes as generating only fixed cashflows.
- Such instruments include, for example, fixed rate bonds, floating rate notes, forward rate agreements, futures and forward contracts, foreign exchange forwards, fixed notional swaps and certificates of deposit.
- cashflow instruments are advantageously represented in computer-implemented apparatus 10 in terms of their financial and accounting descriptions, and not directly as actual cashflows.
- cashflow generation routine 44 generates the cashflows of these instruments based on these financial descriptions.
- a given fixed rate bond may be described by its maturity, notional, coupon rate and coupon frequency. Future cashflows can then be determined completely from this information, and its mark-to-market valuation can be obtained by discounting the future cashflows using current market rates.
- the particulars may vary in accordance with the particular needs of any given implementation.
- first aggregation routine 46 can be configured to generate a new type of instrument, called an aggregated cashflow instrument or ACI, for every interest rate curve.
- An ACI is simply a synthetic bond that pays the specified cashflows at the specified times.
- all the generated cashflows that are discounted with the same discount curve are aggregated into a single ACI, and cashflows occurring on the same day are netted.
- the present value can thus be determined by discounting these cashflows using a single discount curve. For example, a portfolio consisting of 5000 fixed rate bonds in US dollars with maturities up to 10 years and paying semi-annual coupons would contain at most 100,000 cashflows.
- subportfolio(s) output from first aggregation routine 46 containing those instruments that could not be represented by a fixed cashflow (e.g., options) and one or more aggregated cashflow instruments, has the same theoretical value and the same sensitivities to the previously-identified risk factors as the input subportfolio(s) since no approximations have been done. Other discounting approaches can alternatively be applied.
- the present invention is not limited in this regard.
- compression routine 48 applies analytical compression to compress subportfohos containing a large set of cashflows into a much smaller set of cashflows, and also applies scenario-based compression to compress subportfohos containing options.
- Compression routine 48 is ideally designed to be extendible so that additional compression methodologies can easily be added in a modular way. Implementation of such extensible designs is well known in the field of software development. The following discussion describes both analytical compression and scenario-based compression in further detail, including mathematical support for the theoretical models underlying the respective compression techniques.
- Analytical compression bears some resemblance to known principal component techniques, where the changes in the risk factor space are captured in a low-dimensional projection of the original space.
- the mapping obtained through analytical compression is not necessarily linear and it optimally accounts for the behavior of the portfolio.
- analytical compression goes much further than standard cashflow bucketing techniques (discussed below) where, for example, cashflows at given times are mapped to their duration equivalents on adjacent, predetermined nodes.
- standard cashflow bucketing techniques discussed below
- analytical compression preserve the global properties of the portfolio more accurately, but it also offers at least an order of magnitude improvement in processing time.
- the principles and theory of analytical compression are described below in the context of a particular implementation for fixed cashflow portfolios.
- VaR Value-at-Risk
- V(R., t) denotes the value of the portfolio at time t
- R t represents the vector of underlying (stochastic) risk factors
- (one-sided) is typically 0.9 to 0.99.
- the time interval is usually between 1 and 10 days.
- r - (r clove r 2 , . . ., r join) represents the vector of continuously compounded discount rates at each term.
- the "modified duration" of the portfolio is defined as the (negative) derivative of with respect to the yield, that is
- the yield of the portfolio can be viewed as an alternative representation of the value of the portfolio.
- the yield further acts with a similar functional form as the rates to give the value of the portfolio.
- V (y) the pricing function
- VaR can be calculated analytically, without simulation, by noticing that V(y) is monotonic and applying the one factor theorem described in Section 2 of Ron Dembo et al., Analytical Compression of Portfolios and VaR, Algorithmics Technical Paper No. 96-01 (1997), which discussion is incorporated herein by reference. For more general portfolios, performance can be improved even further by making some approximations, as shown below.
- V The transformation from yield to value, V , still requires the discounting of n cashflows.
- an efficient approximation of this function can be used for VaR calculations.
- V y For a portfoho of only positive (or only negative) cashflows, it is possible to reduce the problem to the computation of a single cashflow, such as a zero coupon bond. That is, the function V y can be approximated by
- V(y) is also monotonic, and therefore the VaR approximation could be computed without simulation.
- the result of this approximation is a series of exponentials with a single exponential function that matches both value and first derivative at one point, and where the term (-y t) "averages" the exponents in the series.
- V(y) is always dominated by V (y), the exact value. That is,
- the yields (y + , y ) of both subportfohos are unique in this case, and the total portfoho can be compressed into two cashflows, a positive and a negative one.
- the two compressed portfolios respectively, have yields (y + , y ), computed through Eq. 3, coupons (C + , C ) and durations (t + , t " ), computed through Eq. 11 and Eq. 12.
- the portfolio value function can be approximated by
- the VaR of the portfolio can be computed through a Monte Carlo simulation on the two- dimensional space (y + , y) and using Fas in Eq. 15. Given the low dimensionality and simple valuation, this is an effective computational technique. Furthermore, other low-dimensional integration techniques may be more effectively used in this case (e.g., low discrepancy sequences). Notice also that the property of strict monotonicity of Kin each risk factor, (y + , y), can be exploited to accelerate simulations.
- V(y,S) S- Cexp(-yt) (Eq. 18)
- the portfoho can be seen as one position in a bond in the foreign currency. Note the intrinsic multiplicative functionality of the FX spot rate.
- Eq. 18 may be written as
- Eq. 19 reduces the problem to the single cashflow case, and hence its VaR can be computed analytically.
- the errors in the distribution introduced can be substantial when compared with those that arise from using the yield approximation of Eq. 18 exclusively.
- the main sources of these errors arise from the discrete approximation of Eq. 20 and the degree of non-normality of the distribution of Y s .
- V ⁇ ⁇ s *[ c * +ex P -?* ⁇ ) +c * ⁇ ex P( -Vk )1 (E - 22)
- bucketing is a known technique for reducing a total number of cashflows produced by a set of instruments.
- bucketing is a technique that is desirable in practice not only for performance reasons, but also because distributions are generally only available for a small number of term points.
- the J.P. Morgan distributed data sets see the above-cited RiskMetricsTM - Technical Document
- Industry standards for bucketing of fixed income instruments include “duration bucketing” and the bucketing suggested in RiskMetricsTM. Given a set of standard term nodes, both methods map each cashflow separately to the two (or one) closest nodes.
- Duration bucketing accomplishes this by matching the present value and the duration of the original cashflow.
- the bucketing described in RiskMetricsTM does this by matching present value and the volatility of the original cashflow.
- a further assumption of linear interpolation between the prices of zero coupon bonds is required. Additional information on these two bucketing techniques, including their relative advantages and disadvantages, can be found in the above- cited RiskMetricsTM - Technical Document and Mark B. Garman, Issues and Choices in Analytic (Variance-Covariance) Value at Risk (presented at the RIMAC 97 Conference, Scottsdale, Arizona, February 1997).
- delta bucketing provides a more powerful and robust technique than either duration bucketing or the RiskMetricsTM approach.
- Delta bucketing is generally applicable to all financial instruments, but is perhaps most appropriate for linear instruments. Delta bucketing aims to standardize the times at which cashflows occur. For fixed income instruments, delta bucketing reduces the number of cashflows in a portfoho by redistributing them over the standard term structure. This redistribution of the cashflows is done in a such a way that the partial derivatives (or key rate durations) of each individual instrument or cashflow with respect to each of the original risk factors is preserved.
- compression engine 20 can be configured to perform scenario-based optimization as follows.
- a portfolio or subportfolio to be compressed is passed to a cashflow and embedded option analyzer that returns a set of maturities for all instruments in the subportfolio, a set of underlying risk factors, and a range of strike prices for any embedded options.
- the output from cashflow and embedded option analyzer along with a description of the type of analysis to be performed (e.g., 10-day VaR at 99% confidence, 1 -month VaR at 95%), then serves as input to a replicating set generator.
- the information concerning analysis type can be obtained, for example, from a user through a GUI or from a configuration file.
- the replicating set generator outputs a set of replicating instruments that effectively "spans," or covers, the target portfolio.
- the output from the cashflow and embedded option analyzer and the information concerning analysis type also serves as input to a scenarios generator that returns a set of scenarios and time points under which the replication is to be performed.
- the scenarios generator may generate scenarios dynamically, or may retrieve previously-generated scenarios from, for example, a database.
- the operations of the replicating set generator and the scenarios generator are governed by a simple rule-based system, an example of which is set forth below.
- a simulation module determines the values of every instrument in the target portfolio under every scenario at the specified time points.
