Hybrid Simulation Methodologies To Simulate Risk Factors

• US 20110167020A1
• Filed: 01/06/2010
• Published: 07/07/2011
• Est. Priority Date: 01/06/2010
• Status: Abandoned Application

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Chen, Wei, Christian, Stacey Michelle, Erdman, Donald James, Yang, Zhiping
• Application Number:
  US12/683,020
• Publication Number:
  US 20110167020A1
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

Computer-implemented systems and methods are provided for generating a simulated forecast based on members of a pool of input risk factor variables. Certain members of the pool of input risk factor variables are identified as members of a first set of variables, and certain other members of the pool of input risk factor variables are identified as members of a second set of variables. A first simulation is generated via a first simulation method using the first set of variables, and a second simulation is generated via a second simulation method that differs from the first simulation method using the second set of variables. The first simulation and the second simulation are generated using correlations among variables in the first set of variables and variables in the second set of variables.

Claims

1. A computer-implemented method for providing a simulated forecast based on correlated members of a pool of input risk factor variables representing input data, the method comprising:

identifying certain members of the pool of input risk factor variables as being members of a first set of variables, and identifying certain other members of the pool of input risk factor variables as being members of a second set of variables;

generating a first simulation via a first simulation method using the first set of variables to generate a set of first results;

generating a second simulation via a second simulation method that differs from the first simulation method using the second set of variables to generate a set of second results;

the first simulation and the second simulation being generated utilizing correlations among variables in the first set of variables and variables in the second set of variables; and

storing the set of first results and the set of second results as a simulated forecast in a computer-readable memory.

2. The method of claim 1, wherein the first simulation method and the second simulation methods differ in that the first simulation method is more time and computational-resource intensive than the second simulation method.

3. The method of claim 1, wherein the first simulation method and the second simulation methods differ in that the first simulation method considers more historical data points of variables in first set of variables than the second simulation method considers of variables of the second set of variables.

4. The method of claim 3, wherein the first simulation is required by law to consider more historical data points of the variables of first set of variables than the second simulation method considers of the variables of second set of variables.

5. The method of claim 1, further comprising:

identifying certain other members of the pool of input risk factor variables as being members of a third set of variables;

generating a third simulation via a third simulation method that differs from the first simulation method and the second simulation method using the third set of variables to generate a set of third results; and

storing the set of third results with the set of first results and the set of second results as the simulated forecast.

6. The method of claim 1, further comprising:

generating a copula indicative of correlation among variables in the first set of variables and variables in the second set of variables using the input data;

utilizing the copula in the first simulation and the second simulation to incorporate correlations among variables in the first set of variables and variables in the second set of variables.

7. The method of claim 6, further comprising:

computing independent random vectors for each variable in the first set of variables and each variable in the second set of variables;

converting the independent random variables into a set of correlated uniforms using the copula;

applying the first simulation and the second simulation to the set of correlated uniforms.

8. The method of claim 6, wherein the copula is a multivariate distribution having uniformly distributed values over (0,1) inclusively.

9. The method of claim 1, wherein the priority simulation method is a simulation method selected from the group consisting of:

Monte-Carlo simulation, covariate simulation, historical simulation, and scenario simulation.

10. The method of claim 1, wherein the non-priority simulation method is a simulation method that differs from the priority simulation method selected from the group comprising:

Monte-Carlo simulation, covariate simulation, historical simulation and scenario simulation.

11. The method of claim 1, wherein the members of the first set of variables are identified based on a sensitivity analysis of the members of the pool of input risk factor variables, where a degree of information contribution of each variable in the pool of input risk factor variables is calculated, and variables having a highest degree of information contribution are identified as members of the first set of variables.

12. The method of claim 1, further comprising calculating a target forecast value based on multiple simulated forecast values and storing the target forecast value in a computer-readable memory.

13. The method of claim 6, wherein generating a copula (C) based on the correlation data comprises calculating:

C<sub>Σ

,F1,F2, . . . ,FN</sub>(u₁,u₂, . . . ,u_N)=Φ

_Σ

(F₁<sup>−

1</sup>(u₁),F₂<sup>−

1</sup>(u₂), . . . ,F_N<sup>−

1</sup>(u_N)),

where F_n is the marginal distribution for risk factor input variable n;

where Σ

is a matrix representing the received correlation data indicative of correlations among the members of the pool of risk factor input variables;

where Φ

_Σ

is a standardized multivariate normal distribution with correlation matrix Σ

; and

u_n is uniform data for risk factor input variable n.

14. The method of claim 6, wherein generating a first simulation and generating a second simulation includes generating a conditional normal distribution for a dependent set of risk factors variables in the first set of variables using a Schur complement based on correlations among members of the pool of input risk factor variables.

15. The method of claim 7, wherein the correlated uniforms are calculated by:

calculating a Cholesky decomposition of Σ

, as A;

wherein Σ

identifies correlations among risk factor variables;

simulating n independent random variates z=(z₁, z₂, . . . ,z_n) from N(0,1)

defining x as Az; and

calculating u_i=Φ

(x_i) for I=1, 2, . . . , n, where Φ

is a univariate standard normal distribution function.

16. A computer-implemented system for providing a simulated forecast based on correlated members of a pool of input risk factor variables representing input data, the system comprising:

a data processor;

a computer-readable memory encoded with instructions for commanding the data processor to implement a method, the method comprising;

identifying certain members of the pool of input risk factor variables as being members of a first set of variables, and identifying certain other members of the pool of input risk factor variables as being members of a second set of variables;

generating a first simulation via a first simulation method using the first set of variables to generate a set of first results;

generating a second simulation via a second simulation method that differs from the first simulation method using the second set of variables to generate a set of second results;

the first simulation and the second simulation being generated utilizing correlations among variables in the first set of variables and variables in the second set of variables; and

storing the set of first results and the set of second results as a simulated forecast in a computer-readable memory.

17. The system of claim 16, wherein the first simulation method and the second simulation methods differ in that the first simulation method is more time and computational-resource intensive than the second simulation method.

18. The system of claim 16, wherein the method further comprises:

generating a copula indicative of correlation among variables in the first set of variables and variables in the second set of variables using the input data;

utilizing the copula in the first simulation and the second simulation to incorporate correlations among variables in the first set of variables and variables in the second set of variables.

19. The system of claim 16, wherein the method further comprises calculating a target forecast value based on multiple simulated forecast values and storing the target forecast value in a computer-readable memory.

20. A computer-readable memory encoded with instructions for commanding a data processor to execute a method, the method comprising:

identifying certain members of the pool of input risk factor variables as being members of a first set of variables, and identifying certain other members of the pool of input risk factor variables as being members of a second set of variables;

generating a first simulation via a first simulation method using the first set of variables to generate a set of first results;

generating a second simulation via a second simulation method that differs from the first simulation method using the second set of variables to generate a set of second results;

the first simulation and the second simulation being generated utilizing correlations among variables in the first set of variables and variables in the second set of variables; and

storing the set of first results and the set of second results as a simulated forecast in a computer-readable memory.