UWSpace is currently experiencing technical difficulties resulting from its recent migration to a new version of its software. These technical issues are not affecting the submission and browse features of the site. UWaterloo community members may continue submitting items to UWSpace. We apologize for the inconvenience, and are actively working to resolve these technical issues.
 

Calibration and Model Uncertainty of a Two-Factor Mean-Reverting Diffusion Model for Commodity Prices

Loading...
Thumbnail Image

Date

2013-08-28T19:41:05Z

Authors

Chuah, Jue Jun

Journal Title

Journal ISSN

Volume Title

Publisher

University of Waterloo

Abstract

With the development of various derivative instruments and index products, commodities have become a distinct asset class which can offer enhanced diversification benefits to the traditional asset allocation of stocks and bonds. In this thesis, we begin by discussing some of the key properties of commodity markets which distinguish them from bond and stock markets. Then, we consider the informational role of commodity futures markets. Since commodity prices exhibit mean-reverting behaviour, we will also review several mean-reversion models which are commonly used to capture and describe the dynamics of commodity prices. In Chapter 4, we focus on discussing a two-factor mean-reverting model proposed by Hikspoors and Jaimungal, as a means of providing additional degree of randomness to the long-run mean level. They have also suggested a method to extract the implied market prices of risk, after estimating both the risk-neutral and real-world parameters from the calibration procedure. Given the usefulness of this model, we are motivated to investigate the robustness of this calibration process by applying the methodology to simulated data. The capability to produce stable and accurate parameter estimates will be assessed by selecting various initial guesses for the optimization process. Our results show that the calibration method had a lot of difficulties in estimating the volatility and correlation parameters of the model. Moreover, we demonstrate that multiple solutions obtained from the calibration process would lead to model uncertainty in extracting the implied market prices of risk. Finally, by using historical crude oil data from the same time period, we can compare our calibration results with those obtained by Hikspoors and Jaimungal.

Description

Keywords

Calibration, Model Risk, Commodity

LC Keywords

Citation