System and methods for modeling a multiplicative index

• US 8,255,302 B2
• Filed: 04/10/2008
• Issued: 08/28/2012
• Est. Priority Date: 02/28/2008
• Status: Active Grant

• Current Assignee: Morgan Stanley Services Group Inc. (Morgan Stanley)
• Original Assignee: Morgan Stanley
• Inventors: Chen, Mike
• Primary Examiner(s): Havan, Thu-Thao
• Assistant Examiner(s): Liu, Jennifer
• Application Number:
  US12/082,280
• Publication Number:
  US 20090222385A1
• Time in Patent Office: 1,601 Days
• Field of Search: 705 35- 45
• US Class Current: 705/35
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/10 Tax strategies

Abstract

Computer-implemented methods for valuing a derivative based on the BMA rate: the methods may comprise generating a model of the BMA/LIBOR ratio as a function of the LIBOR index, a stochastic noise function, and a seasonality process. The methods may also comprise solving the model for at least one value of the LIBOR index, and estimating a value of the derivative given the solution of the model. The value of the derivative may then be stored.

Claims

1. A computer-implemented method for valuing a derivative based on a first rate indicating interest paid on tax-exempt instruments, the method comprising:

generating, by a computer, a model of a ratio of the first rate over a second rate as a function of;

the second rate, wherein the second rate indicates interest paid on taxed instruments,

a stochastic noise function, and

a seasonality process, wherein the computer comprises at least one processor and operatively associated memory, wherein the model is expressed as;

BMA/LIBOR=ƒ

(LIBOR(t))+N+s(t)

wherein BMA/LIBOR is the ratio;

wherein LIBOR is the second rate;

wherein s(t) is the seasonality process; and

wherein N is the stochastic noise function;

generating a solution of the model, by the computer, for at least one value of the second rate; and

estimating, by the computer, a value of the derivative given the solution of the model.

2. The method of claim 1, wherein the derivative is a short-term derivative.

3. The method of claim 1, wherein s(t) is a polynomial expressed as:

4. The method of claim 3, wherein P is greater than or equal to ten.

5. The method of claim 4, wherein P is equal to 10, and wherein c₀=−

0.0260;

c₁=0.5455;

c₂=−

32.5873;

c₃=296.7654;

c₄=−

951.9348;

c₅=1081.07;

c₈=258.3131;

c₇=−

1061.1486;

c₈=−

311.0940;

c₉=1262.5281; and

c₁₀=−

542.4610.

6. The method of claim 3, wherein s(0)=s(1).

7. The method of claim 1, wherein the stochastic noise function is expressed as a function of independent and identically distributed (IID) random variables.

8. The method of claim 7, wherein the stochastic noise function is expressed as a Normal distribution.

9. The method of claim 8, wherein the Normal distribution is a best-fit distribution with a set of residuals between observed values of the ratio and the ratio generated by the model without considering the stochastic noise function.

10. The method of claim 1, wherein the stochastic noise function is expressed utilizing an auto-correlated model of a set of residuals between observed values of the ratio and the ratio generated by the model without considering the stochastic noise function.

11. The method of claim 10, wherein the auto-correlated model comprises a model selected from the group consisting of an Autoregressive Moving Average (ARMA) model;

a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model; and

a combination of an ARMA model and a GARCH model.

12. The method of claim 1, wherein the model is also a function of a stochastic tax-regime process.

13. The method of claim 12, wherein the stochastic tax-regime process comprises a jump-process model.

14. The method of claim 12, wherein the stochastic tax-regime process comprises a Markov-Chain with quarterly transitions, wherein the Markov-Chain comprises a first tax-exempt state and a second exemption-repealed state.

15. The method of claim 14, further comprising determining at least one transition probability of the Markov-Chain considering a value of a derivative based on at least one factor selected from the group consisting of the first rate and the ratio.

16. The method of claim 15, wherein the derivative pays when a difference between the first rate and the second rate is less than a top marginal tax rate.

29. The method of claim 1, wherein the first rate is based on the BMA Municipal Swap Index and the second rate is based on a London Interbank Exchange (LIBOR) rate.

