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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.
Full paper (617 KB PDF)
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