Computer-Implemented Systems and Methods for Hedging with Counterbalancing Capacity

• US 20150134566A1
• Filed: 11/10/2014
• Published: 05/14/2015
• Est. Priority Date: 11/08/2013
• Status: Abandoned Application

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Chen, Wei, Skoglund, Klas Jimmy
• Application Number:
  US14/537,591
• Publication Number:
  US 20150134566A1
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

This disclosure describes computer-program products, systems and computer-implemented methods for optimal liquidity reserve planning. Liquidity portfolio optimization models are disclosed that hedge with contractual cash flows and/or hedge with counterbalancing capacity. With respect to hedging with contractual cash flows, disclosed embodiments receive a minimum solvency rate for a simulation of future events that specifies a percentage of simulation scenarios in which a solvent stress test outcome is achieved, and a minimum cost portfolio is identified that provides solvency in a percentage of simulation scenarios that meets or exceeds the minimum solvency rate. With respect to hedging with counterbalancing capacity, a hedging portfolio is structured by executing a liquidity portfolio optimization model, which prescribes a series of liquidity execution actions related to management of the hedging portfolio with respect to at least one random simulation scenario.

Claims

1. A computer-program product comprising a non-transitory machine-readable storage medium that includes instructions operable to cause a data processing apparatus to perform operations including:

obtaining information representing assets and liabilities of an entity;

obtaining a representation of a time horizon defined with respect to a series of consecutive future time periods;

generating representations of multiple simulation scenarios on a computing device, wherein the multiple simulation scenarios include one or more impacts associated with hypothetical events during the time horizon, and wherein the one or more impacts are forecasted based on the hypothetical events and the information representing the assets and liabilities; and

structuring a hedging plan by executing an optimization model subject to at least one constraint, wherein the optimization model is executed on the computing device and prescribes, with respect to at least one of the simulation scenarios, a series of actions related to management of the hedging plan.

2. The computer-program product of claim 1, wherein at least one of the hypothetical events includes:

a series of hypothetical changes of an instrument held by the entity, wherein the series of hypothetical changes is specified with respect to the time horizon.

3. The computer-program product of claim 2, wherein:

the assets include bonds;

the series of hypothetical changes includes multiple changes with respect to valuations of the bonds; and

the changes with respect to valuation of bonds follow a geometric Brownian motion process characterized by a specified mean and standard deviation.

4. The computer-program product of claim 3, wherein at least one of the impacts is further based on forecasted coupon payments yielded by the bonds, wherein the coupon payments are forecasted based on a uniform distribution.

5. The computer-program product of claim 3, wherein at least one of the series of actions includes a transaction involving pledging the bonds in a repo market, wherein the optimization model specifies a face value of the bonds prescribed to be pledged.

6. The computer-program product of claim 1, wherein at least one of the hypothetical events includes:

a hypothetical option call associated with obligations of the entity, wherein the option call is specified with respect to one of the future time periods.

7. The computer-program product of claim 1, wherein at least one of the hypothetical events includes:

a margin call associated with obligations of the entity, wherein the margin call is associated with a required posting of collateral.

8. The computer-program product of claim 1, wherein the hedging plan is a global optimal solution to a combinatorial optimization problem solved by executing the optimization model.

9. The computer-program product of claim 1, wherein executing the optimization model includes processing the representations.

10. The computer-program product of claim 1, wherein the operations further include:

determining contractual cash flows anticipated to result from the assets and liabilities during the time horizon, wherein executing the optimization model includes inputting the determined contractual cash flows to the optimization model.

11. The computer-program product of claim 10, wherein the portfolio optimization model identifies at least one of the series of actions based on the determined contractual cash flows.

12. The computer-program product of claim 11, wherein the operations further include:

receiving an input representing a regulatory requirement, wherein;

the regulatory requirement specifies a targeted solvency rate;

the targeted solvency rate includes a targeted percentage of the scenarios in which the entity is forecasted to be solvent; and

the optimization model structures the hedging plan based on the received input.

13. The computer-program product of claim 12, wherein structuring the hedging plan based on the received input includes determining that the hedging plan is a lowest cost plan that satisfies the regulatory requirement.

14. The computer-program product of claim 1, wherein the hedging plan includes cash and bonds, and wherein structuring the hedging plan includes determining an amount of cash and an aggregate value of the bonds included in the hedging plan.

15. The computer-program product of claim 14, wherein at least one of the series of actions includes buying and selling decisions with respect to assets of the various classes.

16. The computer-program product of claim 1, wherein at least one of the simulation scenarios includes a hypothetical series of forecasted asset valuation changes with respect to assets held by the entity.

17. The computer-program product of claim 1, wherein at least one of the simulated scenarios includes a hypothetical series of events in which a bank portfolio or loan of the entity expands.

18. The computer-program product of claim 1, wherein generating representations of multiple simulation scenarios includes performing a Monte Carlo simulation.

