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Elliptical Stable Distributions
Omelchenko, Vadym
The elliptical stable distributions represent a symmetric subfamily of the stable distributions. Their advantage contrary to the general stable distributions consists in their easy-to-use property and the highest resemblance to the normal distribution. They enable an easy representation of the dependence structure of the margins by means of a matrix Q the same as in case of the normal distribution. In general, the dependence structure between margins is given in form of a spectral measure which can be even continuous. The computations and approximations require so much time that it just the fact that many practitioners avoid using general stable distributions. The general stable distributions possess so many additional properties that they barely take after the multivariate normal distribution. But the multi-variate elliptical stable distributions can be easily simulated and the estimation of their parameters can be obtained by methods whose preciseness is almost the same as the one of the maximum likelihood methodology.
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Comovement of Central European stock markets using wavelet coherence: Evidence from high-frequency data
Baruník, Jozef
;
Vácha, Lukáš
;
Krištoufek, Ladislav
In this paper, we contribute to the literature on international stock market comovement and contagion. The novelty of our approach lies in usage of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock market returns in completely different way. Major part of economic time series analysis is done in time or frequency domain separately. Wavelet analysis can combine these two funda- mental approaches, so we can work in time-frequency domain. Using wavelet coherence, we have found very interesting dynamics of cross-correlations be- tween Central European and Western European stock markets. We analyze the high-frequency (5 minute) and low-frequency (daily) data of Czech (PX), Hungarian (BUX) and Polish (WIG) stock indices with a benchmark of German stock index (DAX) on the period of 2008-2009. Our findings provide possibility of a new approach to financial risk modeling.
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Bayesian vector auto-regression model with Laplace errors applied to financial market data
Šindelář, Jan
The article presents alternative version of Bayesian vector auto-regression model with Laplace distributed innovations. Bayesian estimation in such model is more computationally demanding than estimation in a model with normally distributed innovations, but because of the heavier tails of Laplace distribution, it is more robust. In the article I try to present the way of proceeding with the estimation, obtaining a full posterior distribution of the parameters as a result. At the end an efficient algorithm is sketched, but this part of the research is still unfinished.
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