Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 307-324 |
| Number of pages | 18 |
| Journal | Journal of Econometrics |
| Volume | 172 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2013 |
Funding
We would like to thank the Editor (Marc Paolella) and two anonymous referees for many helpful comments and suggestions. The research was supported by a grant from the NSF to the NBER, and CREATES funded by the Danish National Research Foundation (Bollerslev).
Keywords
- Extreme events
- High-frequency data
- Jump tails
- Jumps
- Non-parametric estimation
- Stochastic volatility
- Systematic risks
- Tail dependence
ASJC Scopus subject areas
- Economics and Econometrics
- Applied Mathematics