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

  • Ilias Tsiakas

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

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Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 208.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:208
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  1. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
  2. Bollerslev, T. & Ghysels, E., 1994. "Periodic Autoregressive Conditional Heteroskedasticity," Cahiers de recherche 9408, Centre interuniversitaire de recherche en ├ęconomie quantitative, CIREQ.
  3. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 371-89, October.
  4. Josef Lakonishok, Seymour Smidt, 1988. "Are Seasonal Anomalies Real? A Ninety-Year Perspective," Review of Financial Studies, Society for Financial Studies, vol. 1(4), pages 403-425.
  5. Michael K Pitt & Neil Shephard, . "Filtering via simulation: auxiliary particle filters," Economics Papers 1997-W13, Economics Group, Nuffield College, University of Oxford.
  6. Sangjoon Kim & Neil Shephard, 1994. "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers 3., Economics Group, Nuffield College, University of Oxford.
  7. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
  8. Ilias Tsiakas, 2004. "Periodic Stochastic Volatility and Fat Tails," Working Papers wp04-09, Warwick Business School, Finance Group.
  9. Jeff Fleming & Chris Kirby, 2003. "A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(3), pages 365-419.
  10. Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September.
  11. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
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