Stochastic Dynamic Programming in a Computer Using a Multi-Part Utility Function and Local Search to Efficiently Compute Asset Allocation and Consumption

• US 20160086277A1
• Filed: 09/22/2014
• Published: 03/24/2016
• Est. Priority Date: 09/22/2014
• Status: Abandoned Application

• Current Assignee: Irlam, Gordon Raymond
• Original Assignee: Irlam, Gordon Raymond
• Inventors: Irlam, Gordon Raymond
• Application Number:
  US14/493,115
• Publication Number:
  US 20160086277A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

The invention relates, among other things, to methods and non-transitory computer-readable medium that include inputting data representing consumption levels into the memory, wherein a value of the consumption levels represents required consumption C₁, inputting data representing returns of each asset class into the memory, performing stochastic dynamic programming in the processors using the data representing the returns of each asset class and values of a utility function U based on the consumption levels to compute values of aggregate utility of wealth, storing the values of the aggregate utility of wealth in the memory, computing local searches in the processors of the values of aggregate utility of wealth over an asset allocation and consumption space to compute optimal asset allocation and optimal consumption, storing the optimal asset allocation and optimal consumption in the memory, and outputting the optimal asset allocation and the optimal consumption value for an initial age and wealth.

Claims

1. A method in a computer including processor(s) coupled to a memory, comprising:

inputting data representing consumption level(s) into the memory, wherein a value of the consumption level(s) represents required consumption C₁;

inputting data representing returns of each asset class into the memory;

performing one or more step(s) of stochastic dynamic programming (SDP) in the processor(s) using the data representing the returns of each asset class and values of a utility function U based on the consumption levels to compute values of aggregate utility of wealth, wherein the SDP step(s) includes the following sub-steps;

reading data representing an aggregate utility of wealth U_t+1 for wealth levels W_t+1,1 through W_t+1,M, and a description of single time period utility U;

computing local searches in the processor(s) an optimal asset allocation vector A_t, an optimal consumption C_t, and an optimal aggregate utility of wealth U_t for a wealth level W_t,1;

computing local searches in the processor(s) the optimal asset allocation vector A_t, the optimal consumption C_t, and the optimal aggregate utility of wealth U_t for a wealth level W_t,2;

computing local searches in the processor(s) the optimal asset allocation vector A_t, the optimal consumption C_t, and the optimal aggregate utility of wealth U_t for additional wealth levels up to at least wealth level W_t,M;

storing the values of the aggregate utility of wealth U_t for wealth levels W_t,1 through W_t,M in the memory; and

outputting the optimal asset allocation vector A_B and the optimal consumption C_B at an initial age B and wealth W_B.

2. The method of claim 1, wherein another value of the consumption level(s) represents extra consumption C₂.

3. The method of claim 1, wherein the utility function U includes a required consumption region and an extra consumption region.

4. The method of claim 3, wherein the utility function U further includes a desired consumption region.

5. The method of claim 4, wherein U in the desired consumption region is a polynomial of the fourth power U_J, wherein U_J joins the utility function U₁ in the required consumption region and utility function U₂ in the extra consumption region, the first derivatives of U₁ and U₂ to U_J′

, and the second derivatives of U₁ and U₂ to U_J″

.

6. The method of claim 4, wherein U in the desired consumption region equals U_J, wherein U_J is defined by the following equations:

U_J′

(C)=wC³+xC²+yC+z

U_J′

(C₁)=U₁′

(C₁)

U_J′

(C₂)=U₂′

(C₂)

U_J″

(C₁)=U₁″

(C₁)

U_J″

(C₂)=U₂″

(C₂).

7. The method of claim 2, wherein utility function U includes utility functions U₁ and U₂ joined by U_J, wherein U is defined by the following equations:

U(C)=U₁(C) for C≦

C₁

U(C)=U_J(C)−

U_J(C₁)+U₁(C₁) for C₁<

C≦

C₂

U(C)=U₂(C)−

U₂(C₂)+U(C₂) for C₂<

C.

8. The method of claim 1, wherein the stochastic dynamic programming includes computing U_t(W_t)=T_t(W_t, R_t+1⁻

1(E_t[R_t+1(U_t+1(W_t+1))])), wherein U_t(W_t) represents the utility of wealth at time t, T_t is the time aggregator, W_t is wealth at time t, R_t+1 is the risk aggregator, and E_t is the expected value operator.

9. The method of claim 1, wherein the stochastic dynamic programming step includes computing U_t(W_t)=a_tU(C_t(W_t))/(1+r)^t+E_t[U_t+1(W_t+1)], wherein U_t(W_t) represents the utility of wealth W_t at time t, a_t represents the probability of an entity being alive at time t, U(C_t(W_t)) represents the single time period utility of consumption C_t(W_t), r represents the discount rate, C_t(W_t) is the consumption amount for wealth W_t at time t, and E_t is the expectation operator at time t.

10. The method of claim 1, further comprising inputting data into the memory that represents the probability of an entity being alive in the future and further performing stochastic dynamic programming to compute an asset allocation and a consumption based on the probability of the entity being alive.

11. The method of claim 1, further comprising inputting data representing the correlations of each asset class and performing stochastic dynamic programming to compute the asset allocation and the consumption based on the correlations.

12. The method of claim 1, wherein the data representing the returns is based on periodic returns or summary statistics.

13. The method of claim 1, further comprising inputting data into the memory that represents discount rates to adjust the utility function U.

14. The method of claim 13, wherein the discount rates represent the floor and/or upside utility discount rates.

15. The method of claim 14, wherein the range of the floor utility discount rate does not match a required consumption region.

16. A non-transitory computer-readable medium storing program instructions that cause a computer to perform the following steps, comprising:

inputting data representing consumption level(s) into the memory, wherein a value of the consumption level(s) represents required consumption C₁;

inputting data representing returns of each asset class into the memory;

performing one or more step(s) of stochastic dynamic programming in the processor(s) using the data representing the returns of each asset class and values of a utility function U based on the consumption levels to compute values of aggregate utility of wealth;

storing the values of the aggregate utility of wealth in the memory;

computing local searches in the processor(s) of the values of aggregate utility of wealth over an asset allocation and consumption space to compute optimal asset allocation and optimal consumption; and

outputting the optimal asset allocation and the optimal consumption value for an initial age and wealth.

17. The non-transitory computer-readable medium of claim 16, wherein the utility function U includes a required consumption region and an extra consumption region.

18. The non-transitory computer-readable medium of claim 17, wherein the utility function U includes a desired consumption region.

19. The non-transitory computer-readable medium of claim 16, wherein the stochastic dynamic programming step includes computing U_t(W_t)=a_tU(C_t(W_t))/(1+r)^t+E_t[U_t+1(W_t+1)], wherein U_t(W_t) represents the utility of wealth W_t at time t, a_t represents the probability of an entity being alive at time t, U(C_t(W_t)) represents the single time period utility of consumption C_t(W_t), r represents the discount rate, C_t(W_t) is the consumption amount for wealth W_t at time t, and E_t is the expectation operator at time t.

20. The non-transitory computer-readable medium of claim 16, further comprising inputting data representing the probability of an entity being alive in the future into the memory and further performing stochastic dynamic programming to compute an asset allocation and a consumption based on the probability of the entity being alive.

21. The non-transitory computer-readable medium of claim 16, further comprising inputting data representing discount rates to adjust the utility function U into the memory.