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Analysis of the predictive ability of information accumulated over nights, weekends and holidays

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  • Ilias Tsiakas
Abstract
Recent empirical evidence suggests that the weekend and holiday calendar effects are much stronger and statistically significant in volatility as opposed to expected returns. This paper seeks an explanation for this empirical finding by undertaking a comprehensive investigation of the predictive ability of information accumulated over nights, weekends and holidays for a series of global indices. We study this form of seasonal heteroscedasticity by employing a generalized stochastic volatility model, in which the conditional daily volatility is measured in calendar time from open-to-close of the market, and depends on lagged close-to-open returns. We conduct a series of empirical tests and conclude that the information accumulated over weekends and especially holidays is a predictor of subsequent daily volatility. The SV parameters are estimated by implementing a Bayesian MCMC algorithm, which is adjusted for sampling the seasonal volatility level effects. We compute in-sample and out-of-sample density forecasts for assessing the adequacy of the conditional distribution. We also use Bayes factors as a likelihood-based framework for evaluating the SV specifications. Bayes factors account for both estimation and model risk. We conclude by computing volatility forecasts relevant for risk management

Suggested Citation

  • Ilias Tsiakas, 2004. "Analysis of the predictive ability of information accumulated over nights, weekends and holidays," Econometric Society 2004 Australasian Meetings 208, Econometric Society.
    • Handle: RePEc:ecm:ausm04:208
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    File URL: http://repec.org/esAUSM04/up.6497.1077824066.pdf
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    References listed on IDEAS

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    Cited by:

    1. Yun-Yeong Kim, 2013. "A Test for Trading Time Hypothesis on Weekends under Time Varying Autoregression with Heteroskedasti," Korean Economic Review, Korean Economic Association, vol. 29, pages 97-118.

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    More about this item

    Keywords

    Stochastic Volatility; Calendar Effects; Seasonal Heteroscedasticity; Bayesian MCMC estimation; Bootstrapping; Forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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