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Time Varying Sensitivities on a GRID architecture

  • Mattia Ciprian


  • Stefano d'Addona


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.

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Paper provided by EconWPA in its series Finance with number 0511007.

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Date of creation: 16 Nov 2005
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Handle: RePEc:wpa:wuwpfi:0511007
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