- the results of the simulation module are then input to an optimization problem module, which formulates a linear programming problem to find the optimal replicating portfolio.
- This problem is then solved using standard linear programming techniques and associated software (e.g., the CPLEXTM application distributed by ILOG of Incline Village, Nevada).
- the solution to the problem is a set of positions to take in the replicating instruments that best matches the behavior of the target portfoho over the specified scenarios.
- a construct compressed portfoho module constructs the compressed replicating portfolio from the output of optimization problem module.
- the construct compressed portfolio module may generate a report identifying actual market transactions to carry out in order to construct the replicating portfoho.
- the set of instruments contained in the replicating portfolio i.e., the replicating instruments
- the scenario-based compression model rests on a number of assumptions. For example, it is assumed that a compressed portfolio will be used as a surrogate for the corresponding target portfoho over some finite period of time (hereinafter, the "replication period"). During the replication period, it is assumed that only a finite number of events or scenarios S can occur; however, there is uncertainty as to which of these events will actually occur. Accordingly, the probability of an i' h future event occurring at some point during the replication period is denoted byp 1 E R s .
- a second assumption underlying the scenario-based compression model is that only a finite number N of financial instruments are available for creating the compressed portfolio.
- the compressed portfolio will only be used as a surrogate for valuing the target portfolio and its attributes, it may be made up of any instruments whose prices are known. Moreover, the liquidity of the instruments is not relevant unless the compressed portfolio is to be used for purposes other than valuation (for example, hedging).
- Fig. 5 sets forth notation conventions that will be used in explaining further the technique of scenario-based compression.
- a superscript Twill denote the transpose of a vector or matrix.
- E(D a ) D T j> denotes an N-dimensional column vector of expected values of attribute a of the instruments in the compressed portfoho at the end of the replication period.
- a tracking function may be used to measure the degree to which a compressed portfoho matches a corresponding target portfoho under the possible values that the attributes might assume during the replication period.
- the tracking function may be expressed as
- norm used to measure the deviations between the compressed portfoho and the target portfolio will depend on the context and the desired statistical properties of the solution. For example, one could choose standard regret or maximum error as the error measure; and all errors, only positive errors, or only negative errors may be minimized. In this measure, weighting constants w a are used to emphasize one attribute over another and to apply a conversion to consistent units. For example, if standard regret, including all errors, is chosen then
- a scenario-based compression model may thus be expressed in a relatively straight-forward manner.
- this model there always exists a feasible compressed portfoho (that is, one satisfying the equation below), provided there are more independent instruments from which the compressed portfoho is selected than there are attributes that must be matched at the start of the replication period.
- This model can be described mathematically as follows:
- a compressed portfoho generated by, for example, compression engine 20 of the embodiment shown in Fig. 1 may be subjected to post-processing where the compression process generates instruments that depend on new risk factors (i.e., risk factors that were not present in the original, uncompressed target portfoho). These new risk factors may be provided, for example, by a market risk factors' distribution module.
- a scenario generation module creates a set of scenarios based on these new risk factors, or alternatively adds the new correlated scenarios to an existing scenario set, after which the institution's risk profile can be calculated using the compressed portfolio and the new scenario set.
- the risk factor space will typically include some new variables (e.g., the compressed yields,).
- scenarios must be generated from the joint distribution of the market factors and the new risk factors. These joint distributions are readily available from the yield sensitivities which describe the stochastic processes they follow (see the discussion of analytical compression above). If a scenario set in the original risk factors exists, each scenario is augmented to include the new risk factors (using, for example, Eq. 8, 9 and 16 above).
- the first example involves application of a compression engine, such as that shown in Fig. 2, to a simple portfoho, and demonstrates both the accuracy and possible time savings that may be realized.
- a compression engine such as that shown in Fig. 2
- Fig. 2 The first example involves application of a compression engine, such as that shown in Fig. 2, to a simple portfoho, and demonstrates both the accuracy and possible time savings that may be realized.
- this reduced set of cashflows was passed to an analytical compression subroutine, and the positive cashflows were separated from the negative cashflows.
- the yield was calculated and the cashflows were compressed to a single zero coupon bond.
- the output from compression routine 48 consisted of two zero coupon bonds — one with a positive notional and one with a negative notional.
- two new risk factors based on the yield to maturity of each zero coupon bond, were created.
- Fig. 6 shows the 13 cashflows produced using the delta bucketing compression technique. Note that a cashflow was created at the 15-year term point, which is three years past the longest maturing bond.
- Fig. 7 shows the cashflows of the compressed portfolio produced by applying the analytical compression technique to the result of the delta bucketing compression.
- the first zero coupon bond created has a cashflow on February 22, 1998 of (39,554,346.0729) USD, and the calculated yield is 5.1185%.
- the second zero coupon bond created has a cashflow of 60,098,278.9511 USD on September 18, 2001, and the calculated yield is 5.5537%.
- the compressed portfolio consists of the two compressed bonds calculated as set forth above.
- Quantities in parentheses represent VaR as a percentage of the original portfolio's scenario-based VaR (column 2).
- Quantities in parentheses represent VaR as a percentage of the original portfolio's scenario-based VaR (column 2).
- the Value-at-Risk obtained from the compressed portfoho differs from the Value-at-Risk obtained from the original portfolio by at most 1.22%), and is generally much closer.
- the time required to compress the portfolio and calculate the scenario-based VaR from the compressed portfolio varied from approximately 3% to 10%) of the time required to calculate the scenario-based VaR from the target portfoho.
- an embodiment such as that illustrated in Fig. 4 was applied to a more complex target portfolio containing a substantial number of derivative positions. Delta bucketing, analytical compression, and scenario-based compression methodologies were applied to the target portfoho to show the accuracy and time savings that can be achieved.
- the target portfolio for this second example consisted of over 18,000 instruments, including many instruments with optionality such as caps and swaptions, in three currencies (British pounds sterling, Japanese yen, and U.S. dollars). The current time used was February 14, 1996 and the three discount curves (one in each currency) ranged from 5.5%o to 6.5%>.
- the instruments that comprised the target portfolio were common stock, European equity options, European FX options, caps, swaptions, fixed notional swaps, fixed rate CDS, fixed rate bonds, swap fixed legs, swap predetermined legs, currency swaps and FX forwards.
- the number of positions in each instrument are summarized in the second column of Table 3.
- load instruments routine 40 data packets describing the instruments 38 in the target portfoho were loaded incrementally in blocks of 400 instruments each.
- sort and divide routine 42 the 400 instruments from the input subportfolio were partitioned into 9 subportfohos. These were created based on the key attribute, which in this case was discount curve, and the compression methodologies to be applied to the instruments. Only one discount curve was used for each currency, and hence the input subportfolio was first partitioned into three subportfohos. Next, the three subportfohos were partitioned based on instrument type only, since instrument type was used to determine the compression methodologies used.
- the input was one of the three subportfohos (separated by discount curve) containing fixed income instruments.
- the cashflows of these instruments were generated and then aggregated into a single ACI.
- the output from these two modules was thus three subportfohos, each consisting of a single ACI.
- compression routine 48 the input subportfohos were compressed using either the analytical compression method or the scenario-based compression method described above.
- Each subportfolio consisting of caps and swaptions was compressed using the scenario-based compression, where the set of replicating instruments consisted of zero coupon bonds and caplets in each of the three currencies.
- the scenarios for replication were bucket shifts of 1%> to the instruments' discount curves at standard node points, and parallel shifts of three standard devia- tions in the discount curves, thus capturing higher-order effects.
- the output of this scenario- based compression was a portfolio of positions in approximately 10 zero coupon bonds and 10 at- the-money caplets.
- Each of the three subportfohos consisting of a single ACI was then compressed using delta bucketing and analytical compression.
- delta bucketing was applied, resulting in at most 14 cashflows at the standard RiskMetricsTM term points.
- this reduced set of cashflows was passed to an analytical compression subroutine and was separated into positive cashflows and negative cashflows. In each instance (positive and negative) the yield was calculated and the cashflows were represented by a zero coupon bond.
- the output from compression routine 48 was two zero coupon bonds — one with a positive notional, and one with a negative notional.
- two new risk factors based on the yield to maturity of each zero coupon bond, were created.
- the six subportfohos that were passed through compression routine 48 comprised the compressed portfolio. The entire process was then repeated until all of the instruments in the target portfolio were loaded and processed. Once there were no more instruments to be loaded, based on the six new yields that were created as risk factors during analytical compression, new scenarios were generated in a scenario generation routine, and these were added to the existing scenario set.
