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Value at risk: A quantile-based dis...

Nam, Doowoo.

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Value at risk: A quantile-based distribution approach for incorporating skewness and fat-tailedness.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Value at risk: A quantile-based distribution approach for incorporating skewness and fat-tailedness./
Author:	Nam, Doowoo.
Description:	131 p.
Notes:	Chairperson: Benton E. Gup.
Contained By:	Dissertation Abstracts International62-03A.
Subject:	Business Administration, Banking. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3008552
ISBN:	049317849X

Value at risk: A quantile-based distribution approach for incorporating skewness and fat-tailedness.
Nam, Doowoo.

Value at risk: A quantile-based distribution approach for incorporating skewness and fat-tailedness. - 131 p.

Chairperson: Benton E. Gup.

Thesis (Ph.D.)--The University of Alabama, 2001.

Non-normality, namely, asymmetry and/or leptokurtosis, has been a stylized fact for financial asset return distributions in the finance literature. Fat-tailedness, in particular, can have significant impact on the accuracy in computing value at risk (VaR), which became popular from the mid-1990s as a primary measure of market risks arising from the trading activities of banks.

ISBN: 049317849XSubjects--Topical Terms:

1018458
Business Administration, Banking.

Value at risk: A quantile-based distribution approach for incorporating skewness and fat-tailedness.
LDR:03310nam 2200313 a 45 001 936820
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035 $a (UnM)AAI3008552
035 $a AAI3008552
040 $a UnM $c UnM
100 1 $a Nam, Doowoo. $3 1260653
245 1 0 $a Value at risk: A quantile-based distribution approach for incorporating skewness and fat-tailedness.
300 $a 131 p.
500 $a Chairperson: Benton E. Gup.
500 $a Source: Dissertation Abstracts International, Volume: 62-03, Section: A, page: 1149.
502 $a Thesis (Ph.D.)--The University of Alabama, 2001.
520 $a Non-normality, namely, asymmetry and/or leptokurtosis, has been a stylized fact for financial asset return distributions in the finance literature. Fat-tailedness, in particular, can have significant impact on the accuracy in computing value at risk (VaR), which became popular from the mid-1990s as a primary measure of market risks arising from the trading activities of banks.
520 $a The normal VaR method might seriously underestimate VaR in the presence of fatter tails than predicted by a normal distribution. Its main variant, the normal GARCH method, based on conditional volatility, achieved some degree of success, but the problem of fat-tailedness still remains, though it is mitigated, after modeling volatilities using GARCH. On the other hand, the historical simulation method has gained popularity in practice because it is free from the non-normality problem, but it has the limitation of requiring a large amount of data to ensure reliable VaR estimation.
520 $a The primary purpose of this study is to develop a new methodology of VaR estimation to better handle skewness and fat-tailedness than the existing analytical VaR approaches, while at the same time being more flexible than the historical simulation approaches. The new approach adopts the <italic> g</italic>-and-<italic>h</italic> distribution and, hence, it is named the <italic> g</italic>-and-<italic>h</italic> VaR method. The proposed methodology permits greater simplicity and flexibility in accommodating non-normality and combines the merits of the normal and the historical simulation methods.
520 $a To validate the accuracy of the <italic>g</italic>-and-<italic>h</italic> VaR method, the empirical analysis is conducted, for several data sets of interest and foreign exchange rates, by comparing the frequency of exceedances with that of the normal method. The method is extended to compute VaR for portfolios. The results from backtesting show that the performance of both methods is equally satisfactory at the 95% confidence level, but at the 99% or higher levels the <italic>g</italic>-and-<italic>h</italic> method significantly outperforms the normal method. In addition, the procedure for decomposing portfolio VaR into its individual components is developed for the <italic> g</italic>-and-<italic>h</italic> method to examine the contribution of each constituent to total VaR.
590 $a School code: 0004.
650 4 $a Business Administration, Banking. $3 1018458
650 4 $a Economics, Finance. $3 626650
690 $a 0508
690 $a 0770
710 2 0 $a The University of Alabama. $3 1019361
773 0 $t Dissertation Abstracts International $g 62-03A.
790 $a 0004
790 1 0 $a Gup, Benton E., $e advisor
791 $a Ph.D.
792 $a 2001
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3008552