Using volume to forecast stock market volatility around the time of the 1929 crash

Bradley T. Ewing, Mark A. Thompson, Mark A. Yanochik

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This article explores the role of trading volume in making out-of-sample forecasts of stock market volatility around the time of the 24 October 1929 crash. Following the recent literature on volatility forecasting, we compare the performance of symmetric and asymmetric GARCH-class models. Moreover, as the volume-volatility relationship is now well established for modern day markets, we also consider the performance of these models when volume is allowed to enter the conditional variance equation. Given the institutional evidence that trading volume was beginning to take on an increasingly important role in the eyes of investors and market regulators during the last part of the 1920s, this is a particularly insightful endeavour. Generally speaking, the volatility models with trading volume provided the best volatility forecasts after 'Black Thursday'.

Original languageEnglish (US)
Pages (from-to)1123-1128
Number of pages6
JournalApplied Financial Economics
Volume17
Issue number14
DOIs
StatePublished - Oct 1 2007

Fingerprint

Crash
Stock market volatility
Trading volume
Volatility models
Investors
Generalized autoregressive conditional heteroscedasticity
Volatility forecasting
Out-of-sample forecasting
Volatility forecasts
Conditional variance

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

Cite this

Using volume to forecast stock market volatility around the time of the 1929 crash. / Ewing, Bradley T.; Thompson, Mark A.; Yanochik, Mark A.

In: Applied Financial Economics, Vol. 17, No. 14, 01.10.2007, p. 1123-1128.

Research output: Contribution to journalArticle

Ewing, Bradley T. ; Thompson, Mark A. ; Yanochik, Mark A. / Using volume to forecast stock market volatility around the time of the 1929 crash. In: Applied Financial Economics. 2007 ; Vol. 17, No. 14. pp. 1123-1128.
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