- VaR for the reduced compressed portfoho was calculated using 1000 Monte Carlo scenarios on the three discount curves.
- the last column of Table 3 above indicates the times required to create the compressed portfoho and the new scenarios are indicated in the last column.
- the time required to simulate the scenario-based VaR is indicated in the middle columns.
- the time required to calculate the VaR of the uncompressed target portfolio was approximately 13.5 hours.
- the time savings resulting from using various compression methodologies to reduce the size and complexity of the portfolios before the VaR was calculated was substantial ⁇ it took only 40 minutes to compress the target portfolio and calculate its VaR.
- the caps were compressed using two methods: delta bucketing and scenario-based compression.
- delta bucketing As also shown in Table 4, the accuracy of the VaR results of the compressed portfolios were compared to the VaR results of the uncompressed portfolio of caps. It can be seen that the scenario-based VaR results of the portfolio compressed with delta bucketing were within 4.5% of the VaR produced using the uncompressed portfoho. The results are even more impressive when scenario-based compression was used to compress the original portfolio — the scenario-based VaR of the compressed portfolio was within 0.2%) of the VaR produced using the uncompressed portfolio. Substantial time savings were also realized when the VaR was calculated using the compressed portfolios. The simulation time of the portfoho consisting of 1,200 caps was almost seven hours; whereas the simulation time of the compressed portfoho, including the time required for compression, was less than six minutes.
- portfoho compression in accordance with embodiments of the present invention is the lack of liquidity restrictions on the replicating variables (i.e., the positions of instruments in the compressed portfolio). In practice, however, some liquidity restrictions make the solution of the compressed portfoho more stable. This benefit also derives from the fact that the essence of a compressed portfolio, as just discussed, is that it price correctly. Since the compressed portfoho need not be comprised of tradeable instruments, it may contain fictitious instruments in any quantity provided the price of such instruments is "fair" with respect to the market. Such fictitious instruments can be priced using analytical models based on, for example, no-arbitrage conditions or equilibrium principles, as described in John C. Hull, Options, Futures and Other Derivatives (3E) 572 (Prentice-Hall 1997).
- Embodiments of the present invention may be distributed, for example, as a set of executable instructions residing on a storage medium.
- a storage medium can be a memory of a computer; a piece of firmware; a portable storage device, such as a diskette or other magnetic storage device, or a CD-ROM; or any other medium on which it is possible to store or otherwise distribute executable instructions.
Abstract
Description
Claims
Priority Applications (6)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU76330/98A AU740917B2 (en) | 1997-05-29 | 1998-05-29 | Computer-implemented method and apparatus for portfolio compression |
JP50003999A JP2002500789A (en) | 1997-05-29 | 1998-05-29 | Computer implemented method and apparatus for portfolio compression |
DE69820873T DE69820873D1 (en) | 1997-05-29 | 1998-05-29 | COMPUTER-BASED METHOD AND APPARATUS FOR COMPRESSING PORTFOLIOS |
CA002290368A CA2290368A1 (en) | 1997-05-29 | 1998-05-29 | Computer-implemented method and apparatus for portfolio compression |
EP98923949A EP0985188B1 (en) | 1997-05-29 | 1998-05-29 | Computer-implemented method and apparatus for portfolio compression |
AT98923949T ATE257256T1 (en) | 1997-05-29 | 1998-05-29 | COMPUTER BASED METHOD AND APPARATUS FOR COMPRESSING PORTFOLIOS |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US5092797P | 1997-05-29 | 1997-05-29 | |
US60/050,927 | 1997-05-29 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO1998054666A1 true WO1998054666A1 (en) | 1998-12-03 |
Family
ID=21968377
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CA1998/000519 WO1998054666A1 (en) | 1997-05-29 | 1998-05-29 | Computer-implemented method and apparatus for portfolio compression |
Country Status (8)
Country | Link |
---|---|
US (1) | US6278981B1 (en) |
EP (1) | EP0985188B1 (en) |
JP (1) | JP2002500789A (en) |
AT (1) | ATE257256T1 (en) |
AU (1) | AU740917B2 (en) |
CA (1) | CA2290368A1 (en) |
DE (1) | DE69820873D1 (en) |
WO (1) | WO1998054666A1 (en) |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
FR2792746A1 (en) * | 1999-04-21 | 2000-10-27 | Ingmar Adlerberg | Control of staged production flow in response to random pressures on production stages using the 'value at risk' method |
WO2001055812A2 (en) * | 2000-01-28 | 2001-08-02 | Pi Eta Consulting Company Pte Ltd | Fully flexible financial instrument pricing system with intelligent user interfaces |
US6278981B1 (en) * | 1997-05-29 | 2001-08-21 | Algorithmics International Corporation | Computer-implemented method and apparatus for portfolio compression |
US6292787B1 (en) | 1998-09-11 | 2001-09-18 | Financial Engines, Inc. | Enhancing utility and diversifying model risk in a portfolio optimization framework |
US7016870B1 (en) | 1997-12-02 | 2006-03-21 | Financial Engines | Identifying a recommended portfolio of financial products for an investor based upon financial products that are available to the investor |
US7249081B2 (en) | 2000-02-23 | 2007-07-24 | Financial Engines, Inc. | Load aware optimization |
US7395236B2 (en) | 1999-06-03 | 2008-07-01 | Algorithmics Software Llc | Risk management system and method providing rule-based evolution of a portfolio of instruments |
US7447652B2 (en) | 2001-05-31 | 2008-11-04 | Ge Corporate Financial Services, Inc. | Methods and systems for portfolio cash flow valuation |
WO2012008847A1 (en) * | 2010-07-16 | 2012-01-19 | Odd Rune Eikemo Fitje | Control system for controlling complex facilities hosting multiple concurrent processes |
US8200561B1 (en) | 2002-03-29 | 2012-06-12 | Financial Engines, Inc. | Tax-aware asset allocation |
Families Citing this family (211)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020046143A1 (en) * | 1995-10-03 | 2002-04-18 | Eder Jeffrey Scott | Method of and system for evaluating cash flow and elements of a business enterprise |
US10586282B2 (en) | 1996-03-25 | 2020-03-10 | Cfph, Llc | System and method for trading based on tournament-style events |
US6505174B1 (en) | 1996-03-25 | 2003-01-07 | Hsx, Inc. | Computer-implemented securities trading system with a virtual specialist function |
US20010034686A1 (en) * | 1997-12-10 | 2001-10-25 | Eder Jeff Scott | Method of and system for defining and measuring the real options of a commercial enterprise |
US20050119922A1 (en) * | 1997-01-06 | 2005-06-02 | Eder Jeff S. | Method of and system for analyzing, modeling and valuing elements of a business enterprise |
US10839321B2 (en) * | 1997-01-06 | 2020-11-17 | Jeffrey Eder | Automated data storage system |
US20080004922A1 (en) * | 1997-01-06 | 2008-01-03 | Jeff Scott Eder | Detailed method of and system for modeling and analyzing business improvement programs |
US20080071588A1 (en) * | 1997-12-10 | 2008-03-20 | Eder Jeff S | Method of and system for analyzing, modeling and valuing elements of a business enterprise |
US7809642B1 (en) | 1998-06-22 | 2010-10-05 | Jpmorgan Chase Bank, N.A. | Debit purchasing of stored value card for use by and/or delivery to others |
US6615189B1 (en) | 1998-06-22 | 2003-09-02 | Bank One, Delaware, National Association | Debit purchasing of stored value card for use by and/or delivery to others |
US7801782B2 (en) | 1998-07-31 | 2010-09-21 | Jpmorgan Chase Bank, Na | Object oriented system for managing complex financial instruments |
US7650303B2 (en) * | 1998-11-05 | 2010-01-19 | Financeware, Inc. | Method and system for financial advising |
