Disentangling systematic and idiosyncratic dynamics in panels of volatility measures
Name:
Publisher version
View Source
Access full-text PDFOpen Access
View Source
Check access options
Check access options
Publication type
Journal article with impact factorPublication Year
2014Journal
Journal of EconometricsPublication Volume
182Publication Issue
2Publication Begin page
364Publication End page
384
Metadata
Show full item recordAbstract
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.Keyword
Accounting & FinanceKnowledge Domain/Industry
Accounting & Financeae974a485f413a2113503eed53cd6c53
10.1016/j.jeconom.2014.05.017