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Disentangling systematic and idiosyncratic dynamics in panels of volatility measures

Matteo Barigozzi, Christian Brownlees (), Giampiero Gallo () and David Veredas

Journal of Econometrics, 2014, vol. 182, issue 2, 364-384

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.

Keywords: Vector multiplicative error model; Seminonparametric estimation; Volatility (search for similar items in EconPapers)
JEL-codes: C32 C51 G01 (search for similar items in EconPapers)
Date: 2014
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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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