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Title: Daily volume, intraday and overnight returns for volatility prediction: Profitability or accuracy?
Authors: Fuertes, Ana Maria 
Kalotychou, Elena 
Todorovic, Natasa 
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Directional change prediction;Trading rules;Realized volatility;Conditional variance forecasting
Issue Date: 8-Feb-2014
Source: Review of Quantitative Finance and Accounting, 2015, vol. 45, no. 2, pp. 251-278.
Volume: 45
Issue: 2
Start page: 251
End page: 278
Journal: Review of Quantitative Finance and Accounting 
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.
ISSN: 0924865X
DOI: 10.1007/s11156-014-0436-6
Rights: © Springer
Type: Article
Affiliation : City University London 
Appears in Collections:Άρθρα/Articles

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