19. The computer-program product of claim 1, wherein the operations further include:

identifying a revolving credit facility accessed by the entity, wherein at least one of the series of actions includes borrowing from the revolving credit facility.

20. The computer-program product of claim 1, wherein executing the optimization model yields, with respect to each of the scenarios, a hypothetical liquidity gap measure.

21. The computer-program product of claim 20, wherein each of the hypothetical liquidity gap measures is based on the respective simulation scenario and the series of actions forecasted for the respective simulation scenario.

22. The computer-program product of claim 20, wherein the operations further include:

determining a distribution of the hypothetical liquidity gap measures, wherein determining the distribution includes calculating a mean hypothetical liquidity gap, and a liquidity gap associated with confidence intervals of the distribution.

23. The computer-program product of claim 1, wherein the operations further include:

using the hedging plan in a process of dynamic trading, wherein the process of dynamic trading includes using at least one of the series of actions and closes a liquidity gap with respect to the assets and liabilities.

24. A system comprising:

a processor configured to perform operations including;

obtaining information representing assets and liabilities of an entity;

obtaining a representation of a time horizon defined with respect to a series of consecutive future time periods;

generating representations of multiple simulation scenarios on a computing device, wherein the multiple simulation scenarios include one or more impacts associated with hypothetical events during the time horizon, and wherein the one or more impacts are forecasted based on the hypothetical events and the information representing the assets and liabilities; and

structuring a hedging plan by executing an optimization model subject to at least one constraint, wherein the optimization model is executed on the computing device and prescribes, with respect to at least one of the simulation scenarios, a series of actions related to management of the hedging plan.

25. The system of claim 24, wherein at least one of the hypothetical events includes:

a series of hypothetical changes of an instrument held by the entity, wherein the series of hypothetical changes is specified with respect to the time horizon.

26. The system of claim 25, wherein:

the assets include bonds;

the series of hypothetical changes includes multiple changes with respect to valuations of the bonds; and

the changes with respect to valuation of bonds follow a geometric Brownian motion process characterized by a specified mean and standard deviation.

27. The system of claim 26, wherein at least one of the impacts is further based on forecasted coupon payments yielded by the bonds, wherein the coupon payments are forecasted based on a uniform distribution.

28. The system of claim 26, wherein at least one of the series of actions includes a transaction involving pledging the bonds in a repo market, wherein the optimization model specifies a face value of the bonds prescribed to be pledged.

29. The system of claim 24, wherein at least one of the hypothetical events includes:

a hypothetical option call associated with obligations of the entity, wherein the option call is specified with respect to one of the future time periods.

30. The system of claim 24, wherein at least one of the hypothetical events includes:

a margin call associated with obligations of the entity, wherein the margin call is associated with a required posting of collateral.

31. The system of claim 24, wherein the hedging plan is a global optimal solution to a combinatorial optimization problem solved by executing the optimization model.

32. The system of claim 24, wherein executing the optimization model includes processing the representations.

33. The system of claim 24, wherein the operations further include:

determining contractual cash flows anticipated to result from the assets and liabilities during the time horizon, wherein executing the optimization model includes inputting the determined contractual cash flows to the optimization model.

34. The system of claim 33, wherein the optimization model identifies at least one of the series of actions based on the determined contractual cash flows.

35. The system of claim 34, wherein the operations further include:

receiving an input representing a regulatory requirement, wherein;

the regulatory requirement specifies a targeted solvency rate;

the targeted solvency rate includes a targeted percentage of the scenarios in which the entity is forecasted to be solvent; and

the optimization model structures the hedging plan based on the received input.

36. The system of claim 35, wherein structuring the hedging plan based on the received input includes determining that the hedging portfolio is a lowest cost portfolio that satisfies the regulatory requirement.

37. The system of claim 24, wherein the hedging plan includes cash and bonds, and wherein structuring the hedging plan includes determining an amount of cash and an aggregate value of the bonds included in the hedging plan.

38. The system of claim 37, wherein at least one of the series of actions includes buying and selling decisions with respect to assets of the various classes.

39. The system of claim 24, wherein at least one of the simulation scenarios includes a hypothetical series of forecasted asset valuation changes with respect to assets held by the entity.

40. The system of claim 24, wherein at least one of the simulated scenarios includes a hypothetical series of events in which a bank portfolio or loan of the entity expands.

41. The system of claim 24, wherein generating representations of multiple simulation scenarios includes performing a Monte Carlo simulation.

42. The system of claim 24, wherein the operations further include:

identifying a revolving credit facility accessed by the entity, wherein at least one of the series of actions includes borrowing from the revolving credit facility.

43. The system of claim 24, wherein executing the optimization model yields, with respect to each of the scenarios, a hypothetical liquidity gap measure.