US7809636B1 (en) | 1998-11-13 | 2010-10-05 | Jpmorgan Chase Bank, N.A. | System and method for multicurrency and multibank processing over a non-secure network |
US6032136A (en) | 1998-11-17 | 2000-02-29 | First Usa Bank, N.A. | Customer activated multi-value (CAM) card |
US7660763B1 (en) | 1998-11-17 | 2010-02-09 | Jpmorgan Chase Bank, N.A. | Customer activated multi-value (CAM) card |
US7644029B2 (en) * | 1998-11-23 | 2010-01-05 | New Market Solutions, Llc | Digital computer system for a synthetic investment and risk management fund |
WO2000038095A2 (en) | 1998-12-23 | 2000-06-29 | The Chase Manhattan Bank | System and method for integrating trading operations including the generation, processing and tracking of and trade documents |
US8392302B2 (en) * | 1999-03-31 | 2013-03-05 | Task Management, Inc. | Computer-aided process for inflation-immunized derivatives |
US7068832B1 (en) | 1999-05-11 | 2006-06-27 | The Chase Manhattan Bank | Lockbox imaging system |
AU5377900A (en) * | 1999-06-02 | 2000-12-28 | Algorithmics International Corp. | Risk management system, distributed framework and method |
US6882984B1 (en) | 1999-06-04 | 2005-04-19 | Bank One, Delaware, National Association | Credit instrument and system with automated payment of club, merchant, and service provider fees |
US6397202B1 (en) * | 1999-07-01 | 2002-05-28 | The United States Of America As Represented By The Secretary Of The Navy | System and method for monitoring risk in a system development program |
US8577778B2 (en) * | 1999-07-21 | 2013-11-05 | Longitude Llc | Derivatives having demand-based, adjustable returns, and trading exchange therefor |
US7742972B2 (en) * | 1999-07-21 | 2010-06-22 | Longitude Llc | Enhanced parimutuel wagering |
US6709330B1 (en) * | 1999-08-20 | 2004-03-23 | Ameritrade Holding Corporation | Stock simulation engine for an options trading game |
US7124111B1 (en) * | 1999-09-14 | 2006-10-17 | Jpmorgan Chase Bank, N.A. | Service charge adjustment platform |
US7542921B1 (en) | 1999-09-30 | 2009-06-02 | Jpmorgan Chase Bank, N.A. | Network-based financial planning system and method |
US7805365B1 (en) | 1999-10-25 | 2010-09-28 | Jpmorgan Chase Bank, N.A. | Automated statement presentation, adjustment and payment system and method therefor |
US6747762B1 (en) * | 1999-11-05 | 2004-06-08 | Sharp Laboratories Of America, Inc. | Method for optimizing compression of scanned data |
US8793160B2 (en) | 1999-12-07 | 2014-07-29 | Steve Sorem | System and method for processing transactions |
US6965865B2 (en) | 1999-12-30 | 2005-11-15 | Bank One Delaware N.A. | System and method for integrated customer management |
US20010056391A1 (en) * | 2000-01-14 | 2001-12-27 | Schultz Frederick J. | Method and apparatus for managing and optimizing stock options |
US7418417B2 (en) * | 2000-02-11 | 2008-08-26 | Goldman Sachs & Co. | Credit index, a system and method for structuring a credit index, and a system and method for operating a credit index |
US7822656B2 (en) | 2000-02-15 | 2010-10-26 | Jpmorgan Chase Bank, N.A. | International banking system and method |
US8768836B1 (en) | 2000-02-18 | 2014-07-01 | Jpmorgan Chase Bank, N.A. | System and method for electronic deposit of a financial instrument by banking customers from remote locations by use of a digital image |
US6941279B1 (en) | 2000-02-23 | 2005-09-06 | Banke One Corporation | Mutual fund card method and system |
US6938010B1 (en) | 2000-03-22 | 2005-08-30 | Ford Global Technologies, Llc | Method of optimizing market and institutional risks in foreign exchange hedging |
US6847976B1 (en) * | 2000-06-15 | 2005-01-25 | Terrence B. Peace | Method and apparatus for significance testing and confidence interval construction based on user-specified distribution |
US20040172401A1 (en) * | 2000-06-15 | 2004-09-02 | Peace Terrence B. | Significance testing and confidence interval construction based on user-specified distributions |
US7031935B1 (en) | 2000-07-31 | 2006-04-18 | J.P. Morgan Advisory Services Inc. | Method and system for computing path dependent probabilities of attaining financial goals |
US8468071B2 (en) | 2000-08-01 | 2013-06-18 | Jpmorgan Chase Bank, N.A. | Processing transactions using a register portion to track transactions |
WO2002011019A1 (en) | 2000-08-01 | 2002-02-07 | First Usa Bank, N.A. | System and method for transponder-enabled account transactions |
AU2001285422A1 (en) | 2000-08-11 | 2002-02-25 | John J. Loy | Trade receivable processing method and apparatus |
US7689498B2 (en) * | 2000-08-24 | 2010-03-30 | Volbroker Limited | System and method for trading options |
CA2420690A1 (en) * | 2000-08-25 | 2002-03-07 | Espeed, Inc. | Systems and methods for developing and administering investment trusts |
US20050144114A1 (en) * | 2000-09-30 | 2005-06-30 | Ruggieri Thomas P. | System and method for providing global information on risks and related hedging strategies |
US8762178B2 (en) * | 2000-09-30 | 2014-06-24 | Advisen, Ltd. | System and method for providing global information on risks and related hedging strategies |
US6952683B1 (en) * | 2000-10-11 | 2005-10-04 | Ubs Ag | System and method for hedging against foreign exchange risk associated with securities transactions |
US7580890B2 (en) * | 2000-10-12 | 2009-08-25 | Jpmorgan Chase Bank, N.A. | System and method for supervising account management operations |
US20040236673A1 (en) | 2000-10-17 | 2004-11-25 | Eder Jeff Scott | Collaborative risk transfer system |
US20090018891A1 (en) * | 2003-12-30 | 2009-01-15 | Jeff Scott Eder | Market value matrix |
US8285641B2 (en) | 2000-11-06 | 2012-10-09 | Jpmorgan Chase Bank, N.A. | System and method for selectable funding of electronic transactions |
US20020077946A1 (en) * | 2000-12-13 | 2002-06-20 | Caplan Russell L. | Method and apparatus for tracking and evaluating the performance of financial analysts |
US7295999B1 (en) | 2000-12-20 | 2007-11-13 | Jpmorgan Chase Bank, N.A. | System and method for determining eligibility and enrolling members in various programs |
US6985873B2 (en) | 2001-01-18 | 2006-01-10 | First Usa Bank, N.A. | System and method for administering a brokerage rebate card program |
US8805739B2 (en) | 2001-01-30 | 2014-08-12 | Jpmorgan Chase Bank, National Association | System and method for electronic bill pay and presentment |
US20040215551A1 (en) * | 2001-11-28 | 2004-10-28 | Eder Jeff S. | Value and risk management system for multi-enterprise organization |
US7120600B2 (en) * | 2001-02-09 | 2006-10-10 | Tarbox Brian C | Systems and methods for improving investment performance |
US7895098B2 (en) | 2001-03-01 | 2011-02-22 | Jpmorgan Chase Bank, N.A. | System and method for measuring and utilizing pooling analytics |
US8121937B2 (en) | 2001-03-20 | 2012-02-21 | Goldman Sachs & Co. | Gaming industry risk management clearinghouse |
US7958027B2 (en) * | 2001-03-20 | 2011-06-07 | Goldman, Sachs & Co. | Systems and methods for managing risk associated with a geo-political area |
US20040143446A1 (en) * | 2001-03-20 | 2004-07-22 | David Lawrence | Long term care risk management clearinghouse |
US7313546B2 (en) | 2001-05-23 | 2007-12-25 | Jp Morgan Chase Bank, N.A. | System and method for currency selectable stored value instrument |
WO2002099684A1 (en) * | 2001-06-04 | 2002-12-12 | Barra, Inc. | Method and apparatus for creating consistent risk forecasts and for aggregating factor models |
CA2452713A1 (en) | 2001-06-05 | 2002-12-12 | Goldman Sachs & Co. | A system and method for structuring and operating a credit index |
US7403919B2 (en) * | 2001-06-05 | 2008-07-22 | Goldman Sachs & Co., | System and method for determining the liquidity of a credit |
US7702563B2 (en) * | 2001-06-11 | 2010-04-20 | Otc Online Partners | Integrated electronic exchange of structured contracts with dynamic risk-based transaction permissioning |
US7418418B2 (en) * | 2001-06-12 | 2008-08-26 | Blackrock Financial Management, Inc. | System and method for pricing fixed income securities |
