Method for stochastically modeling electricity prices

• US 20020165816A1
• Filed: 05/02/2002
• Published: 11/07/2002
• Est. Priority Date: 05/02/2001
• Status: Abandoned Application

• Current Assignee: Graydon Lee Barz
• Original Assignee: Graydon Lee Barz
• Inventors: Barz, Graydon Lee
• Application Number:
  US10/137,918
• Publication Number:
  US 20020165816A1
• Time in Patent Office: Days
• US Class Current: 705/37
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

A method for simulating commodity prices comprising the steps of receiving an input comprising a primary time series, computing a related time series from the primary series, identifying a cyclical variation series comprising a plurality of cycles for the related time series, identifying at least one dominant cyclical variation component series from the cyclical variation series, computing a plurality of contribution time series each comprising a plurality of contributions from each of at least one dominant cyclical variation component series to the cyclical variation series, regressing each of the contribution time series to compute a residual time series and a regression function, computing a future value fit time series from each of the regression functions, computing a future value residual time series from each of the residual time series, constructing a simulated contribution time series comprising a plurality of simulated contributions from each of the future value fit time series and the future value residual time series, combining the dominant cyclical variation component series with the simulated contribution time series to produce a simulated related time series, and computing a simulated primary time series from the simulated related time series.

Claims

1. A method for simulating commodity prices comprising the steps of:

Receiving an input comprising a primary time series;

Computing a related time series from said primary series;

Identifying a cyclical variation series comprising a plurality of cycles for said related time series;

Identifying at least one dominant cyclical variation component series from said cyclical variation series;

Computing a plurality of contribution time series each comprising a plurality of contributions from each of at least one dominant cyclical variation component series to said cyclical variation series;

Regressing each of said contribution time series to compute a residual time series and a regression function;

Computing a future value fit time series from each of said regression functions;

Computing a future value residual time series from each of said residual time series;

Constructing a simulated contribution time series comprising a plurality of simulated contributions from each of said future value fit time series and said future value residual time series;

Combining said dominant cyclical variation component series with the simulated contribution time series to produce a simulated related time series; and

Computing a simulated primary time series from said simulated related time series.

2. The method of claim 1 wherein computing a related time series from said primary series comprises the additional steps of:

Constructing an inverse transform function of said primary time series; and

Applying said inverse transform function to said primary time series.

3. The method of claim 1 wherein computing a future value residual time series comprises the steps of:

Selecting a stochastic process;

Fitting said stochastic process to said residual time series to produce a plurality of fit parameters; and

Simulating said stochastic process with said fit parameters.

4. The method of claim 1 wherein computing a simulated primary time series from said simulated related time series comprises the steps of:

Constructing a transform function of said simulated related time series; and

Applying said transform function to said simulated related time series.

5. The method of claim 2 wherein said inverse transform function is strictly monotonic.

6. The method of claim 1 comprising the additional step of modifying a series selected from the group consisting of primary time series, related time series, cyclical variation series, dominant cyclical variation component series, contribution time series, fit time series, and residual time series.

7. The method of claim 1 wherein said contribution time series is regressed against a time variable selected from the group consisting of hour, day, week, month, season, and year.

8. The method of claim 1 wherein said commodity is selected from the group consisting of prices for bandwidth capacity, DRAM, electronic storage and/or processing, application service providers (ASP) services, spot electricity spot, future electricity, agricultural products, energy commodities, chemicals, and real-estate indices, weather indices, and other physical variables, and derivative contracts of any previously mentioned member of the above group.

9. The method of claim 2, wherein constructing an inverse transform function of said primary time series comprises the additional steps of:

Receiving an input comprising a secondary time series; and

Identifying a transform function from said primary time series to said secondary time series.

10. The method of claim 1, identifying at least one dominant cyclical variation component series from said cyclical variation series comprises the additional steps of:

Constructing a matrix of second moments from said cyclical variation series;

Computing a plurality of principle components of said matrix of second moments; and

Selecting each of said dominant cyclical variation component series from said plurality of principle components.

11. The method of claim 1, wherein regressing each of said contribution time series a residual time series and a regression function comprises the additional steps of:

Receiving an input comprising a supplemental time series; and

Regressing each of said contribution time series on said supplemental time series to produce a residual time series and a regression fit.