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Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets

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Abstract

When observed over a large panel, measures of risk (such as realized volatilities) usually exhibit a secular trend around which individual risks cluster. In this article we propose a vector Multiplicative Error Model achieving a decomposition of each risk measure into a common systematic and an idiosyncratic component, while allowing for contemporaneous dependence in the innovation process. As a consequence, we can assess how much of the current asset risk is due to a system wide component, and measure the persistence of the deviation of an asset specific risk from that common level. We develop an estimation technique, based on a combination of seminonparametric methods and copula theory, that is suitable for large dimensional panels. The model is applied to two panels of daily realized volatilities between 2001 and 2008: the SPDR Sectoral Indices of the S&P500 and the constituents of the S&P100. Similar results are obtained on the two sets in terms of reverting behavior of the common nonstationary component and the idiosyncratic dynamics to with a variable speed that appears to be sector dependent.

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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 wp2010_06.

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Length: 47
Date of creation: Jul 2010
Date of revision:
Handle: RePEc:fir:econom:wp2010_06

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Keywords: Systematic risk; idiosyncratic risk; Multiplicative Error Model; seminonparametric; copula.;

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Cited by:
  1. Bollerslev, Tim & Todorov, Viktor & Li, Sophia Zhengzi, 2013. "Jump tails, extreme dependencies, and the distribution of stock returns," Journal of Econometrics, Elsevier, vol. 172(2), pages 307-324.
  2. Bryan Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2013. "Firm Volatility in Granular Networks," NBER Working Papers 19466, National Bureau of Economic Research, Inc.
  3. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
  4. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Banco de Espa�a Working Papers 1230, Banco de Espa�a.
  5. Bernard Herskovic & Bryan T. Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2014. "The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications," NBER Working Papers 20076, National Bureau of Economic Research, Inc.

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