WO2003010701A1 (en) | 2001-07-24 | 2003-02-06 | First Usa Bank, N.A. | Multiple account card and transaction routing |
US7809641B2 (en) | 2001-07-26 | 2010-10-05 | Jpmorgan Chase Bank, National Association | System and method for funding a collective account |
US8020754B2 (en) | 2001-08-13 | 2011-09-20 | Jpmorgan Chase Bank, N.A. | System and method for funding a collective account by use of an electronic tag |
US7311244B1 (en) | 2001-08-13 | 2007-12-25 | Jpmorgan Chase Bank, N.A. | System and method for funding a collective account by use of an electronic tag |
US8800857B1 (en) | 2001-08-13 | 2014-08-12 | Jpmorgan Chase Bank, N.A. | System and method for crediting loyalty program points and providing loyalty rewards by use of an electronic tag |
US7822684B2 (en) | 2001-10-05 | 2010-10-26 | Jpmorgan Chase Bank, N.A. | Personalized bank teller machine |
US7620581B2 (en) * | 2001-10-16 | 2009-11-17 | Sungard Systems International Inc. | Financial instrument portfolio credit exposure evaluation |
WO2003038547A2 (en) * | 2001-10-30 | 2003-05-08 | Goldman, Sachs & Co. | Risk management clearinghouse |
US20030093351A1 (en) * | 2001-11-14 | 2003-05-15 | Alvin Sarabanchong | Method and system for valuation of financial instruments |
US7523065B2 (en) * | 2001-12-12 | 2009-04-21 | Asset Trust, Inc. | Risk transfer supply chain system |
US7379911B2 (en) * | 2001-12-26 | 2008-05-27 | Espeed, Inc. | Systems and methods for providing financial instruments including contrary positions |
US8732061B2 (en) | 2001-12-27 | 2014-05-20 | Bgc Partners, Inc. | Creating and trading dynamic securities |
US7426499B2 (en) | 2004-11-08 | 2008-09-16 | Asset Trust, Inc. | Search ranking system |
US7730063B2 (en) | 2002-12-10 | 2010-06-01 | Asset Trust, Inc. | Personalized medicine service |
US20080027769A1 (en) | 2002-09-09 | 2008-01-31 | Jeff Scott Eder | Knowledge based performance management system |
US7756896B1 (en) | 2002-03-11 | 2010-07-13 | Jp Morgan Chase Bank | System and method for multi-dimensional risk analysis |
US7899753B1 (en) | 2002-03-25 | 2011-03-01 | Jpmorgan Chase Bank, N.A | Systems and methods for time variable financial authentication |
US20180165441A1 (en) | 2002-03-25 | 2018-06-14 | Glenn Cobourn Everhart | Systems and methods for multifactor authentication |
US20040210498A1 (en) | 2002-03-29 | 2004-10-21 | Bank One, National Association | Method and system for performing purchase and other transactions using tokens with multiple chips |
AU2003230751A1 (en) | 2002-03-29 | 2003-10-13 | Bank One, Delaware, N.A. | System and process for performing purchase transaction using tokens |
WO2003096254A1 (en) * | 2002-05-07 | 2003-11-20 | Markov Processes International, Llc | A method and system to solve dynamic multi-factor models in finance |
US7689482B2 (en) | 2002-05-24 | 2010-03-30 | Jp Morgan Chase Bank, N.A. | System and method for payer (buyer) defined electronic invoice exchange |
US20030220863A1 (en) | 2002-05-24 | 2003-11-27 | Don Holm | System and method for varying electronic settlements between buyers and suppliers with dynamic discount terms |
US7970640B2 (en) * | 2002-06-12 | 2011-06-28 | Asset Trust, Inc. | Purchasing optimization system |
AU2003243629A1 (en) * | 2002-06-18 | 2003-12-31 | Phil Kongtcheu | Methods, systems and computer program products to facilitate the formation and trading of derivatives contracts |
US8239304B1 (en) | 2002-07-29 | 2012-08-07 | Jpmorgan Chase Bank, N.A. | Method and system for providing pre-approved targeted products |
US7606756B2 (en) | 2002-08-02 | 2009-10-20 | Jpmorgan Chase Bank, N.A. | Synthetic funds having structured notes |
US7983974B2 (en) * | 2002-08-14 | 2011-07-19 | GE Corporate Finanical Services, Inc. | Snapshot approach for underwriting valuation of asset portfolios |
US20040039673A1 (en) * | 2002-08-19 | 2004-02-26 | Matt Amberson | Method, system, and computer program product for summarizing an implied volatility surface |
US20040034587A1 (en) * | 2002-08-19 | 2004-02-19 | Amberson Matthew Gilbert | System and method for calculating intra-period volatility |
US7809595B2 (en) | 2002-09-17 | 2010-10-05 | Jpmorgan Chase Bank, Na | System and method for managing risks associated with outside service providers |
AU2003272815A1 (en) | 2002-09-30 | 2004-04-19 | Goldman Sachs And Co. | System for analyzing a capital structure |
US7788154B1 (en) | 2002-10-02 | 2010-08-31 | Goldman Sachs & Co. | Methods, systems and securities for assuring a company an opportunity to sell stock after a specified time |
US7805347B1 (en) | 2002-10-07 | 2010-09-28 | Goldman Sachs & Co. | Methods, systems and securities for assuring a company an opportunity to sell stock after a specified time |
US20040073505A1 (en) * | 2002-10-09 | 2004-04-15 | James Foley Wright | Method for performing monte carlo risk analysis of business scenarios |
US20040122736A1 (en) | 2002-10-11 | 2004-06-24 | Bank One, Delaware, N.A. | System and method for granting promotional rewards to credit account holders |
US7769650B2 (en) | 2002-12-03 | 2010-08-03 | Jp Morgan Chase Bank | Network-based sub-allocation systems and methods for swaps |
US7401057B2 (en) * | 2002-12-10 | 2008-07-15 | Asset Trust, Inc. | Entity centric computer system |
US20040128228A1 (en) * | 2002-12-30 | 2004-07-01 | Fannie Mae | Servicer compensation system and method |
US20040128227A1 (en) * | 2002-12-30 | 2004-07-01 | Fannie Mae | Cash flow system and method |
US7904365B2 (en) | 2003-03-03 | 2011-03-08 | Itg Software Solutions, Inc. | Minimizing security holdings risk during portfolio trading |
US8032441B2 (en) | 2003-03-03 | 2011-10-04 | Itg Software Solutions, Inc. | Managing security holdings risk during portfolio trading |
US7593880B2 (en) * | 2003-03-19 | 2009-09-22 | General Electric Company | Methods and systems for analytical-based multifactor multiobjective portfolio risk optimization |
US7640201B2 (en) * | 2003-03-19 | 2009-12-29 | General Electric Company | Methods and systems for analytical-based multifactor Multiobjective portfolio risk optimization |
WO2004086183A2 (en) * | 2003-03-19 | 2004-10-07 | Ge Financial Assurance Holdings, Inc. | Methods and systems for analytical-based multifactor multiobjective portfolio risk optimization |
US10311412B1 (en) | 2003-03-28 | 2019-06-04 | Jpmorgan Chase Bank, N.A. | Method and system for providing bundled electronic payment and remittance advice |
US20040193469A1 (en) * | 2003-03-31 | 2004-09-30 | Cantor Index Llc | System and method for spread betting on a participant in a group of events |
US8353763B2 (en) | 2003-03-31 | 2013-01-15 | Cantor Index, Llc | System and method for betting on a participant in a group of events |
US8630947B1 (en) | 2003-04-04 | 2014-01-14 | Jpmorgan Chase Bank, N.A. | Method and system for providing electronic bill payment and presentment |
US7641549B2 (en) | 2003-04-11 | 2010-01-05 | Cantor Index Llc | Lottery and auction based tournament entry exchange platform |
US8306907B2 (en) | 2003-05-30 | 2012-11-06 | Jpmorgan Chase Bank N.A. | System and method for offering risk-based interest rates in a credit instrument |
US7853507B2 (en) * | 2003-06-23 | 2010-12-14 | Omx Technology Ab | Method for organizing financial instruments in a CSD-system |
US20050010481A1 (en) * | 2003-07-08 | 2005-01-13 | Lutnick Howard W. | Systems and methods for improving the liquidity and distribution network for illiquid items |
US7624068B1 (en) | 2003-08-18 | 2009-11-24 | Jpmorgan Chase Bank, N.A. | Method and system for dynamically adjusting discount rates for a card transaction |
US7953663B1 (en) | 2003-09-04 | 2011-05-31 | Jpmorgan Chase Bank, N.A. | System and method for financial instrument pre-qualification and offering |
US20050060208A1 (en) * | 2003-09-17 | 2005-03-17 | Gianantoni Raymond J. | Method for optimizing insurance estimates utilizing Monte Carlo simulation |
