Evaluating Forecasts of Correlation Using Option Pricing

Abstract

A forecast of the correlation between two asset prices is required to price or hedge an option whose payoff depends on both asset prices or to measure the risk of a portfolio whose return depends on both asset prices. However, a number of factors make it difficult to evaluate forecasts of correlation. We develop a forecast evaluation methodology based on option pricing, extending a technique that Engle et al. (1993) introduced to evaluate volatility forecasts. A forecast of the variance-covariance matrix of joint asset returns is used to generate a trading strategy for a package of simulated options. The most accurate forecast will produce the most profitable trading strategy. The package of simulated options can be chosen to be sensitive to correlation, to volatility, or to any arbitrary combination of the two. In an empirical application, we focus on the ability to forecast the correlation between two stock market indices. We compare the correlation forecasting ability of three more sophisticated models (two GARCH models and a two-state Markov switching model) and two simple moving averages. We find that the more sophisticated models produce better correlation forecasts than the simple moving averages.