Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures
59 Pages Posted: 31 May 2010 Last revised: 7 Oct 2014
Date Written: January 29, 2014
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 an 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
JEL Classification: C32, C51, G01
Suggested Citation: Suggested Citation