Correlations of extreme stock returns within a non-Gaussian one-factor model

It is commonly believed that the correlations between stock returns increase in high volatility periods. We investigate how much of these correlations can be explained using conditional averages within a simple onefactor description. Using surrogate data with the true market return as the dominant factor, we show that most of these correlations can be accounted for. However, more subtle effects (such as the recently discovered LilloMantegna skewness) require an extension of the one factor model, where the variance and skewness of the residuals depend on the market return.