Model Selection and Testing of Conditional and Stochastic Volatility Models
This paper focuses on the selection and comparison of alternative non-nested volatility models. We review the traditional in-sample methods commonly applied in the volatility framework, namely diagnostic checking procedures, information criteria, and conditions for the existence of moments and asymptotic theory, as well as the out-of-sample model selection approaches, such as mean squared error and Model Confidence Set approaches. The paper develops some innovative loss functions which are based on Value-at-Risk forecasts. Finally, we present an empirical application based on simple univariate volatility models, namely GARCH, GJR, EGARCH, and Stochastic Volatility that are widely used to capture asymmetry and leverage.
|Value-at-Risk forecasts, asymmetry, leverage, model confidence set, non-nested models, volatility model comparison, volatility model selection|
|Erasmus School of Economics|
|Econometric Institute Research Papers|
|Report / Econometric Institute, Erasmus University Rotterdam|
|Organisation||Erasmus School of Economics|
Caporin, M, & McAleer, M.J. (2010). Model Selection and Testing of Conditional and Stochastic Volatility Models (No. EI 2010-57). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–30). Erasmus School of Economics. Retrieved from http://hdl.handle.net/1765/20940