Financial risk mitigation optimization systems and methods

• US 7,624,054 B2
• Filed: 08/25/2005
• Issued: 11/24/2009
• Est. Priority Date: 08/25/2005
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Hu, Ping, Erdman, Donald J., Chen, Wei
• Primary Examiner(s): Patel; Jagdish N
• Assistant Examiner(s): HAMILTON, SARA CHANDLER
• Application Number:
  US11/211,353
• Publication Number:
  US 20070050282A1
• Time in Patent Office: 1,552 Days
• Field of Search: 705/35, 705/36.R, 705/37
• US Class Current: 705/35
• 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

Abstract

Systems and methods for optimizing financial risk mitigations. A system and method can be provided for the generation of a financial risk mitigation optimization model. The model can be used in determining an allocation of financial risk mitigations with respect to exposures.

Claims

1. A computer-implemented method for mitigating financial risk, comprising:

receiving, by at least one processor, data indicative of exposures, data indicative of financial risk mitigations, and data indicative of relationships between financial risk mitigation and exposures, the received relationship data including a one-to-many relationship between a financial risk mitigation and an exposure;

constructing, by the at least one processor, a generalized network model, by applying one or more financial risk mitigations to one or more exposures wherein the step of constructing comprises;

calculating an exposure value associated with each of the exposures and a mitigation value associated with each of the financial risk mitigations,

determining whether there is a one-to-many relationship between one or more of the exposures and one or more of the financial risk mitigations based upon the relationship data received,

when a one-to-many relationship is determined, calculating an exposure riskiness measure associated with each of the one or more exposures having a one-to-many relationship and a mitigation riskiness measure associated with each of the one or more financial risk mitigations having a one-to-many relationship,

comparing each of the exposure values and exposure riskiness measures associated with the exposures to each of the mitigation values and mitigation riskiness measures associated with the financial risk mitigations, and

determining, as an output of the generalized network model, what portion of each of the exposures can be offset by each of the financial risk mitigations based upon the comparison;

producing, by the at least one processor, one or more allocations of the mitigations to the exposures by applying, a first linear programming algorithm to the output of the generalized network model; and

performing, by the at least one processor, an analysis of each of the mitigation values, by applying a financial risk mitigation optimization model to the one or more allocations of the mitigations to the exposures and determining which of the one or more allocations of the mitigations to the exposures is an optimal solution.

2. The method of claim 1, wherein the financial risk mitigation optimization model maximizes the use of financial risk mitigations risk mitigated to a minimal level;

and wherein the financial risk mitigation optimization model is solved based upon the exposure, financial risk mitigation, and relationship data.

3. The method of claim 2, wherein the financial risk mitigation optimization model includes a second linear programming algorithm for use in determining an optimal allocation of the financial risk mitigations with respect to the exposures.

4. The method of claim 3, wherein the second linear programming algorithm is at least one of an interior point algorithm, or a simplex algorithm, including any combinations thereof.

5. The method of claim 1, wherein financial risk mitigation is credit risk mitigation.

6. The method of claim 1, wherein the mitigation riskiness measure for each of the one or more financial risk mitigations and the exposure riskiness measure for each of the one or more exposures is determined based upon probability of default and loss given default associated with the financial risk mitigations and exposures.

7. The method of claim 1, wherein the data indicative of the relationships between financial risk mitigations and exposures is generated based upon agreements which specify how financial risk mitigations are associated with exposures.

8. The method of claim 7, wherein the relationships between the exposures and financial risk mitigations are established by at least one of a collateral agreement, an insurance agreement, a credit derivative, a guarantee, or a netting agreement.

9. The method of claim 7, further comprising:

calculating, by the at least one processor, a discount factor between related exposures and financial risk mitigations;

wherein the discount factor is calculated based upon maturity mismatches between the exposures and financial risk mitigations, currency mismatches between the exposures and financial risk mitigations, or an over-collateralization requirement, and combinations thereof.

10. The method of claim 9, further comprising:

calculating, by the at least one processor, a cost of using a financial risk mitigation to cover an exposure based upon the mitigation riskiness measure of the financial risk mitigation and the exposure riskiness measure of the exposure.

11. The method of claim 1, further comprising:

linking, by the at least one processor, the data indicative of exposures to the data indicative of financial risk mitigations, and

associating, by the at least one processor, a cost and a discount factor with each of the linkings of the generalized network.

12. The method of claim 1, wherein the financial risk optimization model is configured to notify a user of a minimum asset value level adequate to cover risk-weighted financial risk mitigations and any risk-weighted unsecured exposures.

13. The method of claim 12, wherein performing the analysis of risk weighted assets comprises:

adjusting each of the financial risk mitigations by;

multiplying each of the financial risk mitigations by associated financial risk mitigation discounts based upon the relationships between one or more of the financial risk mitigations and one or more of the exposures,

multiplying each of the financial risk mitigations by associated financial risk mitigation riskiness measures;

adjusting any uncovered portions of each of the exposures by multiplying risk-weighted unsecured exposures by associated exposure riskiness measures; and

adding the adjusted financial risk mitigations associated with an exposure to the adjusted risk-weighted financial risk portions of the exposures.

14. The method of claim 13, wherein the analysis of risk-weighted assets for the exposures alerts a user to a level of regulatory capital projected to cover risk-weighted unsecured exposures and risk-weighted financial risk mitigations.

15. The method of claim 14, wherein the risk weighted assets analysis of the financial risk mitigation optimization model is created based upon financial regulations.

16. The method of claim 1, wherein the one-to-many relationship comprises at least one of:

a relationship between an exposure and a plurality of financial risk mitigations, a relationship between a financial risk mitigation and a plurality of exposures, and a relationship between a plurality of financial risk mitigations and a plurality of exposures.

18. The method of claim 1, wherein producing an allocation includes matching a mitigation associated with multiple exposures to one or more of the associated exposures.

19. The method of claim 1, wherein producing an allocation includes matching an exposure associated with multiple mitigations to one or more of the associated mitigations.

17. A computer-implemented system configured to generate a financial risk mitigation optimization model including an allocation of financial risk mitigations to exposures, comprising:

memory; and

at least one processor linked to the memory and configured to;

receive data indicative of exposures, data indicative of financial risk mitigations, and data indicative of relationships between financial risk mitigations and exposures, wherein the received relationship data includes a one-to-many relationship between a financial risk mitigation and an exposure;

construct a generalized network model, by applying one or more financial risk mitigations to one or more exposures, wherein constructing includes;

calculating an exposure value associated with each of the exposures and a mitigation value associated with each of the financial risk mitigations;

determining whether there is a one-to-many relationship between one or more of the exposures and one or more of the financial risk mitigations based on the relationship data received;

when a one-to many relationship is determined, calculating an exposure riskiness measure associated with each of the one or more exposures having a one-to-many relationship and a mitigation riskiness measure associated with each of the one or more financial risk mitigations having a one-to-many relationship;

comparing each of the exposure values and exposure riskiness measures associated with the exposures to each of the mitigation values and mitigation riskiness measures associated with the financial risk mitigations; and

determining, as an output of the generalized network model, what portion of each of the exposures can be offset by each of the financial risk mitigations based on the comparison;

produce one or more allocations of the mitigations to the exposures by applying a first linear programming algorithm to the output of the generalized network model; and

perform an analysis of each of the mitigation values, by applying a financial risk mitigation optimization model to the one or more allocations of the mitigations to the exposures, and determine which of the one or more allocations of the mitigations to the exposures is an optimal solution.