Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/91708
Authors: 
Krasnosselski, Nikolai
Cremers, Heinz
Sanddorf, Walter
Year of Publication: 
2014
Series/Report no.: 
Working Paper Series, Frankfurt School of Finance & Management 208
Abstract (Translated): 
The globalisation on financial markets and the development of financial derivatives has increased not only chances but also potential risk within the banking industry. Especially market risk has gained major significance since market price variation of interest rates, stocks or exchange rates can bear a substantial impact on the value of a position. Thus, a sound estimation of the volatility in the market plays a key role in quantifying market risk exposure correctly. This paper presents GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models with Generalised AutoRegressive Conditional Heteroskedasticity are characterised by the ability to estimate and forecast time-varying volatility. In this paper, the estimation of conditional volatility is applied to Value at Risk measurement. Univariate as well as multivariate concepts are presented for the estimation of the conditional volatility.
Subjects: 
ARCH
Backtesting
BEKK-GARCH
Bootstrapping
CCC-GARCH
Conditional Volatility
Constant Mean Model
DCC-GARCH
EWMA
GARCH
GJR-GARCH
Heteroskedasticity
IGARCH
Mandelbrot
Misspecification Test
Multivariate Volatility Model
Stylized Facts
Univariate Volatility Model
Value at Risk
Volatility Clustering
JEL: 
C01
C02
C12
C13
C14
C15
C22
C32
C51
C52
C53
G32
G38
Document Type: 
Working Paper

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