Value-at-Risk Model Combination Using Artificial Neural Networks
نویسنده
چکیده
Value at Risk (VaR) has become the industry standard to measure the market risk. However, the selection of the VaR models is controversial. Simulation Results indicate Historical Simulation has significant positive bias, while GARCH (1,1) has has significant negative bias. Also HS adapts structural change slowly but stable, while GARCH adapts structural break rapidly but less stable. Thus the model selection is often unstable and cause high variability in the final estimation. This paper proposes VaR forecast combinations using Artificial Neural Networks (ANNs) instead of model selection. Based on Mean Loss Comparison, Violation Ratio and Christofferson’s conditional coverage test, both the simulation and real data results prove that the ANNs combinations have superior forecast performance than the individual VaR models. JEL classification: C22 C32 C45 C50
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