17. A system for valuing a derivative based on a first rate indicating interest paid on tax-exempt municipal bonds, the system comprising at least one computer comprising at least one processor and operatively associated memory, wherein the memory comprises instructions that, when executed by the processor, causes the computer to:

generate a model of a ratio of the first rate over a second rate as a function of;

the second rate, wherein the second rate indicates interest paid on taxed instruments,

a stochastic noise function, and

a seasonality process, wherein the model is expressed as;

BMA/LIBOR=ƒ

(LIBOR(t))+N+s(t)

wherein BMA/LIBOR is the ratio;

wherein LIBOR is the second rate;

wherein s(t) is the seasonality process; and

wherein N is the stochastic noise function;

generate a solution of the model for at least one value of the second rate; and

estimate a value of the derivative given the solution of the model.

18. A computer readable storage medium having instructions thereon that when executed by at least one processor cause the at least one processor to:

generate a model of a ratio of a first rate over a second rate as a function of;

the second rate, wherein the second rate indicates interest paid on taxed instruments and the first rate indicates interest paid on tax-exempt instruments,

a stochastic noise function, and

a seasonality process, wherein the model is expressed as;

BMA/LIBOR=ƒ

(LIBOR(t))+N+s(t)

wherein BMA/LIBOR is the ratio;

wherein LIBOR is the second rate;

wherein s(t) is the seasonality process; and

wherein N is the stochastic noise function;

generate a solution of the model for at least one value of the second rate; and

estimate a value of the derivative given the solution of the model, wherein the medium is a computer storage medium.

19. A computer-implemented method for valuing a derivative based on a first rate indicating interest paid on tax-exempt instruments, the method comprising:

generating, by a computer, a model of a ratio of the first rate over a second rate as a function of;

the second rate, wherein the second rate indicates interest paid on taxed instruments,

a stochastic tax-regime process, and

a seasonality process, wherein the computer comprises at least one processor and operatively associated memory, wherein the model is expressed as;

BMA/LIBOR=ƒ

_r(LIBOR(t))+s_r(t)

wherein BMA/LIBOR is the ratio;

wherein LIBOR is the second rate;

wherein s(t) is the seasonality process; and

wherein r is the stochastic tax-regime process;

generating a solution of the model, by the computer, for at least one value of the second rate;

estimating, by the computer, a value of the derivative given the solution of the model.

20. The method of claim 19, wherein the derivative is a long-term derivative.

21. The method of claim 19, wherein the stochastic tax-regime process comprises a jump-process model.

22. The method of claim 19, wherein the stochastic tax-regime process comprises a Markov-Chain with quarterly transitions, wherein the Markov-Chain comprises a first tax-exempt state and a second exemption-repealed state.

23. The method of claim 22, further comprising determining at least one transition probability of the Markov-Chain considering a value of a derivative based on at least one factor selected from the group consisting of the first rate and the ratio.

24. The method of claim 19, wherein the model is also a function of a noise function.

25. The method of claim 24, wherein the noise function is a stochastic noise function expressed as a function of independent and identically distributed (IID) random variables.

26. The method of claim 25, wherein the stochastic noise function is expressed utilizing an auto-correlated model of a set of residuals between observed values of the ratio and the ratio generated by the model without considering the stochastic noise function.

30. The method of claim 19, wherein the first rate is based on the BMA Municipal Swap Index and the second rate is based on a London Interbank Exchange (LIBOR) rate.

27. A system for valuing a derivative based on the BMA rate, the system comprising at least one computer comprising at least one processor and operatively associated memory, wherein the memory comprises instructions that, when executed by the processor, causes the computer to:

generate a model of a ratio of the first rate over a second rate as a function of;

the second rate, wherein the second rate indicates interest paid on taxed instruments,

a stochastic tax-regime process, and

a seasonality process, wherein the model is expressed as;

BMA/LIBOR=ƒ

_r(LIBOR(t))+s_r(t)

wherein BMA/LIBOR is the ratio;

wherein LIBOR is the second rate;

wherein s(t) is the seasonality process; and

wherein r is the stochastic tax-regime process;

generate a solution to the model for at least one value of the second rate; and

estimate a value of the derivative given the solution of the model.

28. A computer readable storage medium having instructions thereon that when executed by at least one processor cause the at least one processor to:

generate a model of a ratio of the first rate over a second rate as a function of;

the second rate, wherein the second rate indicates interest paid on taxed instruments,

a stochastic tax-regime process, and

a seasonality process, wherein the model is expressed as;

BMA/LIBOR=ƒ

_r(LIBOR(t))+s_r(t)

wherein BMA/LIBOR is the ratio;

wherein LIBOR is the second rate;

wherein s(t) is the seasonality process; and

wherein r is the stochastic tax-regime process;

generate a solution to the model for at least one value of the second rate; and

estimate a value of the derivative given the solution of the model, wherein the medium is a computer storage medium.