Robust volatility forecasts and model selection in financial time series
AbstractIn order to cope with the stylized facts of financial time series, many models have been proposed inside the GARCH family (e.g. EGARCH, GJR-GARCH, QGARCH, FIGARCH, LSTGARCH) and the stochastic volatility models (e.g. SV). Generally, all these models tend to produce very similar results as concerns forecasting performance. Most of the time it is difficult to choose which is the most appropriate specification. In addition, all these models are very sensitive to the presence of atypical observations. The purpose of this paper is to provide the user with new robust model selection procedures in financial models which downweight or eliminate the effect of atypical observations. The extreme case is when outliers are treated as missing data. In this paper we extend the theory of missing data to the family of GARCH models and show how to robustify the loglikelihood to make it insensitive to the presence of outliers. The suggested procedure enables us both to detect atypical observations and to select the best models in terms of forecasting performance.
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Bibliographic InfoPaper provided by Department of Economics, Parma University (Italy) in its series Economics Department Working Papers with number 2006-SE02.
Length: 23 pages
Date of creation: 2006
Date of revision:
GARCH models; extreme value; robust estimation;
Find related papers by JEL classification:
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-10-21 (All new papers)
- NEP-CFN-2006-10-21 (Corporate Finance)
- NEP-ECM-2006-10-21 (Econometrics)
- NEP-ETS-2006-10-21 (Econometric Time Series)
- NEP-FIN-2006-10-21 (Finance)
- NEP-FMK-2006-10-21 (Financial Markets)
- NEP-FOR-2006-10-21 (Forecasting)
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