Barigozzi, Matteo, Brownlees, Christian T., Gallo, Giampiero M. and Veredas, David (2010) Disentangling systematic and idiosyncratic risk for large panels of assets. ECARES working paper, 2010‐019. Université Libre de Bruxelles, Brussels, Belgium.
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.
|Item Type:||Monograph (Working Paper)|
|Additional Information:||© 2010 Université Libre de Bruxelles|
|Uncontrolled Keywords:||systematic risk, idiosyncratic risk, multiplicative error model, seminonparametric, copula.|
|Library of Congress subject classification:||H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
|Journal of Economic Literature Classification System:||C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
|Sets:||Departments > Statistics
Collections > Economists Online
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