Retail lending risk related scenario generation

• US 20030225659A1
• Filed: 02/07/2003
• Published: 12/04/2003
• Est. Priority Date: 02/22/2000
• Status: Active Grant

• Current Assignee: Argus Information and Advisory Services Incorporated (Verisk Analytics Incorporated)
• Original Assignee: Strategic Analytics, Inc. (Verisk Analytics Incorporated)
• Inventors: Smith, Michael A., Breeden, Joseph L., Franklin, R. David
• Granted Patent: US 7,469,227 B2
• Time in Patent Office: Days
• US Class Current: 705/36
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Generation of risk-related retail lending portfolio scenarios is disclosed. A selected functional form is used to decompose vintage performance data into a maturation component, an exogenous component and vintage calibration parameters for the portfolio. Known exogenous drivers are extracted from the exogenous component to create a residual exogenous component. Monthly changes in the residual exogenous component are computed, and the distribution of monthly changes in the residual exogenous component is measured. This information is used to generate a number of random potential future scenarios for the residual exogenous component and, utlimately, for the generation of a number of forecasts for key portfolio drivers.

Claims

1. A method for modeling a retail lending portfolio comprising:

providing vintage performance data for a retail lending portfolio, wherein the portfolio has at least one key portfolio driver;

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 at least one vintage calibration parameter, wherein the portfolio exogenous component includes at least one known exogenous driver;

extracting the at least one 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 plurality of random potential future scenarios for the residual exogenous component using the measured distribution of monthly changes;

generating a plurality of potential future scenarios for the exogenous component using the plurality of generated potential future scenarios for the residual exogenous component; and

generating a plurality of forecasts for the at least one key portfolio driver using the plurality of exogenous scenarios.

2. The method for modeling a retail lending portfolio of claim 1 wherein the at least one known exogenous driver is a management action element.

3. The method for modeling a retail lending portfolio of claim 1 wherein the at least one known exogenous driver is a seasonality element.

4. The method for modeling a retail lending portfolio of claim 1 wherein the at least one known exogenous driver is an underlying trend.

5. The method for modeling a retail lending portfolio of claim 4 wherein the underlying trend is competitve pressure.

6. The method for modeling a retail lending portfolio of claim 4 wherein the underlying trend is macroeconomic.

7. The method for modeling a retail lending portfolio of claim 1, further comprising:

measuring autocorrelation in the residual exogenous component and using the measured autocorrelation in the generation of the plurality of random potential future scenarios for the residual component.

8. The method for modeling a retail lending portfolio of claim 1 wherein the plurality of random potential future scenarios for the residual exogenous component are generated using a Monte Carlo approach.

9. The method for modeling a retail lending portfolio of claim 1, further comprising:

applying a business simulation layer to the generated plurality of forecasts for the at least one key portfolio driver to produce a plurality of potential future forecasts for at least one portfolio performance measure.

10. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is potential revenues.

11. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is potential losses.

12. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is potential profits.

13. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is value at risk.

14. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is earnings at risk.

15. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is economic capital.

16. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is return on capital.

17. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is return on equity.

18. The method for modeling a retail lending portfolio of claim 9, wherein the at least one portfolio performance measure is a risk adjusted performance measurement.

19. The method for modeling a retail lending portfolio of claim 1 wherein the selected functional form is additive.

20. The method for modeling a retail lending portfolio of claim 1 wherein the selected functional form is multiplicative.

21. A system for modeling a retail lending portfolio comprising:

a data storage device having vintage performance data for a retail lending portfolio stored thereon, wherein the portfolio has at least one key portfolio driver;

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, wherein when the modeling engine is executed;

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 at least one vintage calibration parameter;

wherein the portfolio exogenous component includes at least one known exogenous driver;

wherein the executed modeling engine further;

extracts the at least one 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 plurality of random potential future scenarios for the residual exogenous component using the measured distribution of monthly changes;

generates a plurality of potential future scenarios for the exogenous component using the plurality of generated potential future scenarios for the residual exogenous component; and

generates a plurality of forecasts for the at least one key portfolio driver using the plurality of exogenous scenarios.

22. The system for modeling a retail lending portfolio of claim 21 wherein the at least one known exogenous driver is a management action element.

23. The system for modeling a retail lending portfolio of claim 21 wherein the at least one known exogenous driver is a seasonality element.

24. The system for modeling a retail lending portfolio of claim 21 wherein the at least one known exogenous driver is an underlying trend.