US8239323B2 (en) | 2003-09-23 | 2012-08-07 | Jpmorgan Chase Bank, N.A. | Method and system for distribution of unactivated bank account cards |
US7792717B1 (en) | 2003-10-31 | 2010-09-07 | Jpmorgan Chase Bank, N.A. | Waterfall prioritized payment processing |
US7702577B1 (en) | 2003-11-06 | 2010-04-20 | Jp Morgan Chase Bank, N.A. | System and method for conversion of initial transaction to final transaction |
US7814003B2 (en) | 2003-12-15 | 2010-10-12 | Jp Morgan Chase | Billing workflow system for crediting charges to entities creating derivatives exposure |
US7698198B2 (en) | 2004-01-16 | 2010-04-13 | Bgc Partners, Inc. | System and method for purchasing a financial instrument indexed to entertainment revenue |
US7567931B2 (en) | 2004-01-16 | 2009-07-28 | Bgc Partners, Inc. | System and method for forming a financial instrument indexed to entertainment revenue |
US7698184B2 (en) * | 2004-01-16 | 2010-04-13 | Bgc Partners, Inc. | System and method for trading a financial instrument indexed to entertainment revenue |
US8560414B2 (en) * | 2004-02-04 | 2013-10-15 | Research Affiliates, Llc | Synthetic ultralong inflation-protected separate trading of registered interest and principal of securities system, method and computer program product |
AU2005213425B2 (en) * | 2004-02-04 | 2010-09-02 | Research Affiliates, Llc | Separate trading of registered interest and principal of securities system, method and computer program product |
US7778905B2 (en) * | 2004-02-04 | 2010-08-17 | Research Affiliates, Llc | Separate trading of registered interest and principal of securities system, method and computer program product |
US7380707B1 (en) | 2004-02-25 | 2008-06-03 | Jpmorgan Chase Bank, N.A. | Method and system for credit card reimbursements for health care transactions |
US7422224B2 (en) * | 2004-04-13 | 2008-09-09 | Kimir Seatpost | Adjustable bicycle seat post assembly |
US20090043637A1 (en) * | 2004-06-01 | 2009-02-12 | Eder Jeffrey Scott | Extended value and risk management system |
US8554673B2 (en) | 2004-06-17 | 2013-10-08 | Jpmorgan Chase Bank, N.A. | Methods and systems for discounts management |
US8121944B2 (en) | 2004-06-24 | 2012-02-21 | Jpmorgan Chase Bank, N.A. | Method and system for facilitating network transaction processing |
US8996481B2 (en) | 2004-07-02 | 2015-03-31 | Goldman, Sach & Co. | Method, system, apparatus, program code and means for identifying and extracting information |
US8510300B2 (en) | 2004-07-02 | 2013-08-13 | Goldman, Sachs & Co. | Systems and methods for managing information associated with legal, compliance and regulatory risk |
US8762191B2 (en) | 2004-07-02 | 2014-06-24 | Goldman, Sachs & Co. | Systems, methods, apparatus, and schema for storing, managing and retrieving information |
US8442953B2 (en) | 2004-07-02 | 2013-05-14 | Goldman, Sachs & Co. | Method, system, apparatus, program code and means for determining a redundancy of information |
US7974895B1 (en) | 2004-07-16 | 2011-07-05 | Jp Morgan Chase Bank | System and method for developing finance rate information |
US8290862B2 (en) * | 2004-07-23 | 2012-10-16 | Jpmorgan Chase Bank, N.A. | Method and system for expediting payment delivery |
US8290863B2 (en) | 2004-07-23 | 2012-10-16 | Jpmorgan Chase Bank, N.A. | Method and system for expediting payment delivery |
US7392222B1 (en) | 2004-08-03 | 2008-06-24 | Jpmorgan Chase Bank, N.A. | System and method for providing promotional pricing |
US20060031104A1 (en) * | 2004-08-09 | 2006-02-09 | Gianantoni Raymond J | System and method for optimizing insurance estimates |
US20060041490A1 (en) * | 2004-08-18 | 2006-02-23 | Roberts John A | Optimizing investment strategies for long/short fund portfolios |
US8583529B2 (en) * | 2004-10-08 | 2013-11-12 | Mark Greenstein | Method of purchasing a product to avoid adverse selection |
US7792722B2 (en) * | 2004-10-13 | 2010-09-07 | Ares Capital Management Pty Ltd | Data processing system and method incorporating feedback |
US20060149664A1 (en) * | 2004-12-30 | 2006-07-06 | Jp Morgan Chase Bank | Marketing system and method |
US7890343B1 (en) | 2005-01-11 | 2011-02-15 | Jp Morgan Chase Bank | System and method for generating risk management curves |
US8630898B1 (en) | 2005-02-22 | 2014-01-14 | Jpmorgan Chase Bank, N.A. | Stored value card provided with merchandise as rebate |
US8713025B2 (en) | 2005-03-31 | 2014-04-29 | Square Halt Solutions, Limited Liability Company | Complete context search system |
US20060253360A1 (en) * | 2005-04-22 | 2006-11-09 | Lehman Brothers Inc. | Methods and systems for replicating an index with liquid instruments |
US7401731B1 (en) | 2005-05-27 | 2008-07-22 | Jpmorgan Chase Bank, Na | Method and system for implementing a card product with multiple customized relationships |
US7822682B2 (en) | 2005-06-08 | 2010-10-26 | Jpmorgan Chase Bank, N.A. | System and method for enhancing supply chain transactions |
US7676409B1 (en) | 2005-06-20 | 2010-03-09 | Jpmorgan Chase Bank, N.A. | Method and system for emulating a private label over an open network |
US7917417B2 (en) * | 2005-10-08 | 2011-03-29 | Dion Kenneth W | System and method for organizational and personal portfolio |
US7818238B1 (en) * | 2005-10-11 | 2010-10-19 | Jpmorgan Chase Bank, N.A. | Upside forward with early funding provision |
US8301529B1 (en) | 2005-11-02 | 2012-10-30 | Jpmorgan Chase Bank, N.A. | Method and system for implementing effective governance of transactions between trading partners |
US7962396B1 (en) | 2006-02-03 | 2011-06-14 | Jpmorgan Chase Bank, N.A. | System and method for managing risk |
US8408455B1 (en) | 2006-02-08 | 2013-04-02 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to both customers and non-customers |
US7784682B2 (en) | 2006-02-08 | 2010-08-31 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to both customers and non-customers |
US8498915B2 (en) | 2006-04-02 | 2013-07-30 | Asset Reliance, Inc. | Data processing framework for financial services |
US7753259B1 (en) | 2006-04-13 | 2010-07-13 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to both customers and non-customers |
US7707192B1 (en) | 2006-05-23 | 2010-04-27 | Jp Morgan Chase Bank, N.A. | Confidence index for assets |
US7734545B1 (en) | 2006-06-14 | 2010-06-08 | Jpmorgan Chase Bank, N.A. | Method and system for processing recurring payments |
EP2050062A4 (en) * | 2006-07-18 | 2009-07-22 | Pipeline Capital Llc | Interest rate swap index |
US7885885B1 (en) | 2006-08-15 | 2011-02-08 | Goldman Sachs & Co. | System and method for creating, managing and trading hedge portfolios |
US7636681B2 (en) | 2006-12-27 | 2009-12-22 | Cfph, Llc | Methods and systems for generating an investment trust comprising neutralized securities |
US7916925B2 (en) | 2007-02-09 | 2011-03-29 | Jpmorgan Chase Bank, N.A. | System and method for generating magnetic ink character recognition (MICR) testing documents |
WO2008131010A1 (en) | 2007-04-16 | 2008-10-30 | Cfph, Llc | Box office game |
US8676642B1 (en) | 2007-07-05 | 2014-03-18 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to financial account holders |
US20090024539A1 (en) * | 2007-07-16 | 2009-01-22 | Decker Christopher L | Method and system for assessing credit risk in a loan portfolio |
US8762270B1 (en) | 2007-08-10 | 2014-06-24 | Jpmorgan Chase Bank, N.A. | System and method for providing supplemental payment or transaction information |
US8417601B1 (en) | 2007-10-18 | 2013-04-09 | Jpmorgan Chase Bank, N.A. | Variable rate payment card |
US8788281B1 (en) | 2007-12-03 | 2014-07-22 | Jp Morgan Chase Bank, N.A. | System and method for processing qualified healthcare account related financial transactions |
US7766244B1 (en) | 2007-12-31 | 2010-08-03 | Jpmorgan Chase Bank, N.A. | System and method for processing transactions using a multi-account transactions device |
US8622308B1 (en) | 2007-12-31 | 2014-01-07 | Jpmorgan Chase Bank, N.A. | System and method for processing transactions using a multi-account transactions device |
