Retail lending risk related scenario generation
First Claim
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 provides the relationship between the 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.
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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, ultimately, for the generation of a number of forecasts for key portfolio drivers.
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Citations
61 Claims
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1. A method for modeling a retail lending portfolio comprising:
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providing vintage performance data for a retail lending portfolio, wherein the portfolio has at least one key portfolio driver; selecting a functional form that provides the relationship between the 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A system for modeling a retail lending portfolio comprising:
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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 provides the relationship between the 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; decomposes 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; 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. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40)
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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 provides the relationship between the 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. - View Dependent Claims (42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60)
- wherein when the instructions are executed, the-instructions perform a method comprising;
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61. A method for modeling a retail lending portfolio comprising:
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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.
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Specification