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2013 | 14(XIV) | nr 2 | 240-250
Tytuł artykułu

Multivariate Decompositions for Value at Risk Modelling

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Języki publikacji
EN
Abstrakty
EN
This paper presents the application of independent component analysis (ICA) for value at risk modelling (VaR). The probabilistic models fitted to hidden components from the time series help to identify the independent factors influencing the portfolio value. An important issue here is the choice of the ICA algorithm, especially taking into account the characteristics of the instruments with respect to higher-order statistics. The proposed ICA-VaR concept has been tested on transactional data of selected stocks listed on Warsaw Stock Exchange. (original abstract)
Twórcy
  • Szkoła Główna Handlowa w Warszawie
  • Szkoła Główna Handlowa w Warszawie
  • Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
Bibliografia
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  • Cardoso J.F. (1999) High-order contrasts for independent component analysis, Neural Computation 11, pp. 157-192.
  • Chen Y., Hardle W., Spokoiny V. (2007) Portfolio value at risk based on independent component analysis Journal of Computational and Applied Mathematics 205(1), pp. 594-607.
  • Cichocki A., Amari S. (2002) Adaptive Blind Signal and Image Processing, John Wiley, Chichester.
  • Cichocki A., Sabala I., Choi S., Orsier B., Szupiluk R. (1997) Self adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with unknown number of sources and additive noise, Proc. of NOLTA-97, vol. 2, Hawaii USA, pp. 731-734.
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Typ dokumentu
Bibliografia
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