Time Varying Sensitivities on a GRID Architecture

We estimate time varying risk sensitivities on a wide range of stocks’ portfolios of the US market. We empirically test, on the Fama and French database, a classic one factor model augmented with a time varying specification of betas. Using a Kalman filter based on a genetic algorithm, we show that the model is able to explain a large part of the variability of stock returns. Furthermore we run a Risk Management application on a GRID computing architecture. By estimating a parametric Value at Risk, we show how GRID computing offers an opportunity to enhance the solution of computational demanding problems with decentralized data retrieval