In: Journal of financial econometrics, 2011, vol. 9, no. 2, p. 281-313
This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that controls bootstrap instability due to outlying observations. A Monte Carlo simulation shows that our robust method provides more accurate VaR...
|
In: The review of financial studies, 2008, vol. 21, no. 3, p. 1223-1258
We propose a new method for pricing options based on GARCH models with filtered historical innovaions. In an incomplete market framework, we allow for different distributions of historical and pricing return dynamics, which enhances the model’s flexibility to fit market option prices. An extensive empirical analysis based on S&P 500 Index options shows that our model outperforms other...
|
In: Journal of the American Statistical Association, 2005, vol. 100, no. 470, p. 628-641
This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the classical likelihood-based tests for parametric...
|
Thèse de doctorat : Università della Svizzera italiana, 2004 ; 2004ECO001.
In the first part of the thesis, we study the local robustness of inference procedures of the conditional location and scale parameters in a stationary time series model. We first derive optimal bounded-influence estimators for the parameters of conditional location and scale models under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the...
|