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Using Value-at-Risk to Evaluate Financial Returns Distributions
Year Of Publication: 2013
Month Of Publication: December
Resource Link: Click here to open
Pages: 50
Download Count: 0
View Count: 1473
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 1-6-2014
Publisher: Administrator
In order to construct Value-at-Risk forecasts that take into account the serial autocorrelation and non-normality observed in financial returns, we develop a two-dimensional approach that combines a scalar BEKK MGARCH model with an assumption on the returns distribution. The set of distributions we use comprises the Normal, Student, MEP and their skewed equivalents, obtained via a transformation function. Using a sample of 10 assets from the Dow Jones Industrial Average over a period of eight years, we estimate the parameters of the sBEKK model via Maximum Likelihood and then compute a series of 700 one-step ahead forecasts of the conditional covariance matrix by use of a rolling fixed-window scheme. The out-of-sample VaR forecasts are constructed using an equal-weighting portfolio technique along with the covariance matrix forecasts. Finally, we assess the accuracy of the VaR forecasts for each distribution by implementing a series of statistical backtesting procedure. The results unanimously show that the skew-Student distribution yields the most accurate VaR across all tests. By contrast, the ranking of the remaining distribution
Braione, Manuela Sign in to follow this author
Scholtes, Nicolas K. Sign in to follow this author
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