MEAN REVERSION MODELING WITH APPLICATION IN ENERGY MARKETS

Abstract

A phenomenon observed in energy prices is that they tend to exhibit mean-reversion behavior. This thesis proposes two new models on mean-reversion patterns of energy assets: Time-invariant Wavelet-Schwartz Model and Time-Varying State Space Model. The first model is capable of describing stationary time series with fixed degree of mean-reversion by incorporating wavelet-decomposition techniques into the one-factor Schwartz model. As a de-noising method, the wavelet filter is a useful tool to track the cycles of the price movements which can be modeled by mean-reversion. The second model can be used to describe mean reverting processes with constantly changing parameters by adopting a Bayesian estimation approach. The prediction step uses the Kalman Filter, while the Bayesian approach with variance gamma assumption is applied on the calibration of the time-varying mean reversion model. The proposed two models are applied to historical energy price data to test their performance in trading activity. The simulation results generated by the two models are then compared and discussed. This application shows that when different measures are taken, similar sensitivity appears by fixing a relationship between symmetric parameters.