US8078528B1 (en) | 2008-02-21 | 2011-12-13 | Jpmorgan Chase Bank, N.A. | System and method for providing borrowing schemes |
US7895102B1 (en) * | 2008-02-29 | 2011-02-22 | United Services Automobile Association (Usaa) | Systems and methods for financial plan benchmarking |
US7707089B1 (en) | 2008-03-12 | 2010-04-27 | Jpmorgan Chase, N.A. | Method and system for automating fraud authorization strategies |
US8478637B1 (en) | 2008-04-08 | 2013-07-02 | Jpmorgan Chase Bank, N.A. | Index for assessing discount potential |
WO2010000487A1 (en) | 2008-07-03 | 2010-01-07 | Thetaris Gmbh | Apparatus for energy-efficient estimation of a yield of a financial product |
US8112355B1 (en) | 2008-09-05 | 2012-02-07 | Jpmorgan Chase Bank, N.A. | Method and system for buyer centric dispute resolution in electronic payment system |
US9092447B1 (en) | 2008-10-20 | 2015-07-28 | Jpmorgan Chase Bank, N.A. | Method and system for duplicate detection |
US8391584B2 (en) | 2008-10-20 | 2013-03-05 | Jpmorgan Chase Bank, N.A. | Method and system for duplicate check detection |
US8386381B1 (en) | 2009-12-16 | 2013-02-26 | Jpmorgan Chase Bank, N.A. | Method and system for detecting, monitoring and addressing data compromises |
US8447641B1 (en) | 2010-03-29 | 2013-05-21 | Jpmorgan Chase Bank, N.A. | System and method for automatically enrolling buyers into a network |
US8529337B2 (en) | 2010-06-11 | 2013-09-10 | Longitude Llc | Enhanced parimutuel platform for wagering |
US8554631B1 (en) | 2010-07-02 | 2013-10-08 | Jpmorgan Chase Bank, N.A. | Method and system for determining point of sale authorization |
US8589288B1 (en) | 2010-10-01 | 2013-11-19 | Jpmorgan Chase Bank, N.A. | System and method for electronic remittance of funds |
US8543504B1 (en) | 2011-03-30 | 2013-09-24 | Jpmorgan Chase Bank, N.A. | Systems and methods for automated invoice entry |
US8543503B1 (en) | 2011-03-30 | 2013-09-24 | Jpmorgan Chase Bank, N.A. | Systems and methods for automated invoice entry |
US9697695B2 (en) | 2011-06-15 | 2017-07-04 | Longitude Llc | Enhanced parimutuel wagering filter |
US8532798B2 (en) | 2011-08-23 | 2013-09-10 | Longitude Llc | Predicting outcomes of future sports events based on user-selected inputs |
USD678653S1 (en) | 2012-07-19 | 2013-03-19 | Jpmorgan Chase Bank, N.A. | Drive-up financial transaction machine |
US8629872B1 (en) * | 2013-01-30 | 2014-01-14 | The Capital Group Companies, Inc. | System and method for displaying and analyzing financial correlation data |
USD690074S1 (en) | 2013-03-13 | 2013-09-17 | Jpmorgan Chase Bank, N.A. | Financial transaction machine |
US9058626B1 (en) | 2013-11-13 | 2015-06-16 | Jpmorgan Chase Bank, N.A. | System and method for financial services device usage |
US10475123B2 (en) * | 2014-03-17 | 2019-11-12 | Chicago Mercantile Exchange Inc. | Coupon blending of swap portfolio |
US10319032B2 (en) | 2014-05-09 | 2019-06-11 | Chicago Mercantile Exchange Inc. | Coupon blending of a swap portfolio |
US10810671B2 (en) | 2014-06-27 | 2020-10-20 | Chicago Mercantile Exchange Inc. | Interest rate swap compression |
US20160098795A1 (en) * | 2014-10-02 | 2016-04-07 | Mehmet Alpay Kaya | Path-Dependent Market Risk Observer |
EP3016058A1 (en) * | 2014-10-31 | 2016-05-04 | Chicago Mercantile Exchange, Inc. | Generating a blended fx portfolio |
US10609172B1 (en) | 2017-04-27 | 2020-03-31 | Chicago Mercantile Exchange Inc. | Adaptive compression of stored data |
US10229092B2 (en) | 2017-08-14 | 2019-03-12 | City University Of Hong Kong | Systems and methods for robust low-rank matrix approximation |
US11080785B1 (en) * | 2017-11-14 | 2021-08-03 | Chicago Mercantile Exchange Inc. | Listed options position compression system |
US11907207B1 (en) | 2021-10-12 | 2024-02-20 | Chicago Mercantile Exchange Inc. | Compression of fluctuating data |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1992015064A1 (en) * | 1991-02-25 | 1992-09-03 | The Prudential Insurance Company Of America | Method and apparatus for volatility analysis of interest rate sensitive financial instruments |
EP0573991A1 (en) * | 1992-06-10 | 1993-12-15 | Cantor Fitzgerald | Fixed income portfolio data processor and method for using same |
EP0686926A2 (en) * | 1994-05-24 | 1995-12-13 | Ron S. Dembo | Method and apparatus for optimal portfolio replication |
Family Cites Families (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4346442A (en) | 1980-07-29 | 1982-08-24 | Merrill Lynch, Pierce, Fenner & Smith Incorporated | Securities brokerage-cash management system |
IT8448723A0 (en) | 1983-08-13 | 1984-02-13 | British Aerospace | IF IN CORRESPONDENCE TO A SERIES SYSTEM FOR ALLOCATION OF RESOURCES REQUESTS AND METHOD FOR DETERMINING THE OPTIMAL DISTRIBUTION OF RESOURCES |
US4752877A (en) | 1984-03-08 | 1988-06-21 | College Savings Bank | Method and apparatus for funding a future liability of uncertain cost |
US4722055A (en) | 1984-03-08 | 1988-01-26 | College Savings Bank | Methods and apparatus for funding future liability of uncertain cost |
US4642768A (en) | 1984-03-08 | 1987-02-10 | Roberts Peter A | Methods and apparatus for funding future liability of uncertain cost |
US4694397A (en) | 1984-12-27 | 1987-09-15 | The Advest Group, Inc. | Banking/brokerage computer interface system |
US4674044A (en) | 1985-01-30 | 1987-06-16 | Merrill Lynch, Pierce, Fenner & Smith, Inc. | Automated securities trading system |
US4744028A (en) | 1985-04-19 | 1988-05-10 | American Telephone And Telegraph Company, At&T Bell Laboratories | Methods and apparatus for efficient resource allocation |
US4744026A (en) | 1986-04-11 | 1988-05-10 | American Telephone And Telegraph Company, At&T Bell Laboratories | Methods and apparatus for efficient resource allocation |
US4744027A (en) | 1986-08-22 | 1988-05-10 | American Telephone And Telegraph Company, At&T Bell Laboratories | Method and apparatus for optimizing system operational parameters |
US4953085A (en) | 1987-04-15 | 1990-08-28 | Proprietary Financial Products, Inc. | System for the operation of a financial account |
US5101353A (en) | 1989-05-31 | 1992-03-31 | Lattice Investments, Inc. | Automated system for providing liquidity to securities markets |
US5148365A (en) | 1989-08-15 | 1992-09-15 | Dembo Ron S | Scenario optimization |
JPH0377163A (en) | 1989-08-19 | 1991-04-02 | Fujitsu Ltd | Risk minimized portfolio selection device using mutually coupled network |
JPH03103966A (en) | 1989-09-19 | 1991-04-30 | Fujitsu Ltd | Risk minimizing portfolio selection device utilizing mutual connection type network |
JPH03189862A (en) | 1989-12-20 | 1991-08-19 | Fujitsu Ltd | Risk minimized portfolio selector using interconnection type network |
US5193056A (en) | 1991-03-11 | 1993-03-09 | Signature Financial Group Inc. | Data processing system for hub and spoke financial services configuration |
EP0806017A4 (en) * | 1994-12-13 | 2000-08-30 | Fs Holdings Inc | A system for receiving, processing, creating, storing and disseminating investment information |
US6278981B1 (en) * | 1997-05-29 | 2001-08-21 | Algorithmics International Corporation | Computer-implemented method and apparatus for portfolio compression |
-
1998
- 1998-05-28 US US09/084,923 patent/US6278981B1/en not_active Expired - Lifetime
- 1998-05-29 EP EP98923949A patent/EP0985188B1/en not_active Expired - Lifetime
- 1998-05-29 AU AU76330/98A patent/AU740917B2/en not_active Expired
- 1998-05-29 DE DE69820873T patent/DE69820873D1/en not_active Expired - Lifetime
- 1998-05-29 JP JP50003999A patent/JP2002500789A/en active Pending
- 1998-05-29 CA CA002290368A patent/CA2290368A1/en not_active Abandoned
- 1998-05-29 WO PCT/CA1998/000519 patent/WO1998054666A1/en active IP Right Grant
- 1998-05-29 AT AT98923949T patent/ATE257256T1/en not_active IP Right Cessation
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1992015064A1 (en) * | 1991-02-25 | 1992-09-03 | The Prudential Insurance Company Of America | Method and apparatus for volatility analysis of interest rate sensitive financial instruments |
