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WO2003067395A2 - Creation de scenarios portant sur le risque de credit de detail - Google Patents

Creation de scenarios portant sur le risque de credit de detail Download PDF

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Publication number
WO2003067395A2
WO2003067395A2 PCT/US2003/003677 US0303677W WO03067395A2 WO 2003067395 A2 WO2003067395 A2 WO 2003067395A2 US 0303677 W US0303677 W US 0303677W WO 03067395 A2 WO03067395 A2 WO 03067395A2
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WIPO (PCT)
Prior art keywords
portfolio
exogenous
modeling
component
residual
Prior art date
Application number
PCT/US2003/003677
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English (en)
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WO2003067395A3 (fr
Inventor
Joseph L. Breeden
Michael A. Smith
R. David Franklin
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Strategic Analytics Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Strategic Analytics Inc. filed Critical Strategic Analytics Inc.
Priority to AU2003217339A priority Critical patent/AU2003217339A1/en
Publication of WO2003067395A2 publication Critical patent/WO2003067395A2/fr
Publication of WO2003067395A3 publication Critical patent/WO2003067395A3/fr

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    • 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
    • G06Q40/08—Insurance

Definitions

  • Monte Carlo is a well-known and useful technology, but when applied to total portfolio performance, it is unable to account for any of the portfolio and environmental changes mentioned above.
  • Industry comparison is a logical approach to bolstering the limited internal data by looking at performance of other retail lending institutions.
  • industry comparison is crude at best because there is no clear approach to calibrating industry-wide data to an individual retail lending institution's portfolio. The industry average can be changing in composition as well as the individual portfolio.
  • a method for modeling a retail lending portfolio includes providing vintage performance data for a retail lending portfolio in which the portfolio has a key portfolio driver. It further includes selecting a functional form that relates maturation aspects and exogenous aspects of the provided data; decomposing the provided data using the selected functional form to generate a portfolio maturation component, a portfolio exogenous component and a vintage calibration parameter.
  • the portfolio exogenous component includes a known exogenous driver.
  • the method further includes extracting the known exogenous driver from the portfolio exogenous component to generate a residual exogenous component; computing monthly changes in the residual exogenous component; measuring the distribution of monthly changes in the residual exogenous component; generating a number of random potential future scenarios for the residual exogenous component using the measured distribution of monthly changes; generating a number of potential future scenarios for the exogenous component using the number of generated potential future scenarios for the residual exogenous component; and generating a number of forecasts for the key portfolio driver using the number of exogenous scenarios.
  • the known exogenous driver may be a management action element, a seasonality element or an underlying trend.
  • the underlying trend may be competitve pressure or macroeconomic, among other things.
  • the method may further include measuring autocorrelation in the residual exogenous component and using the measured autocorrelation in the generation of the number of random potential future scenarios for the residual component.
  • the number of random potential future scenarios for the residual exogenous component may be generated using a Monte Carlo approach.
  • the method may further include applying a business simulation layer to the generated plurality of forecasts for the key portfolio driver to produce a number of potential future forecasts for a portfolio performance measure.
  • the portfolio portfolio performance measure may be, among others, potential revenues, potential losses, potential profits, value at risk, earnings at risk, economic capital, return on capital, return on equity, a risk adjusted performance measurement.
  • the selected functional form fo the method may be additive.
  • the selected functional form may also be multiplicative.
  • a system for modeling a retail lending portfolio includes a data storage device having vintage performance data for a retail lending portfolio stored thereon.
  • the portfolio has a key portfolio driver.
  • the system also includes a computing device having a modeling engine stored thereon, wherein the modeling engine has a selected functional form programmed therein that relates maturation aspects and exogenous aspects of data.
  • the vintage performance data is retrieved from the data storage device and the data is processed to decompose the data to generate a portfolio maturation component, a portfolio exogenous component and a vintage calibration parameter; wherein the portfolio exogenous component includes a known exogenous driver.
  • the executed modeling enginge further extracts the known exogenous driver from the portfolio exogenous component to generate a residual exogenous component; computes monthly changes in the residual exogenous component; measures the distribution of monthly changes in the residual exogenous component; generates a number of random potential future scenarios for the residual exogenous component using the measured distribution of monthly changes; generates a number of potential future scenarios for the exogenous component using the number of generated potential future scenarios for the residual exogenous component; and generates a number of forecasts for the key portfolio driver using the number of exogenous scenarios.
