Multivariate extremes, aggregation and risk estimation
By H. A. Hauksson, Michel M. Dacorogna, Thomas Domenig, Ulrich A. Müller, Gennady Samorodnitsky. 2001
74 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/6f176516a4a7bf3ab8fc368c46833c4060c163af
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- A. C. Davison
- Marco Rocco
- Ramazan Gencay
- Wolfgang Karl Härdle
- John H. J. Einmahl
- Vladimir Spokoiny
- F. Selçuk
- Juliette Blanchet
In Finance
- A. C. Davison
- John H. J. Einmahl
- Marco Rocco
- Raphaël Huser
- Juliette Blanchet
- Wolfgang Karl Härdle
- Vladimir Spokoiny
- Ramazan Gencay
Citations in the corpus
- Nonparametric estimation of the spectral measure of an extreme value distribution (2001, background): “Apart from Einmahl, de Haan and Huang (1993) and Einmahl, de Haan and Sinha (1997), we are not aware of other work on estimating the spectral measure starting from observations in the domain of attraction.”
- Extreme US Stock Market Fluctuations in the Wake of 9/11 (2003, background): “which has a standard normal distribution in large samples.(15) For our sample of US sectoral stock indices, we test whether co-crashes are more likely than co-booms (asymmetry test) and whether the potential for co-crashes has increased after 9/11 (structural breaks test).”
- Extreme Value Theory for Finance: A Survey (2012, background): “…Werner and Upper (2004) study tick data (approximately covering five years) of German Bund futures for a total amount of 13.4 million trades, while Hauksson et al. (2001) consider 10 minute to biweekly returns of the foreign exchange market and conclude that high frequency data can significantly…”
- Local quantile regression (2010, methodology): “Hauksson et al. (2001) and Embrechts and Straumann (1999) provide good access to the literature on tail dependence and Value at Risk.”
- Extreme value theory and Value-at-Risk: Relative performance in emerging markets (2004, methodology): “A Hill-plot is constructed such that estimated n is plotted as a function of k upper order statistics or the threshold. A threshold is selected from the plot where the shape parameter n is fairly stable. The Hill estimator is proven to be a consistent estimator of n = 1/a for fat-tailed distribution”
- Spatial modeling of extreme snow depth (2011, background): “Financial examples are currency exchange rate data [Hauksson et al. (2001)], swap rate data [Hsing, Klüppelberg and Kuhn (2004)] and stock market returns [Poon, Rockinger and Tawn (2003, 2004)], and environmental examples are rainfall data [Schlather and Tawn (2003)], oceanographical data [de Haan…”
- Empirical properties of asset returns: stylized facts and statistical issues (2001, background): “Some recent theoretical work has been done in this direction using copulas [108] and multivariate extreme value theory [64, 112, 113], but a lot remains to be done on empirical grounds.”