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.
D'Addona, S., Ciprian, M. (2007). Time Varying Sensitivities on a GRID Architecture. INTERNATIONAL JOURNAL OF THEORETICAL AND APPLIED FINANCE, 10 (2), 307-329.