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It's all about volatility of volatility: evidence from a two-factor stochastic volatility model

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  • Stefano Grassi
  • Paolo Santucci de Magistris

Abstract

The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with time varying parameters. The parameters are estimated by means of a sequential matching procedure which adopts as auxiliary model a time-varying generalization of the HAR model for the realized volatility series. It emerges that during the recent financial crisis the relative weight of the daily component dominates over the monthly term. The estimates of the two factor stochastic volatility model suggest that the change in the dynamic structure of the realized volatility during the financial crisis is due to the increase in the volatility of the persistent volatility term. A set of Monte Carlo simulations highlights th correctness of the methodology adopted to extract the variability in the parameters.

Suggested Citation

  • Stefano Grassi & Paolo Santucci de Magistris, 2013. "It's all about volatility of volatility: evidence from a two-factor stochastic volatility model," Studies in Economics 1404, School of Economics, University of Kent.
    • Handle: RePEc:ukc:ukcedp:1404
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    2. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark E. Wohar, 2016. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Open Economies Review, Springer, vol. 27(2), pages 229-250, April.
    3. Michele Costola & Matteo Iacopini & Casper Wichers, 2023. "Bayesian SAR model with stochastic volatility and multiple time-varying weights," Papers 2310.17473, arXiv.org.
    4. Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong & Simo-Kengne, Beatrice D., 2014. "Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 170-189.
    5. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    6. Eduardo Rossi & Paolo Santucci de Magistris, 2018. "Indirect inference with time series observed with error," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 874-897, September.
    7. Hyeyoen Kim & Doojin Ryu, 2013. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 81-92, September.
    8. Alper Gormus, N., 2016. "Do different time-horizons in volatility have any significance for the emerging markets?," Economics Letters, Elsevier, vol. 145(C), pages 29-32.
    9. Danglun Luo & Qianwei Ying, 2014. "Political Connections and Bank Lines of Credit," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 5-21, May.
    10. Andrea Barletta & Elisa Nicolato & Stefano Pagliarani, 2019. "The short‐time behavior of VIX‐implied volatilities in a multifactor stochastic volatility framework," Mathematical Finance, Wiley Blackwell, vol. 29(3), pages 928-966, July.
    11. Liu, Min, 2022. "The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 288-309.
    12. Omokolade Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2016. "Real estate returns predictability revisited: novel evidence from the US REITs market," Empirical Economics, Springer, vol. 51(3), pages 1165-1190, November.
    13. Naser, Hanan & Alaali, Fatema, 2015. "Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach," MPRA Paper 65295, University Library of Munich, Germany, revised 25 Jun 2015.
    14. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.
    15. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    16. repec:ipg:wpaper:2014-470 is not listed on IDEAS
    17. Lo, Chien-Ling & Shih, Pai-Ta & Wang, Yaw-Huei & Yu, Min-Teh, 2019. "VIX derivatives: Valuation models and empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 1-21.
    18. Costola, Michele & Iacopini, Matteo & Wichers, Casper, 2023. "Bayesian SAR model with stochastic volatility and multiple time-varying weights," SAFE Working Paper Series 407, Leibniz Institute for Financial Research SAFE.

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    More about this item

    Keywords

    Time-Varying Parameters; On-line Kalman Filter; Simulation-based inference; Predictive Likelihood; Volatility Factors;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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