Evaluating the Accuracy of Value-at-Risk Models－The Case of Taiwan Weighted Stock Index
Date Issued
2008
Date
2008
Author(s)
Tang, Fu-Chia
Abstract
After reading lots of papers and related articles, I find that most people use extreme value theory to calculate value-at-risk. The reason is that it is not necessary to assume the distribution of the portfolio in advance by using the theory. All that we need to do is to estimate the tail distribution of the portfolio. Although the theory is adopted commonly by everyone, it is fine to use general estimating models, such as historical simulation method, Monte Carlo simulation method, and GARCH model, with no extreme values. Extreme value theory can provide a more accurate value-at-risk with extreme values. In The Journal of Risk Finance of Jan. 2008, Colin J. Thompson and Michael A. McCarthy propose another method for evaluating value-at-risk. This paper uses Taiwan weighted stock index to evaluate the accuracy of this new method, historical simulation method, model of non-conditional Normal distribution , and the two models of extreme value theory, which are Block Maximum Method (BMM) and Peak over Threshold Method (POT). According to value-at-risks calculated by different models, value-at-risk of extreme value theory is usually bigger than others. Comparing to other models, extreme value theory usually needs to estimate parameters in advance. That makes the method generate errors easily. If you are not sure that there will be extreme values, it will be better to use other methods.
Subjects
extreme value theory(EVT)
estimation of parameters
generalized extreme value distribution(GEV)
generalized Pareto distribution(GPD)
value-at-risk(VaR)
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-97-R95724075-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):5a85af825cf22d56bf8118cfb1065a99