Krasnosselski, Nikolai Cremers, Heinz Sanddorf, Walter
Year of Publication:
Frankfurt School - Working Paper Series No. 208
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.
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