Time Varying Sensitivities on a GRID architecture
Mattia Ciprian and
Stefano d'Addona ()
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Finance from University Library of Munich, Germany
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 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.
JEL-codes: G (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpfi:0511007
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