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Value-at-Risk Model Combination Using Artificial Neural Networks
Company: Emory University
Year Of Publication: 2005
Month Of Publication: August
Pages: 28
Download Count: 812
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Comment Num: 0
Language: EN
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Who Can Read: Free
Date: 6-25-2005
Publisher: Administrator
Summary
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 Simulationhas signi¯cant positive bias, while GARCH (1,1) has has signi¯cant negative bias. Also HS adaptsstructural 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 ¯nal estimation. Thispaper proposes VaR forecast combinations using Arti¯cial Neural Networks (ANNs) instead ofmodel selection. Based on Mean Loss Comparison, Violation Ratio and Christo?erson's condi-tional coverage test, both the simulation and real data results prove that the ANNs combinationshave superior forecast performance than the individual VaR models.
THIS PAPER WAS PREVIOUSLY TITLE VALUE-AT-RISK MODELS COMBINA
Author(s)
Liu, Yan Sign in to follow this author
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