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Disentangling systematic and idiosyncratic dynamics in panels of volatility measures

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  • Barigozzi, Matteo
  • Brownlees, Christian
  • Gallo, Giampiero M.
  • Veredas, David

Abstract

Realized volatilities observed across several assets show a common secular trend and some idiosyncratic pattern which we accommodate by extending the class of Multiplicative Error Models (MEMs). In our model, the common trend is estimated nonparametrically, while the idiosyncratic dynamics are assumed to follow univariate MEMs. Estimation theory based on seminonparametric methods is developed for this class of models for large cross-sections and large time dimensions. The methodology is illustrated using two panels of realized volatility measures between 2001 and 2008: the SPDR Sectoral Indices of the S&P500 and the constituents of the S&P100. Results show that the shape of the common volatility trend captures the overall level of risk in the market and that the idiosyncratic dynamics have a heterogeneous degree of persistence around the trend. Out-of-sample forecasting shows that the proposed methodology improves volatility prediction over several benchmark specifications.

Suggested Citation

  • Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
  • Handle: RePEc:eee:econom:v:182:y:2014:i:2:p:364-384
    DOI: 10.1016/j.jeconom.2014.05.017
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    Cited by:

    1. Jozef Barun'ik & Tobias Kley, 2015. "Quantile Cross-Spectral Measures of Dependence between Economic Variables," Papers 1510.06946, arXiv.org.
    2. Harry-Paul Vander Elst, 2015. "FloGARCH: Realizing Long Memory and Asymmetries in Returns Valitility," Working Papers ECARES ECARES 2015-12, ULB -- Universite Libre de Bruxelles.
    3. repec:eee:econom:v:201:y:2017:i:2:p:307-321 is not listed on IDEAS
    4. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    5. Giampiero M. Gallo & Edoardo Otranto, 2017. "Combining Sharp and Smooth Transitions in Volatility Dynamics: a Fuzzy Regime Approach," Econometrics Working Papers Archive 2017_05, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    6. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    7. Fabrizio Cipollini & Giampiero M. Gallo, 2018. "Modeling Euro STOXX 50 Volatility with Common and Market–specific Components," Working Paper series 18-26, Rimini Centre for Economic Analysis.
    8. Paul G. Egan & Anthony J. Leddin, 2016. "Examining Monetary Policy Transmission in the People's Republic of China–Structural Change Models with a Monetary Policy Index," Asian Development Review, MIT Press, vol. 33(1), pages 74-110, March.
    9. repec:taf:jnlbes:v:35:y:2017:i:4:p:513-527 is not listed on IDEAS
    10. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    11. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.

    More about this item

    Keywords

    Vector multiplicative error model; Seminonparametric estimation; Volatility;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G01 - Financial Economics - - General - - - Financial Crises

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