  • a computer-readable medium encoded with a set of instructions for modeling a retail lending portfolio, wherein the portfolio has a key portfolio driver and wherein the instructions have a selected functional form programmed therein that relates maturation aspects and exogenous aspects of provided vintage performance data.
  • the instructions When the instructions are executed, the instructions perform a method which includes retrieving vintage performance data for a retail lending portfolio; decomposing the provided data using the selected functional form to generate a portfolio maturation component, a portfolio exogenous component and a vintage calibration parameter, wherein the portfolio exogenous component includes a known exogenous driver; extracting the known exogenous driver from the portfolio exogenous component to generate a residual exogenous component; computing monthly changes in the residual exogenous component; measuring the distribution of monthly changes in the residual exogenous component; generating a number of random potential future scenarios for the residual exogenous component using the measured distribution of monthly changes; generating a number of potential future scenarios for the exogenous component using the number of generated potential future scenarios for the residual exogenous component; and generating a number of forecasts for the key portfolio driver using the number of exogenous scenarios.
  • FIG 1 illustrates an exemplary operating environment of the present invention
  • FIG 2 is a visualization of how dual-time dynamics decomposes historical data into age-based (maturation) and time-based (exogenous) effects
  • FIG 3 A depicts a visualization of how the modeling approach of the present invention decomposes historical data into age-based (maturation) and time-based
  • FIG 3B is an exemplary visualization of a decomposed age-based (maturation) curve
  • FIG 3C is an exemplary visualization of a decomposed time-based (exogenous) curve
  • FIG 4 shows examples of autocorrelation curves measured on actual consumer credit loss data with the legends indicating the risk scaling factors for these curves;
  • FIG 5 illustrates an exemplary distribution of possible future losses as a function of amount.
  • the mean of the distribution determines expected losses, and unexpected losses are set by a separate bond ratings goal;
  • FIG 6 is an exemplary exogenous curve generated by the dual-time dynamics engine
  • FIG 7 is an exemplary polar plot of the raw exogenous curve illustrating two sudden level shifts
  • FIG 8 is an exemplary exogenous curve with management actions extracted
  • FIG 9 is an exemplary polynomial fit to the exogenous curve without management effects
  • FIG 10 is an exemplary detrended exogenous curve produced by removing the polynomial trend and management effects
  • FIG 11 is an exemplary polar plot of the detrended curve highlighting seasonal effects with the "seasonality" curve showing a smoothed curve generated with consideration of the uncertainty present in this small of a data set;
  • FIG 12 is an exemplary plot of the average seasonality with error bars and the smoothed curve created via the constrained nearest-neighbor algorithm
  • FIG 13 is an exemplary autocorrelation structure of the monthly changes in the exogenous curve without seasonality or management effects
  • FIG 14 is an illustration of the cumulative distribution of monthly changes in the exogenous curve without seasonality and management changes;
  • FIG 15 shows many random extrapolations of monthly changes preserving the historical structures;
  • FIG 16 shows the simulated exogenous curves from the monthly changes in
  • FIG 17 shows the simulated exogenous curves with the seasonality added
  • FIG 18 is the distribution of possible future losses obtained by simulating the portfolio with the randomly generated exogenous curves shown in Figure 17.
  • the modeling approach of the present invention for decomposing historical data into age-based and time-based components generally called dual-time dynamics is depicted.
  • the modeling approach of the present invention may be implemented in any form most practical for the user.
  • the modeling approach is implemented as a modeling engine 100 resident on a computing device 102 that interacts with a portfolio database 104 which may be external to the computing device 102 as depicted or may be resident within the computing device 102.
  • the modeling approach of the present invention may be implemented in other forms as well, such as a set of stored instructions on a computer-readable medium. Dual-time dynamics is more fully described in co- pending application Serial No.
  • Dual-time dynamics decomposes historical data into tenure-based and time- based components. Dual-time dynamics derives and interprets the natural, usually nonlinear, maturation process for segments of customer accounts. By knowing what should happen under normal conditions, dual-time dynamics is able to quantify the unexpected components of performance and relate them to economic, management, competitive, or other exogenous factors. This decomposition is a critical first step to understanding the underlying drivers of consumer behavior.
  • the dual-time dynamics system begins with actual historical data for an institution. From this data, dual-time dynamics learns the nonlinear functions governing the way the customer relationship matures with time. Simultaneously, dual-time dynamics quantifies the impact of exogenous variables on these accounts.
  • Dual-time dynamics technology is uniquely capable of quantifying exogenous factors in the presence of changing portfolio demographics, policies, and competitors.
  • FIG. 2 illustrates the dual-time dynamics approach. Rather than model the historical data with a single model, the dual-time dynamics engine creates two distinct models. Dual-time dynamics decomposes the historical performance of key portfolio drivers into their constituent parts: maturation curves, exogenous curves, and vintage sensitivities.
