Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?

Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy? This article presents a comprehensive analysis of the relative ability of three information sets—daily trading volume, intraday returns and overnight returns—to predict equity volatility. We investigate the extent to which statistical accuracy of one-day-ahead forecasts translates into economic gains for technical traders. Various profitability criteria and utility-based switching fees indicate that the largest gains stem from combining historical daily returns with volume information. Using common statistical loss functions, the largest degree of predictive power is found instead in intraday returns. Our analysis thus reinforces the view that statistical significance does not have a direct mapping onto economic value. As a byproduct, we show that buying the stock when the forecasted volatility is extremely high appears largely profitable, suggesting a strong return-risk relationship in turbulent conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?

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Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Economics / Management Science; Finance/Investment/Banking; Accounting/Auditing; Econometrics; Operations Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-014-0436-6
Publisher site
See Article on Publisher Site

Abstract

This article presents a comprehensive analysis of the relative ability of three information sets—daily trading volume, intraday returns and overnight returns—to predict equity volatility. We investigate the extent to which statistical accuracy of one-day-ahead forecasts translates into economic gains for technical traders. Various profitability criteria and utility-based switching fees indicate that the largest gains stem from combining historical daily returns with volume information. Using common statistical loss functions, the largest degree of predictive power is found instead in intraday returns. Our analysis thus reinforces the view that statistical significance does not have a direct mapping onto economic value. As a byproduct, we show that buying the stock when the forecasted volatility is extremely high appears largely profitable, suggesting a strong return-risk relationship in turbulent conditions.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Feb 8, 2014

References

  • Is there a risk-return trade-off? Evidence from high-frequency data
    Bali, TG; Peng, L
  • Power and bipower variation with stochastic volatility and jumps
    Barndorff-Nielsen, OE; Shephard, N
  • Comparison of volatility measures: a risk management perspective
    Brownlees, C; Gallo, G
  • Filter rule tests of the economic significance of serial dependence in daily stock returns
    Corrado, CJ; Lee, SH
  • Volatility forecasts trading volume and the ARCH versus option-implied volatility trade-off
    Donaldson, G; Kamstra, M
  • Efficient capital markets: a review of theory and empirical work
    Fama, E
  • Optimally harnessing inter-day and intra-day information for value-at-risk prediction
    Fuertes, AM; Olmo, J
  • An intertemporal capital asset pricing model
    Merton, RC

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