We introduce a class of multiplicative dynamic models for realized covariance matrices assumed to be conditionally Wishart distributed. The multiplicative structure enables consistent three-step estimation of the parameters, starting by covariance targeting of a scale matrix. The dynamics of...

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...

Value at risk (VaR) and expected shortfall (ES) are two of the most widely used risk measures in economics and finance. In this paper, we use a semiparametric method, together with realized variance measures, to jointly estimate structural models for the two risk measures. The semiparametric...

Innovations in volatility constitute a potentially important asset pricing risk factor that can be tested using the return on variance swaps. We characterize the exposures of returns on equities, bonds and currencies in all regions of the world to U.S. based equity variance risk. We explore...

We apply univariate GARCH models to construct a computationally simple filter for estimating the conditional correlation matrix of asset returns. The proposed Variance Implied Conditional Correlation (VICC) exploits the polarization result that links the correlation between two standardized...

We study the design of an optimal insurance contract in which the insured maximizes her expected utility and the insurer limits the variance of his risk exposure while maintaining the principle of indemnity and charging the premium according to the expected value principle. We derive the optimal...