  • the maturation curve describes the intrinsic consumer behavior over the lifetime of a loan.
  • the exogenous curve includes the impact of exogenous drivers, such as management actions, seasonality, underlying trends which may include competitive pressures and the macroeconomic environment, upon the portfolio. Some of these exogenous drivers may be known.
  • the vintage sensitivities measure the quality of new originations.
  • Table 1 shows the minimum set of variables needed to capture all the structure in portfolio losses for installment and line-of-credit loans.
  • the prepayment rate needs to be added to the installment modeling and the revolving balance rate and possibly some fee generation rates need to be added to the line-of-credit modeling.
  • costs such as call center activity, need to be added to both loan types.
  • Table 1 depicts exemplary key portfolio drivers for loss forecasting for installment and line-of-credit retail loans.
  • the maturation model (U) 200 extracts the tenure- based component of performance while filtering tenure dependence from the input to the exogenous model.
  • the exogenous model (V) 210 exfracts the date-based component of performance while filtering date dependence from the input to the maturation model.
  • Models U and V may be tabulated functions, neural networks, or other non-linear modeling techniques. Specific vintages are modeled with U, V, and a set of sensitivity parameters, ⁇ , 212. The modeling and filtering process for U and V and vintage sensitivities iterates until all three models have converged. After convergence is attained, models similar to those depicted in Figures 3A-C may be generated.
  • a second-stage decomposition of the exogenous curve may exfract exogenous drivers, such as management actions, seasonality and underlying trends, to determine a residual variability.
  • the extracted exogenous drivers are typically known.
  • Management actions typically appear in an exogenous curve as spikes or sudden level shifts.
  • To extract these spikes from the data either (1) management must tag specific spikes as having been generated by management, or (2) data from other retail lending institutions are run through dual-time dynamics and their exogenous curves are compared to identify the spikes which are unique to the individual institution.
  • Seasonal effects are those things that happen in the same month each year. Christmas is the classic example, particularly because it impacts consumer spending so dramatically. Strong underlying trends can create biases when trying to quantify the seasonality. A linear trend can be removed by adjusting the monthly seasonal adjustments to sum to zero. Nonlinear trends must be fit directly and subtracted from the data prior to measuring the seasonality.
  • the seasonality is measured. Quantifying seasonality is simply a matter of computing the average of all values available for a given calendar month. Often times only short data sets are available. In these instances, it is important to compute error bars as well. The final seasonality to be used can be smoothed relative to the error bars if changes month-to-month are assumed to be small.
  • Dual-time dynamics are used in the present invention to decompose key portfolio drivers into their underlying causes.
  • methods such as Monte Carlo, may be used with the decomposed portfolio drivers to automatically generate many possible future environments (exogenous curves). With those environmental scenarios generated, in an embodiment of the present invention, the future performance of each key driver for each scenario is forecasted.
  • the key drivers may be combined to create revenue and loss forecasts for each scenario. From the range of possible future losses or revenue, a range of portfolio performance metrics including economic capital may be computed.
  • the autocorrelation in the exogenous curve, Pr is measured after removing seasonality and management actions, but with the trend left in. To make the autocorrelation estimate accurate, it is helpful to have either many years of historical data or data from multiple regions for comparison.
  • r is randomly generated from the observed distribution of changes
  • x * ' * are the randomly generated monthly changes
  • pp l ⁇ l is the set of parameters to be optimized so as to recreate the autocorrelation structure relative to the previous " months.
  • simple gradient descent optimization is used to solve this problem, but other approaches may be applied as well.
  • Equation 1 can alter the distribution under poor choices of parameters.
  • KS Kolmogorov-Smirnov
  • L is an L-norm; a distance metric.
  • the L -norm is a usual choice in problems with moderate noise levels.
  • Forecast Portfolio Performance With many realizations of possible future exogenous curves generated, numerous forecasts of the total portfolio behavior may be created. Each simulated exogenous curve is combined with the maturation process of the vintages, the planned new bookings, and any planned policy changes. The result is a set of forecasts of the key rates driving portfolio performance, which is then run through a business simulation layer to produce forecast distributions of possible future revenues, losses, and profits. This distribution accounts for the expected maturation behavior of the portfolio while overlaying many realizations of unknown external impacts. Such a distribution appears to be ideally suited for use by risk management and capital allocation systems. [0057] In addition to the Monte Carlo approach of creating many random futures, specifically chosen scenarios may be generated to consider possibilities that may not be fully covered otherwise.
