Jump tails, extreme dependencies, and the distribution of stock returns

Tim Bollerslev (), Viktor Todorov and Sophia Zhengzi Li

Journal of Econometrics, 2013, vol. 172, issue 2, 307-324

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.

Keywords: Extreme events; Jumps; High-frequency data; Jump tails; Non-parametric estimation; Stochastic volatility; Systematic risks; Tail dependence (search for similar items in EconPapers)
JEL-codes: C13 C14 G10 G12 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (66)

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Related works:
Working Paper: Jump Tails, Extreme Dependencies, and the Distribution of Stock Returns (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:172:y:2013:i:2:p:307-324

DOI: 10.1016/j.jeconom.2012.08.014

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