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

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. 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 pro…tability 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 signi…cance 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 pro…table, suggesting a strong return-risk relationship in turbulent conditions. JEL Classi…cation: C53; C32; C14. Permanent repository link

On perceptions of financial volatility in price sequences

“…The study of volatility has long held academic interest and has witnessed many advances over the years, as exemplified by the rapidly growing literature on modelling and forecasting "realized volatility" using intra-day data to obtain more accurate and efficient forecasts. See, for example, the many papers published in the "realized volatility" special issue of the Journal of Econometrics edited byMeddahi, Mykland, and Shephard (2011) and more recent studies includingFuertes et al (2015),Andrada-Félix et al (2016) andKourtis et al (2016), among others. 2 Earlier,Parkinson (1980) andKunitomo (1992) propose price-based, extreme value methods for estimating volatility.…”

Volatility forecasting across tanker freight rates: The role of oil price shocks

“…Forecasting in the shipping industry and energy markets, which are directly linked to the financial markets, has gained popularity as demonstrated by a large number of studies (Kavussanos, 1996b;Jing, Marlow, and Wangi, 2008;Drobetz et al, 2012;Wang and Wu, 2012;Alizadeh, 2013). Such studies are aided by the tools and methodological approaches developed for forecasting shipping freight rates; while in the general finance literature a number of studies have included various economic and other parameters in the modeling process, such as implied volatility, captured by the Volatility Index (VIX), and trading volume (Lamoureux and Lastrapes, 1990;Blair, Poon, and Tay-lor, 2001;Fuertes, Kalotychou, and Todorovic, 2015;Kambouroudis and McMillan, 2016;Chao, 2016).…”

Stock volatility predictability in bull and bear markets

“…All these things considered, the stocks in the DJIA index offer a relatively large but at the same time manageable sample with wide industry representation. This is why the components of the DJIA are a popular choice in the literature on volatility forecasting (see, for example, Hansen and Lunde 2005b, Awartani et al 2009, Scharth and Medeiros 2009, Hansen et al 2012, Fuertes et al 2015, Hansen and Huang 2016.…”