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Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures

  • Matteo Barigozzi

    ()

    (London School of Economics and Political Science – Department of Statistics)

  • Christian T. Brownlees

    ()

    (Universitat Pompeu Fabra – Department of Economics and Business & Barcelona GSE)

  • Giampiero M. Gallo

    ()

    (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze)

  • David Veredas

    ()

    (ECARES – Solvay Brussels School of Economics and Management – Université libre de Bruxelles)

Realized volatilities measured on several assets exhibit a common secular trend and some idiosyncratic pattern. We accommodate such an empirical regularity extending the class of Multiplicative Error Models (MEMs) to a model where 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 an heterogeneous degree of persistence around the trend. An out–of–sample forecasting exercise shows that the proposed methodology improves volatility prediction over a number of benchmark specifications.

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Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number 2014_02.

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Length: 71 pages
Date of creation: Feb 2014
Date of revision: Feb 2014
Handle: RePEc:fir:econom:wp2014_02
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