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Independent Component Analysis Filtration for Value at Risk Modelling

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Artificial Neural Networks and Machine Learning – ICANN 2013 (ICANN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8131))

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Abstract

In this article we present independent component analysis (ICA) applied to the concept of value at risk (VaR) modelling. The use of ICA decomposition enables to extract components with particular statistical properties that can be interpreted in economic terms. However, the characteristic of financial time series, in particular the nonstationarity in terms of higher order statistics, makes it difficult to apply ICA to VaR right away. This requires using adequate ICA algorithms or their modification taking into account the statistical characteristics of financial data.

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Author information

Authors and Affiliations

  1. Warsaw School of Economics, Al. Niepodleglosci 162, 02-554, Warsaw, Poland

    Ryszard Szupiluk & Piotr Wojewnik

  2. Warsaw University of Life Sciences, Nowoursynowska 159, 02-776, Warsaw, Poland

    Tomasz Ząbkowski

Authors
  1. Ryszard Szupiluk
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  2. Piotr Wojewnik
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  3. Tomasz Ząbkowski
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Editor information

Editors and Affiliations

  1. Faculty Automation,, Technical University of Sofia, 8 St. Kl. Ohridski Blvd., 1000, Sofia, Bulgaria

    Valeri Mladenov

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  3. Institute of Neural Information Processing, University of Ulm, 89075, Ulm, Germany

    Günther Palm

  4. Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  5. Department of Computer Science, University of Milano, Via Comelico, 39, 20135, Milano, Italy

    Bruno Appollini

  6. Knowledge Engineering, School of Computing and Mathematical Sciences, Auckland University of Technology, 120 Mayoral Drive, 3rd floor, 1010, Auckland, New Zealand

    Nikola Kasabov

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Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2013). Independent Component Analysis Filtration for Value at Risk Modelling. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_69

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  • DOI: https://doi.org/10.1007/978-3-642-40728-4_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40727-7

  • Online ISBN: 978-3-642-40728-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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