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on Econometric Time Series |
| By: | Maria Rosa Nieto; Esther Ruiz |
| Abstract: | We review several procedures for estimating and backtesting two of the most important measures of risk, the Value at Risk (VaR) and the Expected Shortfall (ES). The alternative estimators differ in the way the specify and estimate the conditional mean and variance and the conditional distribution of returns. The results are illustrated by estimating the VaR and ES of daily S&P500 returns. |
| Keywords: | Backtesting, Extreme value, GARCH models, Leverage effect |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws087326&r=ets |
| By: | Andres M. Alonso; David Casado; Sara Lopez Pintado; Juan Romo |
| Abstract: | We propose using the integrated periodogram to classify time series. The method assigns a new element to the group minimizing the distance from the integrated periodogram of the element to the group mean of integrated periodograms. Local computation of these periodograms allows the application of the approach to nonstationary time series. Since the integrated periodograms are functional data, we apply depth-based techniques to make the classification robust. The method provides small error rates with both simulated and real data, and shows good computational behaviour. |
| Keywords: | Time series, Classification, Integrated periodogram, Data depth |
| JEL: | C14 C22 |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws087427&r=ets |
| By: | Antonio Garcia-Ferrer; Ester Gonzalez-Prieto; Daniel Pena |
| Abstract: | We propose a new multivariate factor GARCH model, the GICA-GARCH model , where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one. |
| Keywords: | ICA, Multivariate GARCH, Factor models, Forecasting volatility |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws087528&r=ets |