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Abstracts
The proposed article presents a new approach to analyze the relationships between financial instruments. We use blind signal separation methods to decompose time series into the core components. The components common to the various instruments provide broad set of characteristics to describe the internal morphology of the time series. In this research a modified and extended version of AMUSE algorithm is used. The concept is presented based on real financial instruments.
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Pages
1018-1022
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Dates
published
2016-05
Contributors
author
- Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland
author
- Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland
author
- Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland
References
- [1] J.C. Hull, Options, Futures and Other Derivatives, Prentice Hall, Upper Saddle River 2014
- [2] A.N. Shiryaev, Essentials of Stochastic Finance: Facts, Models, Theory, World Sci., Singapore 1999
- [3] A. Cichocki, S. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, Wiley, Chichester 2003
- [4] P. Comon, Ch. Jutten, Handbook of Blind Source Separation: Independent Component Analysis and Applications, Academic Press, Boston 2010
- [5] R. Szupiluk, A. Cichocki, in: Proc. SPETO, Wydawnictwo Politechniki Śląskiej, Gliwice/Ustroń 2001, p. 485
- [6] L. Tong, V. Soon, Y.F. Huang, R. Liu, IEEE Trans. Circ. Syst. 38, 499 (1991), doi: 10.1109/31.76486
- [7] M.J. Fischer, Generalized Hyperbolic Secant Distributions with Applications to Finance, Springer, Heidelberg 2014, doi: 10.1007/978-3-642-45138-6
- [8] W. Perks, J. Inst. Actuar. 63, 1257 (1932)
- [9] R. Szupiluk, Multivariate decompositions for predictive Data Mining models aggregation, Oficyna Wydawnicza Szkoły Głównej Handlowej w Warszawie, Warszawa 2013 (in Polish)
- [10] R. Szupiluk, P. Wojewnik, T. Zabkowski, Lect. Notes Comp. Sci. 6594, 133 (2006), doi: 10.1007/11840930_14
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv129n523kz