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Bayesian analysis of moving average stochastic volatility models: modeling in-mean effects and leverage for financial time series

Author

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  • Stefanos Dimitrakopoulos
  • Michalis Kolossiatis
Abstract
We propose a moving average stochastic volatility in mean model and a moving average stochastic volatility model with leverage. For parameter estimation, we develop efficient Markov chain Monte Carlo algorithms and illustrate our methods, using simulated and real data sets. We compare the proposed specifications against several competing stochastic volatility models, using marginal likelihoods and the observed-data Deviance information criterion. We also perform a forecasting exercise, using predictive likelihoods, the root mean square forecast error and Kullback-Leibler divergence. We find that the moving average stochastic volatility model with leverage better fits the four empirical data sets used.

Suggested Citation

  • Stefanos Dimitrakopoulos & Michalis Kolossiatis, 2020. "Bayesian analysis of moving average stochastic volatility models: modeling in-mean effects and leverage for financial time series," Econometric Reviews, Taylor & Francis Journals, vol. 39(4), pages 319-343, April.
    • Handle: RePEc:taf:emetrv:v:39:y:2020:i:4:p:319-343
    DOI: 10.1080/07474938.2019.1630075
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    Cited by:

    1. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    2. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    3. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    4. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.

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