Comparing the accuracy of density forecasts from competing GARCH models

Shamiri, Ahmed and Shaari, Abu Hassan and Isa, Zaidi (2008): Comparing the accuracy of density forecasts from competing GARCH models.

Abstract

In this paper we introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. Use of KLIC is practically attractive as well as convenient, given its equivalent of the widely used LR test. We include an illustrative simulation to compare a set of distributions, including symmetric and asymmetric distribution, and a family of GARCH volatility models. Our results on simulated data show that the choice of the conditional distribution appears to be a more dominant factor in determining the adequacy and accuracy (quality) of density forecasts than the choice of volatility model.

• Item Type: MPRA Paper
• Language: English
• Keywords: Density, conditional distribution, forecast accuracy, GARCH, KLIC
• Subjects: D - Microeconomics > D5 - General Equilibrium and Disequilibrium > D53 - Financial Markets; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

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