Consortium of Swiss Academic Libraries

What Drives Short Rate Dynamics? A Functional Gradient Descent Approach

Audrino, Francesco

In: Computational Economics, 2012, vol. 39, no. 3, p. 315-335

Università della Svizzera italiana

A dynamic model of expected bond returns : a functional gradient descent approach

Audrino, Francesco ; Barone-Adesi, Giovanni

In: Computational statistics & data analysis, 2006, vol. 51, no. 4, p. 2267-2277

A multivariate methodology based on Functional Gradient Descent to estimate and forecast time-varying expected bond returns is presented and discussed. Backtesting this procedure on US monthly data, empirical evidence of its strong forecasting potential in terms of the accuracy of the predictions is collected. The proposed methodology clearly outperforms the classical univariate analysis used...

Università della Svizzera italiana

Average conditional correlation and tree structures for multivariate GARCH models

Audrino, Francesco ; Barone-Adesi, Giovanni

In: Journal of forecasting, 2006, vol. 25, no. 8, p. 579–600

We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions...

Università della Svizzera italiana

A general multivariate threshold GARCH model with dynamic conditional correlations

Audrino, Francesco ; Trojani, Fabio

In: Journal of business & economic statistics, 2011, vol. 29, no. 1, p. 138-149

We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data, and the model conditional covariance matrix is ensured to be positive definite. We study the empirical performance of our model in...

Università della Svizzera italiana

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...

Università della Svizzera italiana

Semi-parametric implied volatility surface models and forecasts based on a regression tree-boosting algorithm

Colangelo, Dominik ; Audrino, Francesco (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2009 ; 2009ECO006.

A new methodology for semi-parametric modelling of implied volatility surfaces is presented. This methodology is dependent upon the development of a feasible estimating strategy in a statistical learning framework. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and...

Università della Svizzera italiana

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...