Seminar Details

Seminar Details


Nov 28

3:30 pm

Cholesky Stochastic Volatility

Hedibert Freitas Lopes


Chicago Booth - Associate Professor of Econometrics and Statistics

Multivariate volatility has many important applications in finance, including asset allocation and risk management. Estimating multivariate volatility, however, is not straightforward because of two major difficulties. The first difficulty is the curse of dimensionality. For p assets, there are p(p+1)=2 volatility and cross-correlation series. In addition, the commonly used volatility models often have many parameters, making them impractical for real application. The second difficulty is that the conditional covariance matrix must be positive definite for all time points. This is not easy to maintain when the dimension is high. In this paper, we develop a new approach to modeling multivariate volatility. We name our approach Cholesky Stochastic Volatility (CSV). Our approach is Bayesian and we carefully derive the prior distributions with an appealing practical flavor that allows us to search for simplifying structure without placing hard restrictions on our model space. We illustrate our approach by a number of real and synthetic examples, including a real application based twenty of the S&P100 components