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

Tim Bollerslev, Viktor Todorov*, Sophia Zhengzi Li

*Corresponding author for this work

Research output: Contribution to journalArticle

55 Scopus citations

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 languageEnglish (US)
Pages (from-to)307-324
Number of pages18
JournalJournal of Econometrics
Volume172
Issue number2
DOIs
StatePublished - Feb 2013

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

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