Variance Optimal Hedging for discrete time processes with independent increments. Application to Electricity Markets

TL;DR

This paper develops an algorithm for variance optimal hedging in a discretized two-factor electricity market model, explicitly deriving the decomposition for vanilla options and analyzing rebalancing strategies' effects on hedging accuracy.

Contribution

It introduces an explicit Foellmer-Schweizer decomposition-based algorithm for mean-variance hedging in a discretized electricity market model with independent increments.

Findings

The algorithm effectively computes hedging strategies for vanilla options.

Rebalancing date choices significantly influence hedging errors.

Payoff regularity and non-stationarity impact hedging performance.

Abstract

We consider the discretized version of a (continuous-time) two-factor model introduced by Benth and coauthors for the electricity markets. For this model, the underlying is the exponent of a sum of independent random variables. We provide and test an algorithm, which is based on the celebrated Foellmer-Schweizer decomposition for solving the mean-variance hedging problem. In particular, we establish that decomposition explicitely, for a large class of vanilla contingent claims. Interest is devoted in the choice of rebalancing dates and its impact on the hedging error, regarding the payoff regularity and the non stationarity of the log-price process.