Mixture transition distribution (MTD) modeling of heteroscedastic time series
Computational Statistics & Data Analysis
Evaluating volatility forecasts in option pricing in the context of a simulated options market
Computational Statistics & Data Analysis
Estimating confidence regions over bounded domains
Computational Statistics & Data Analysis
The impact of general non-parametric volatility functions in multivariate GARCH models
Computational Statistics & Data Analysis
Unobserved component models with asymmetric conditional variances
Computational Statistics & Data Analysis
Bootstrap prediction for returns and volatilities in GARCH models
Computational Statistics & Data Analysis
The asymptotic convexity of the negative likelihood function of GARCH models
Computational Statistics & Data Analysis
Saddlepoint approximations for the doubly noncentral t distribution
Computational Statistics & Data Analysis
Asymmetric multivariate normal mixture GARCH
Computational Statistics & Data Analysis
A comparison of GARCH models for VaR estimation
Expert Systems with Applications: An International Journal
Self-similarity in financial markets: A fractionally integrated approach
Mathematical and Computer Modelling: An International Journal
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The Asymmetric Power GARCH (APGARCH) model allows a wider class of power transformations than simply taking the absolute value or squaring the data as in classical heteroscedastic models. A dynamic estimation is used to compare the three GARCH families and examine their forecasting performances in a value-at-risk setting. The results suggest that the optimal power transformation obtained with the APGARCH model is virtually never statistically different from 1 or 2. Moreover, although some indices switch between these two values over the time, the measures of accuracy and efficiency used to assess the performance of VaR forecasts indicate that the additional flexibility brought by the APGARCH model provides little, if any, improvements for risk management.