  • Compute Performance Metrics Dual-time dynamics provides the unique ability to enhance portfolio metrics with predictions of a distribution of possible outcomes.
  • the distribution of possible future outcomes incorporates the maturation process for accounts, seasonality, and a range of exogenous effects. It is a distribution of where the current portfolio might go rather than where it has been. Being able to create forward-looking distributions of portfolio performance opens the door to a range of possible statistical measures of future performance.
  • Value at Risk Conceptually, Value at Risk is intended to be a forward-looking statistic. If one can accurately estimate the current value, the sensitivity to change, and the probability of change, the potential loss in value can be estimated. Any model for these quantities must be based in large part on historical experience. Most such approaches currently use simplified models or even intuitive estimates.
  • Value at Risk is meant to capture the potential for loss of value in the portfolio.
  • all portfolios are expected to change value at least in part due to the expected maturation process, not just from unexpected events.
  • the concept of Value at Risk can be improved by improving either the definition of current value or by broadening the range of risks to include expected threats such as maturation.
  • Many possible portfolio sensitivities in a distribution of possible outcomes may be incorporated. Each of these time series can be used to measure the current value of the portfolio using a Net Present Value calculation.
  • VaR k ⁇ NPV -Avg NPV
  • Earnings are usually measured as revenue net of credit losses, operating expenses, and cost of funds.
  • Return on Capital Return on Capital is the ratio of profit generated on a pool of loans to the capital reserves required to support those loans.
  • the current standard for regulatory capital is 8% of assets, although this will change under the Basel II Accord such that the estimate of economic capital above will become a more appropriate measure.
  • the basic Return on Capital formula is not altered, but the forward looking estimates of expected return and capital requirements are replaced as described above. return
  • Return on Equity Capital is greater than or equal to equity, depending upon subordinated debt. Subordinated debt is counted as capital but not equity. In an embodiment of the present invention, the formula for Return on Equity will not change, but the measure of capital is set as described above in the paragraph entitled "Economic (or Risk) Capital”.
  • RAPM Risk Adjusted Performance Measurement
  • RAPM Risk Adjusted Performance Measurement
  • Dependence upon Value at Risk or Earnings at Risk may also be replaced with the version of these statistics generated by the embodiments of the present invention described above.
  • RoRAA, RARoA, RoRAC(I), RoRAC(II), RoRAC(III), and RARoC are all variations on the general RAPM framework. As such, they can all be extended similarly using the simulation techniques described above to provide forward-looking expectations.
  • R R A A return ⁇ (regulatory capital- interest rate to borrow) assets
  • Example 1 Modeling Credit Card Performance An embodiment of the current invention was illustrated by analyzing a specific credit card portfolio.
  • the credit card portfolio data was analyzed with dual-time dynamics to produce a maturation curve, an exogenous curve, and vintage sensitivities. The steps shown below were followed in analyzing the exogenous curve 300 ( Figure 6), creating the needed scenarios, and generating a future loss distribution for the purposes of setting economic capital.
  • Management Impacts The polar plot 302 in Figure 7 of the exogenous curve 300 reveals two dramatic level shifts. A polar plot by calendar month was used because it reduces confusion from seasonality. From the plot 302, it is shown as precisely as possible when these shifts occurred.
  • the exogenous curve can then be shifted by amounts that minimize the discontinuities at these points, as illustrated by the shifted curve 304 in Figure 8.
  • the shifts are 0.36 at Feb-98 and 0.47 at Jan-99.
  • Seasonality Nonlinear trends such as in Figure 9 must be fit directly and subtracted from the data prior to measuring the seasonality.
  • Figure 9 shows a polynomial fit 306 to the trend in the exogenous curve and
  • Figure 10 shows the detrended curve 307.
  • the smoothed curve shown in Figure 12 is a simple nearest-neighbor smoothing constrained not to drift more than one standard deviation from the original average.
  • Figure 11 shows the smoothed curve 308 relative to the underlying data 310 in a polar plot.
  • this embodiment of the invention utilized a random process that matched the autocorrelation and distribution historically observed to randomly generate many possible scenarios of the future of the exogenous curve.
  • Table 2 shows the result of performing a gradient descent optimization to learn the parameters for Equation 1 that would preserve the autocorrelation with the distribution from Figure 14.
  • Securitization Most credit card securitizations use a dynamic pool. A dynamic pool replaces accounts performing below a promised level with better performing accounts. This has the effect of keeping the risk on the lending institution's books.
  • Dynamic pools are used because of the inability of institutions to predict future performance.
  • Future Losses a fixed pool of accounts can be created for securitization purposes.
  • a pool of accounts is securitized such that several different risk categories are created.
  • the purchasers assume the risk of under-performance, but that risk will be priced into the securitization using the distribution of future losses.
  • the approach for doing this is the classic ABS structure.
  • the only piece that has been missing in the past is the creation of a reliable distribution of future losses.

