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Value-at-risk and expected shortfall when there is long range dependence
Härdle, Wolfgang Karl; Mungo, Julius - 2008
, we estimate two long memory models, the Fractional Integrated Asymmetric Power-ARCH and the Hyperbolic-GARCH with …
Persistent link: https://www.econbiz.de/10010274140
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Tail Wags Dog? Time-Varying Information Shares in the Bund Market
Upper, Christian; Werner, Thomas - 2002
The flow of information between futures and spot prices may vary over time, in particular during periods of stress. This article analyses the information content of the Bund Future and German government bonds during 1998 and test whether it is constant over time. The use of high-frequency data...
Persistent link: https://www.econbiz.de/10010295741
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Real-time price discovery in stock, bond and foreign exchange markets
Andersen, Torben G.; Bollerslev, Tim; Diebold, Francis X.; … - 2004
We characterize the response of U.S., German and British stock, bond and foreign exchange markets to real-time U.S. macroeconomic news. Our analysis is based on a unique data set of high-frequency futures returns for each of the markets. We find that news surprises produce conditional mean...
Persistent link: https://www.econbiz.de/10010298290
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Return and Volatility Reactions to Monthly Announcements of Business Cycle Forecasts: An Event Study Based on High-Frequency Data
Steiner, Christian; Groß, Anne; Entorf, Horst - 2009
in a GARCH(1,1)-model, the paper reveals that significant increases in volatility only show up in the presence of …
Persistent link: https://www.econbiz.de/10010298727
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Modeling and Forecasting DAX Index Volatility
Lazarov, Zdravetz - 2004
The recent introduction of the realized variance measure defined as the sum of the squared intra-daily returns stamped on some high frequency basis has spurred the research in the field of volatility modeling and forecasting into new directions. First, the realized variance is a much better...
Persistent link: https://www.econbiz.de/10010263102
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Pricing of options under different volatility models
Herzberg, Markus; Sibbertsen, Philipp - 2004
In this paper we compare the price of an option with one year maturity of the German stock index DAX for several volatility models including long memory and leverage effects. We compute the price by applying a present value scheme as well as the Black-Scholes and Hull-White formulas which...
Persistent link: https://www.econbiz.de/10010296646
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Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach
Rankovic, Vladimir; Drenovak, Mikica; Uroševic, Branko; … - 2016
Conditional Heteroscedasticity (GARCH) volatility model. The optimization was performed by employing a Nondominated Sorting … to those obtained from applying more customary mean-multivariate GARCH and historical VaR models. The results hold true …
Persistent link: https://www.econbiz.de/10011431272
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Return based risk measures for non-normally distributed returns: An alternative modelling approach
Samunderu, Eyden; Murahwa, Yvonne T. - In: Journal of Risk and Financial Management 14 (2021) 11, pp. 1-48
Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical...
Persistent link: https://www.econbiz.de/10013201223
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One-factor-Garch models for German stocks: Estimation and forecasting
Kaiser, Thomas - 1996
factor models with GARCH dynamics (GARCH(1,1)-M, IGARCH(1,1)-M, Nonlinear Asymmetrie GARCH(1,1)-M and Glosten …-Jagannathan-Runkle GARCH(1,1)-M) and three different distributions for the disturbances (Normal, Student's t and Generalized Error Distribution … compared with forecasts based on individual GARCH(1,1)-M models, static factor models, naive, random walk and exponential …
Persistent link: https://www.econbiz.de/10010435583
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A review and some complements on quantile risk measures and their domain
Fuchs, Sebastian; Schlotter, Ruben; Schmidt, Klaus D. - In: Risks 5 (2017) 4, pp. 1-16
In the present paper, we study quantile risk measures and their domain. Our starting point is that, for a probability measure Q on the open unit interval and a wide class L Q of random variables, we define the quantile risk measure ϱ Q as the map that integrates the quantile function of a...
Persistent link: https://www.econbiz.de/10011996554