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Hybrid Functional Autoregressive Modelling of Non-parametric Density for Improved Value-at-Risk Analysis
Year Of Publication: 2011
Month Of Publication: July
Pages: 45
Download Count: 1
View Count: 1509
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 1-1-2013
Publisher: Administrator
Functional Auto-regressive modelling of the non-parametric density function is proposed to improve Value-at-Risk (VaR) analysis by taking into account the relative advantages of parametric and non-parametric models in a hybrid manner. In particular, this approach enables us to use the intraday information for forecasting the time-varying daily return density function. Monte Carlo simulation study and empirical evaluations of VaR, based on thirty components of the Dow Jones Industrial Average and their equal weighted portfolio, clearly demonstrate that the overall performance of the proposed hybrid model is superior to those of both the parametric and the non-parametric models.
Zhang, Jacky Qi Sign in to follow this author
Cai, Charlie X. Sign in to follow this author
Kim, Minjoo Sign in to follow this author
Shin, Yongcheol Sign in to follow this author
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