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Abstract

L'invention concerne un procédé de création de scénarios portant sur le risque de crédit de détail pour un portefeuille. Une forme fonctionnelle choisie est utilisée pour décomposer des données de performances d'époque en un élément de maturation, un élément exogène et des paramètres d'étalonnage d'époque pour ledit portefeuille. Des pilotes exogènes connus sont extraits dudit élément exogène pour créer un élément exogène résiduel. Les changements mensuels dudit élément exogène résiduel sont calculés, et la répartition desdits changements mensuels d'élément exogène résiduel est mesurée. Ces informations sont ensuite utilisées pour créer plusieurs scénarios futurs potentiels pour l'élément exogène résiduel, et créer ainsi plusieurs prévisions pour des pilotes de portefeuilles clés.
PCT/US2003/003677 2002-02-08 2003-02-07 Creation de scenarios portant sur le risque de credit de detail WO2003067395A2 (fr)

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AU2003217339A AU2003217339A1 (en) 2002-02-08 2003-02-07 Retail lending risk related scenario generation

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7835958B1 (en) * 2003-08-20 2010-11-16 Teradata Us, Inc. Life-time value financial processing in a relational database management system
US7835959B1 (en) * 2003-08-20 2010-11-16 Teradata Us, Inc. Future value attrition for life-time value financial processing in a relational database management system
US7844526B1 (en) * 2003-08-20 2010-11-30 Teradata Us, Inc. Net present value attrition for Life-Time Value financial processing in a relational database management system
US7844516B1 (en) * 2003-08-20 2010-11-30 Teradata Us, Inc. Future value propensity for life-time value financial processing in a relational database management system
US7844515B1 (en) * 2003-08-20 2010-11-30 Teradata Us, Inc. Net present value forecast for life-time value financial processing in a relational database management system
US11461841B2 (en) 2018-01-03 2022-10-04 QCash Financial, LLC Statistical risk management system for lending decisions

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6275814B1 (en) * 1996-11-27 2001-08-14 Investment Strategies Network Investment portfolio selection system and method
US6078904A (en) * 1998-03-16 2000-06-20 Saddle Peak Systems Risk direct asset allocation and risk resolved CAPM for optimally allocating investment assets in an investment portfolio
US6236973B1 (en) * 1999-06-02 2001-05-22 Greg Dillard Apparatus and method for providing collateral construction loan insurance coverage
US8145556B2 (en) * 2000-04-10 2012-03-27 Tealdi Daniel A Online mortgage approval and settlement system and method therefor

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7835958B1 (en) * 2003-08-20 2010-11-16 Teradata Us, Inc. Life-time value financial processing in a relational database management system
US7835959B1 (en) * 2003-08-20 2010-11-16 Teradata Us, Inc. Future value attrition for life-time value financial processing in a relational database management system
US7844526B1 (en) * 2003-08-20 2010-11-30 Teradata Us, Inc. Net present value attrition for Life-Time Value financial processing in a relational database management system
US7844516B1 (en) * 2003-08-20 2010-11-30 Teradata Us, Inc. Future value propensity for life-time value financial processing in a relational database management system
US7844515B1 (en) * 2003-08-20 2010-11-30 Teradata Us, Inc. Net present value forecast for life-time value financial processing in a relational database management system
US11461841B2 (en) 2018-01-03 2022-10-04 QCash Financial, LLC Statistical risk management system for lending decisions

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AU2003217339A8 (en) 2003-09-02
WO2003067395A3 (fr) 2004-04-01

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