Self-exciting Extreme Value Models for Stock Market Crashes

Rodrigo Herrera and Bernhard Schipp ()
Additional contact information
Bernhard Schipp: Technische Universität Dresden, Fakultät Wirtschaftswissenschaften

A chapter in Statistical Inference, Econometric Analysis and Matrix Algebra, 2009, pp 209-231 from Springer

Abstract: Abstract We demonstrate the usefulness of Extreme value Theory (EVT) to evaluate magnitudes of stock market crashes and provide some extensions. A common practice in EVT is to compute either unconditional quantiles of the loss distribution or conditional methods linking GARCH models to EVT. Our approach combines self-exciting models for exceedances over a given threshold with a marked dependent process for estimating the tail of loss distributions. The corresponding models allow to adopt ex-ante estimation of two risk measures in different quantiles to assess the expected frequency of different crashes of important stock market indices. The paper concludes with a backtesting estimation of the magnitude of major stock market crashes in financial history from one day before an important crash until one year later. The results show that this approach provides better estimates of risk measures than the classical methods and is moreover able to use available data in a more efficient way.

Keywords: Risk Measure; Generalize Pareto Distribution; Conditional Intensity; Expect Shortfall; Extreme Value Theory (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2121-5_15

Ordering information: This item can be ordered from
http://www.springer.com/9783790821215

DOI: 10.1007/978-3-7908-2121-5_15

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().