Abstract:
This paper uses high-frequency price data to study the relative contribution of jumps
to the total volatility of an equity. In particular, it systematically compares the relative contribution of jumps across a panel of stocks from three different industries by computing the cross-correlation of this statistic for pairs of stocks. We identify a number of empirical regularities in this cross-correlation and compare these observations to predictions from a standard jump-diffusion model for the joint price process of two stocks. A main finding of this paper is that this jump-diffusion model, when calibrated to particular pairs of stocks in the data, cannot replicate some of the empirical patterns observed. The model predictions differ from the empirical observations systematically: predictions for pairs of stocks from the same industry are on the whole much less accurate than predictions for pairs of stocks from different industries. Some possible explanations for this discrepancy are discussed.