EP0573991A1 (en) * | 1992-06-10 | 1993-12-15 | Cantor Fitzgerald | Fixed income portfolio data processor and method for using same |
EP0686926A2 (en) * | 1994-05-24 | 1995-12-13 | Ron S. Dembo | Method and apparatus for optimal portfolio replication |
Non-Patent Citations (2)
Title |
---|
AZHAR S ET AL: "Data compression techniques for stock market prediction", PROCEEDINGS OF IEEE DATA COMPRESSION CONFERENCE (DCC'94), SNOWBIRD, UT, USA, 29 March 1994 (1994-03-29) - 31 March 1994 (1994-03-31), ISBN 0-8186-5637-9, 1994, Los Alamitos, CA, USA, IEEE Comput. Soc. Press, USA, pages 72 - 82, XP002078323 * |
KOFLOWITZ L: "Hedging tools provide portfolio security blanket", WALL STREET COMPUTER REVIEW, USA, vol. 6, no. 6, March 1989 (1989-03-01), ISSN 0738-4343, pages 42 - 43, 45 - 46, 48, 50, 80 - 81, XP002078322 * |
Cited By (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6278981B1 (en) * | 1997-05-29 | 2001-08-21 | Algorithmics International Corporation | Computer-implemented method and apparatus for portfolio compression |
US7062458B2 (en) | 1997-12-02 | 2006-06-13 | Financial Engines | User Interface for a financial advisory system that allows an end user to interactively explore tradeoffs among input decisions |
US7774257B2 (en) | 1997-12-02 | 2010-08-10 | Financial Engines, Inc. | User interface for a financial advisory system |
US7016870B1 (en) | 1997-12-02 | 2006-03-21 | Financial Engines | Identifying a recommended portfolio of financial products for an investor based upon financial products that are available to the investor |
US7983975B2 (en) | 1997-12-02 | 2011-07-19 | Financial Engines, Inc. | Financial advisory system |
US7813989B2 (en) | 1997-12-02 | 2010-10-12 | Financial Engines, Inc. | Financial goal planning and analysis system |
US7788155B2 (en) | 1997-12-02 | 2010-08-31 | Financial Engines, Inc. | Financial advisory system |
US7321871B2 (en) | 1998-09-11 | 2008-01-22 | Financial Engines, Inc. | Enhancing utility and diversifying model risk in a portfolio optimization framework |
US6292787B1 (en) | 1998-09-11 | 2001-09-18 | Financial Engines, Inc. | Enhancing utility and diversifying model risk in a portfolio optimization framework |
WO2000065418A3 (en) * | 1999-04-21 | 2001-04-12 | Billiotte Jean Marie | Method and automatic control for regulating a multiple-stage industrial production controlling random chained stress, application to noise and value at risk control of a clearing house |
FR2792746A1 (en) * | 1999-04-21 | 2000-10-27 | Ingmar Adlerberg | Control of staged production flow in response to random pressures on production stages using the 'value at risk' method |
WO2000065418A2 (en) * | 1999-04-21 | 2000-11-02 | Billiotte Jean Marie | Method and automatic control for regulating a multiple-stage industrial production controlling random chained stress, application to noise and value at risk control of a clearing house |
US7395236B2 (en) | 1999-06-03 | 2008-07-01 | Algorithmics Software Llc | Risk management system and method providing rule-based evolution of a portfolio of instruments |
WO2001055812A3 (en) * | 2000-01-28 | 2002-08-08 | Pi Eta Consulting Company Pte | Fully flexible financial instrument pricing system with intelligent user interfaces |
WO2001055812A2 (en) * | 2000-01-28 | 2001-08-02 | Pi Eta Consulting Company Pte Ltd | Fully flexible financial instrument pricing system with intelligent user interfaces |
US7249081B2 (en) | 2000-02-23 | 2007-07-24 | Financial Engines, Inc. | Load aware optimization |
US7447652B2 (en) | 2001-05-31 | 2008-11-04 | Ge Corporate Financial Services, Inc. | Methods and systems for portfolio cash flow valuation |
US8200561B1 (en) | 2002-03-29 | 2012-06-12 | Financial Engines, Inc. | Tax-aware asset allocation |
WO2012008847A1 (en) * | 2010-07-16 | 2012-01-19 | Odd Rune Eikemo Fitje | Control system for controlling complex facilities hosting multiple concurrent processes |
Also Published As
Publication number | Publication date |
---|---|
AU7633098A (en) | 1998-12-30 |
DE69820873D1 (en) | 2004-02-05 |
JP2002500789A (en) | 2002-01-08 |
ATE257256T1 (en) | 2004-01-15 |
CA2290368A1 (en) | 1998-12-03 |
EP0985188B1 (en) | 2004-01-02 |
US6278981B1 (en) | 2001-08-21 |
EP0985188A1 (en) | 2000-03-15 |
AU740917B2 (en) | 2001-11-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP0985188B1 (en) | Computer-implemented method and apparatus for portfolio compression | |
Pástor et al. | Dissecting green returns | |
Linsmeier et al. | Risk measurement: An introduction to value at risk | |
Daskalaki et al. | Diversification benefits of commodities: A stochastic dominance efficiency approach | |
Boyle | Options: A monte carlo approach | |
Penza et al. | Measuring market risk with value at risk | |
US7016873B1 (en) | System and method for tax sensitive portfolio optimization | |
US8589276B2 (en) | Using accounting data based indexing to create a portfolio of financial objects | |
USRE44362E1 (en) | Using accounting data based indexing to create a portfolio of financial objects | |
US8392321B2 (en) | System and method for using diversification spreading for risk offset | |
AU2012298732A1 (en) | Using accounting data based indexing to create a portfolio of financial objects | |
Gourieroux et al. | Affine models for credit risk analysis | |
US7698196B1 (en) | Method and system for modeling and benchmarking private equity and applications of same | |
EP1563422A1 (en) | Electronic interpretation of financials | |
Gourieroux et al. | International money and stock market contingent claims | |
Jarrow et al. | A unified approach for pricing contingent claims on multiple term structures | |
Shearer et al. | Shortcomings of risk ratings impede success in commercial lending | |
Karimi et al. | Value of financial flexibility and quantitative aspects of net working capital: Evidence from Tehran Stock Exchange | |
Neave et al. | Claims-based asset pricing: theory and application | |
Huang et al. | The Hedging Policies of BP with the Systematical Analysis | |
Aguais et al. | Enterprise Credit Risk Management | |
Yoshino | Market risk and volatility in the Brazilian stock market | |
Kim | A Black-Scholes-integrated Gaussian Process Model for American Option Pricing | |
Obadire | Empirical Analysis of the Effects of Macroeconomic Variables on the Equity Market Risk Premium in South Africa | |
Zhang et al. | The Pricing of Multiple Line P&C Insurance Based on the Full Information Underwriting Beta |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AK | Designated states |
Kind code of ref document: A1 Designated state(s): AL AM AT AU AZ BA BB BG BR BY CA CH CN CU CZ DE DK EE ES FI GB GE GH GM GW HU ID IL IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT UA UG US UZ VN YU ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: A1 Designated state(s): GH GM KE LS MW SD SZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN ML MR NE SN TD TG |
|
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
ENP | Entry into the national phase |
Ref document number: 2290368 Country of ref document: CA Kind code of ref document: A Ref document number: 2290368 Country of ref document: CA |
|
WWE | Wipo information: entry into national phase |
Ref document number: 76330/98 Country of ref document: AU |
|
ENP | Entry into the national phase |
Ref document number: 1999 500039 Country of ref document: JP Kind code of ref document: A |
|
WWE | Wipo information: entry into national phase |
Ref document number: 1998923949 Country of ref document: EP |
|
WWP | Wipo information: published in national office |
Ref document number: 1998923949 Country of ref document: EP |
|
REG | Reference to national code |
Ref country code: DE Ref legal event code: 8642 |
|
WWG | Wipo information: grant in national office |
Ref document number: 76330/98 Country of ref document: AU |
|
WWG | Wipo information: grant in national office |
Ref document number: 1998923949 Country of ref document: EP |