Dimension reduction in the computation of value-at-risk
نویسندگان
چکیده
Value-at-risk models can have many dimensions. We present two new algorithms for dimension reduction in value-at-risk algorithms with approximations. In the first method, we compute a reduced portfolio with a small mean square error for the residual and, in the second method, we use low rank approximations to find a reduced portfolio. The paper concludes with an example, estimating value-at-risk and hedging an option portfolio, with that demonstrates that dimension reduction leads to large savings in computational time without sacrificing accuracy.
منابع مشابه
Computation of Value-at-Risk: The Fast Convolution Method, Dimension Reduction and Perturbation Theory
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