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A multivariate FGD technique to improve VaR computation in equity markets

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  • Francesco Audrino
  • Giovanni Barone-Adesi

Abstract

It is difficult to compute Value-at-Risk (VaR) using multivariate models able to take into account the dependence structure between large numbers of assets and being still computationally feasible. A possible procedure is based on functional gradient descent (FGD) estimation for the volatility matrix in connection with asset historical simulation. Backtest analysis on simulated and real data provides strong empirical evidence of the better predictive ability of the proposed procedure over classical filtered historical simulation, with a resulting significant improvement in the measurement of risk. Copyright Springer-Verlag Berlin/Heidelberg 2005

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File URL: http://hdl.handle.net/10.1007/s10287-004-0028-3
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Bibliographic Info

Article provided by Springer in its journal Computational Management Science.

Volume (Year): 2 (2005)
Issue (Month): 2 (03)
Pages: 87-106

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Handle: RePEc:spr:comgts:v:2:y:2005:i:2:p:87-106

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Cited by:
  1. Fabio Trojani & Francesco Audrino, 2005. "Accurate Yield Curve Scenarios Generation using Functional Gradient Descent," Computing in Economics and Finance 2005 14, Society for Computational Economics.
  2. Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(4), pages 591-623, Fall.
  3. Pritsker, Matthew, 2006. "The hidden dangers of historical simulation," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 561-582, February.

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