25. The system for modeling a retail lending portfolio of claim 21 wherein the underlying trend is competitive pressure.

26. The system for modeling a retail lending portfolio of claim 21 wherein the underlying trend is macroeconomic.

27. The system for modeling a retail lending portfolio of claim 21 wherein the executed modeling engine further measures autocorrelation in the residual exogenous component and uses the measured autocorrelation in the generation of the plurality of random potential future scenarios for the residual component.

28. The system for modeling a retail lending portfolio of claim 21 wherein the plurality of random potential future scenarios for the residual exogenous component are generated using a Monte Carlo approach.

29. The system for modeling a retail lending portfolio of claim 21 wherein the executed modeling engine further applies a business simulation layer to the plurality of potential future portfolio scenarios to produce a potential future forecast.

30. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is potential revenues.

31. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is potential losses.

32. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is potential profits.

33. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is value at risk.

34. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is earnings at risk.

35. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is economic capital.

36. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is return on capital.

37. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is return on equity.

38. The system for modeling a retail lending portfolio of claim 29, wherein the at least one portfolio performance measure is a risk adjusted performance measurement.

39. The system for modeling a retail lending portfolio of claim 21 wherein the selected functional form is additive.

40. The system for modeling a retail lending portfolio of claim 21 wherein the selected functional form is multiplicative.

41. A computer-readable medium encoded with a set of instructions for modeling a retail lending portfolio, wherein the portfolio has at least one 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;

wherein when the instructions are executed, the-instructions perform a method comprising;

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 at least one vintage calibration parameter, wherein the portfolio exogenous component includes at least one known exogenous driver;

extracting the at least one 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 plurality of random potential future scenarios for the residual exogenous component using the measured distribution of monthly changes;

generating a plurality of potential future scenarios for the exogenous component using the plurality of generated potential future scenarios for the residual exogenous component; and

generating a plurality of forecasts for the at least one key portfolio driver using the plurality of exogenous scenarios.

42. The encoded computer-readable medium of claim 41 wherein the at least one known exogenous driver of the portfolio exogenous component is a management action element.

43. The encoded computer-readable medium of claim 41 wherein the at least one known exogenous driver of the portfolio exogenous component is a seasonality element.

44. The encoded computer-readable medium of claim 41 wherein the at least one known exogenous driver of the portfolio exogenous component is an underlying trend.

45. The encoded computer-readable medium of claim 41 wherein the at least one known exogenous driver of the portfolio exogenous component is competitive pressure.

46. The encoded computer-readable medium of claim 41 wherein the at least one known exogenous driver of the portfolio exogenous component is macroeconomic.

47. The encoded computer-readable medium of claim 41, wherein the executed instructions further measure autocorrelation in the residual exogenous component and use the measured autocorrelation in the generation of the plurality of random potential future scenarios for the residual component.

48. The encoded computer-readable medium of claim 41 wherein the plurality of random potential future scenarios for the residual exogenous component are generated using a Monte Carlo approach.

49. The encoded computer-readable medium of claim 41 wherein the executed instructions further apply a business simulation layer to the generated plurality of forecasts for the at least one key portfolio driver to produce a plurality of potential future forecasts for at least one portfolio performance measure.

50. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is potential revenues.

51. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is potential losses.

52. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is potential profits.

53. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is value at risk.

54. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is earnings at risk.

55. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is economic capital.

56. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is return on capital.

57. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is return on equity.

58. The encoded computer-readable medium of claim 49, wherein the at least one portfolio performance measure is a risk adjusted performance measurement.

59. The encoded computer-readable medium of claim 41 wherein the selected functional form is additive.

60. The encoded computer-readable medium of claim 41 wherein the selected functional form is multiplicative.

61. A method for modeling a retail lending portfolio comprising:

providing vintage performance data for a retail lending portfolio, wherein the portfolio has at least one key portfolio driver;

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 at least one vintage calibration parameter, wherein the portfolio exogenous component includes elements of management action and seasonality;

extracting the elements of management action and seasonality from the portfolio exogenous component to generate a residual exogenous component;

computing monthly changes in the residual exogenous component;

measuring autocorrelation in the residual exogenous component;

measuring the distribution of monthly changes in the residual exogenous component;

generating a plurality of random potential future scenarios for the residual exogenous component using the measured distribution of monthly changes and the measured autocorrelation;

generating a plurality of potential future scenarios for the exogenous component using the plurality of generated potential future scenarios for the residual exogenous component; and

generating a plurality of forecasts for the at least one key portfolio driver using the plurality of exogenous scenarios.