44. The system of claim 43, wherein each of the hypothetical liquidity gap measures is based on the respective scenario and the series of actions forecasted for the respective scenario.

45. The system of claim 43, wherein the operations further include:

determining a distribution of the hypothetical liquidity gap measures, wherein determining the distribution includes calculating a mean hypothetical liquidity gap, and a liquidity gap associated with confidence intervals of the distribution.

46. The system of claim 24, wherein the operations further include:

using the hedging plan in a process of dynamic trading, wherein the process of dynamic trading includes using at least one of the series of actions and closes a liquidity gap with respect to the assets and liabilities.

47. A computer-implemented method comprising:

obtaining information representing assets and liabilities of an entity;

obtaining a representation of a time horizon defined with respect to a series of consecutive future time periods;

generating representations of multiple simulation scenarios on a computing device, wherein the multiple simulation scenarios include one or more impacts associated with hypothetical events during the time horizon, and wherein the one or more impacts are forecasted based on the hypothetical events and the information representing the assets and liabilities; and

structuring a hedging plan by executing an optimization model subject to at least one constraint, wherein the optimization model is executed on the computing device and prescribes, with respect to at least one of the simulation scenarios, a series of actions related to management of the hedging plan.

48. The method of claim 47, wherein at least one of the events includes:

a series of hypothetical changes of an instrument held by the entity, wherein the series of hypothetical valuation changes is specified with respect to the time horizon.

49. The method of claim 48, wherein:

the assets include bonds;

the series of hypothetical changes includes multiple changes with respect to valuations of the bonds; and

the changes with respect to valuation of bonds follow a geometric Brownian motion process characterized by a specified mean and standard deviation.

50. The method of claim 49, wherein at least one of the impacts is further based on forecasted coupon payments yielded by the bonds, wherein the coupon payments are forecasted based on a uniform distribution.

51. The method of claim 49, wherein at least one of the series of actions includes a transaction involving pledging the bonds in a repo market, wherein the optimization model specifies a face value of the bonds prescribed to be pledged.

52. The method of claim 47, wherein at least one of the hypothetical events includes:

a hypothetical option call associated with obligations of the entity, wherein the option call is specified with respect to one of the future time periods.

53. The method of claim 47, wherein at least one of the hypothetical events includes:

a margin call associated with obligations of the entity, wherein the margin call is associated with a required posting of collateral.

54. The method of claim 47, wherein the hedging plan is a global optimal solution to a combinatorial optimization problem solved by executing the optimization model.

55. The method of claim 47, wherein executing the optimization model includes processing the representations.

56. The method of claim 47, further comprising:

determining contractual cash flows anticipated to result from the assets and liabilities during the time horizon, wherein executing the optimization model includes inputting the determined contractual cash flows to the optimization model.

57. The method of claim 56, wherein the optimization model identifies at least one of the series of actions based on the determined contractual cash flows.

58. The method of claim 57, further comprising:

receiving an input representing a regulatory requirement, wherein;

the regulatory requirement specifies a targeted solvency rate;

the targeted solvency rate includes a targeted percentage of the scenarios in which the entity is forecasted to be solvent; and

the optimization model structures the hedging plan based on the received input.

59. The method of claim 47, wherein structuring the hedging plan based on the received input includes determining that the hedging plan is a lowest cost plan that satisfies the regulatory requirement.

60. The method of claim 47, wherein the hedging plan includes cash and bonds, and wherein structuring the hedging plan includes determining an amount of cash and an aggregate value of the bonds included in the hedging plan.

61. The method of claim 60, wherein at least one of the series of actions includes buying and selling decisions with respect to assets of the various classes.

62. The method of claim 47, wherein at least one of the scenarios includes a hypothetical series of forecasted asset valuation changes with respect to assets held by the entity.

63. The method of claim 47, wherein at least one of the simulated scenarios includes a hypothetical series of events in which a bank portfolio or loan of the entity expands.

64. The method of claim 47, wherein generating representations of multiple simulation scenarios includes performing a Monte Carlo simulation.

65. The method of claim 47, further comprising:

identifying a revolving credit facility accessed by the entity, wherein at least one of the series of actions includes borrowing from the revolving credit facility.

66. The method of claim 47, wherein executing the optimization model yields, with respect to each of the simulation scenarios, a hypothetical liquidity gap measure.

67. The method of claim 66, wherein each of the hypothetical liquidity gap measures is based on the respective simulation scenario and the series of actions forecasted for the respective simulation scenario.

68. The method of claim 66, further comprising:

determining a distribution of the hypothetical liquidity gap measures, wherein determining the distribution includes calculating a mean hypothetical liquidity gap, and a liquidity gap associated with confidence intervals of the distribution.

69. The method of claim 47, further comprising:

using the hedging plan in a process of dynamic trading, wherein the process of dynamic trading includes using at least one of the series of actions and closes a liquidity gap with respect to the assets and liabilities.