Jump Tails, Extreme Dependencies and the Distribution of Stock Returns, Journal of Econometrics
We provide a new framework for estimating the systematic and idiosyncratic jump tail risks in financial asset prices. Our estimates are based on in-fill asymptotics for directly identifying the jumps, together with Extreme Value Theory (EVT) approximations and methods-of-moments for assessing the tail decay parameters and tail dependencies. On implementing the procedures with a panel of intraday prices for a large cross-section of individual stocks and the S&P 500 market portfolio, we find that the distributions of the systematic and idiosyncratic jumps are both generally heavy-tailed and close to symmetric, and show how the jump tail dependencies deduced from the high-frequency data together with the day-to-day variation in the diffusive volatility account for the extreme joint dependencies observed at the daily level.
Tim Bollerslev, Viktor Todorov, Sophia Zhengzi Li
Bollerslev, Tim, Viktor Todorov, and Sophia Zhengzi Li. 2013. Jump Tails, Extreme Dependencies and the Distribution of Stock Returns. Journal of Econometrics.(172): 307-324.LINK