Overnight information and stochastic volatility: A study of European and US stock exchanges

Tsiakas, Ilias

doi:10.1016/j.jbankfin.2007.03.008

Cited by 68 publications

(42 citation statements)

References 58 publications

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“…9 Consistent with Figure 2, the existing literature on intraday price dynamics finds that the average per-hour ratio of daytime to overnight volatility is around 4 0 (e.g., Stoll and Whaley 1990, Lockwood and Linn 1990, Tsiakas 2008. It is also found in the existing literature that expected returns are not significantly different across day and night.…”

Section: Numerical Analysissupporting

confidence: 76%

“…This assumption is motivated by the empirical findings that either the expected returns do not vary significantly across trading and nontrading periods or the returns over the nontrading periods are significantly higher than those over the trading periods (e.g., Tsiakas 2008). Although it is beyond the scope of this paper to provide an equilibrium model that can generate different Sharpe ratios across day and night, such an equilibrium can be consistent with an economy with heterogeneous investors; e.g., some investors may be more risk averse toward carrying overnight inventories than others, or they may have heterogeneous beliefs on the time-varying return dynamics.…”

Section: Constantinidesmentioning

confidence: 99%

“…An extensive literature on stock return dynamics across trading and nontrading periodsfinds that although expected returns do not vary significantly across these periods, return volatility is much higher during trading periods (e.g., French and Roll 1986, Stoll and Whaley 1990, Tsiakas 2008; see Figure 2 in §5). For example, French and Roll (1986) and Stoll and Whaley (1990) find that return volatility during trading periods is more than four times the volatility during nontrading periods on a per-hour basis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Portfolio Choice with Market Closure and Implications for Liquidity Premia

Dai

Hong

et al. 2016

Management Science

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M ost existing portfolio choice models ignore the prevalent periodic market closure and the fact that market volatility is significantly higher during trading periods. We find that market closure and the volatility difference across trading and nontrading periods significantly change optimal trading strategies. In addition, we demonstrate numerically that transaction costs can have a first-order effect on liquidity premia that is largely comparable to empirical findings. Moreover, this effect on liquidity premia increases in the volatility difference, which is supported by our empirical analysis.

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Section: Numerical Analysissupporting

confidence: 76%

Section: Constantinidesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Portfolio Choice with Market Closure and Implications for Liquidity Premia

Dai

Hong

et al. 2016

Management Science

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“…Overnight volatility also could be some missing part of the proxy. Related to the overnight volatility, Tsiakas (2008) found that there is substantial predictive ability in financial information accumulated during nontrading hours. Ahoniemi and Lanne (2013) established that the most accurate measure of realised volatility is the weighted sum of intraday squared returns and the squared overnight returns.…”

Section: Literature Reviewmentioning

confidence: 99%

Relative performance of VIXC vs. GARCH in predicting realised volatility changes

Jung

2016

Investment Analysts Journal

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This paper examines the forecasting power of the volatility index of Canada (VIXC) and GARCH-family volatility. Specifically, this paper is motivated by an enquiry into how well volatility estimators predict volatility changes. To this end, we use a daily series of VIXC from 1 October 2009 through 30 April 2015. To estimate out-of-sample parameters for GARCH volatilities, a series of daily returns of the TSX60 since 29 November 2002 is used roll-forwardly. Then we run the forecasting regressions for a full-sample and five subsamples grouped by the daily percentage change in realised volatility, and compare R 2 and the loss functions to assess the forecasting power of volatility estimators. Additionally, this paper proposes a new measurement called mean-directional-error (MDE), which can comprehensively evaluate the forecast error in both direction and size of movement. The key findings of this paper can be summarised as follows: even though the information content of VIXC is highest on average and intensifies when volatility falls high, the directional accuracy measured by MDE is worst for VIXC in all subsamples. GJR-GARCH (1,1) achieves the highest accuracy judged by the traditional loss functions. Focusing on the directional accuracy, GARCH (1,1) demonstrates the best predictability. ARTICLE HISTORY

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“…It has been shown that realized volatilities can improve the statistical accuracy of daily forecasts from historical volatility models (Blair et al, 2001;Fuertes et al, 2009). There is also evidence based on statistical criteria that the overnight information ‡ow triggered by interactions across stock exchanges in di¤erent time zones, cross-listed stocks and news released outside regular trading hours has predictive content for the subsequent daytime volatility (Gallo, 2001;Tsiakas, 2008). On the other hand, the evidence on the ability of trading volume to improve the statistical accuracy of volatility forecasts is rather weak (Brooks, 1998;Donaldson and Kamstra, 2005).…”

Section: Introductionmentioning

confidence: 99%

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

Fuertes

Kalotychou

Todorović

2014

Rev Quant Finan Acc

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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

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Overnight information and stochastic volatility: A study of European and US stock exchanges

Cited by 68 publications

References 58 publications

Portfolio Choice with Market Closure and Implications for Liquidity Premia

Portfolio Choice with Market Closure and Implications for Liquidity Premia

Relative performance of VIXC vs. GARCH in predicting realised volatility changes

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

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