We estimate time varying risk sensitivities on a wide range of stocks' portfolios of the US market. We empirically test, on a 1926-2004 Monthly CRSP database, a classic one factor model augmented with a time varying specification of betas. Using a Kalman filter based on a genetic algorithm, we...

We investigate the gains obtained by using GRID, an innovative web-based technology for parallel computing, in a Risk Management application. We show, by estimating a parametric Value at Risk, how GRID computing offers an opportunity to enhance the solution of computationally demanding problems...

We estimate time varying risk sensitivities on a wide range of stocks' portfolios of the US market. We empirically test, on a 1926-2004 Monthly CRSP database, a classic one factor model augmented with a time varying specification of betas. Using a Kalman filter based on a genetic algorithm, we...