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A multivariate FGD technique to improve VaR computation in equity markets

Audrino, Francesco ; Barone-Adesi, Giovanni

In: Computational management science, 2005, vol. 2, no. 2, p. 87-106

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...

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Accurate short-term yield curve forecasting using functional gradient descent

Audrino, Francesco ; Trojani, Fabio

In: Journal of financial econometrics, 2007, vol. 5, no. 4, p. 591–623

We propose a multivariate nonparametric technique for generating reliable shortterm historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest rate series. It is computationally feasible in large dimensions and it can account for non- linearities...

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