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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
  • Amari S., Cichocki A., Yang H.H. (1996) A new learning algorithm for blind signal separation, Advances in Neural Information Processing Systems NIPS-1995, MIT Press, Cambridge MA, pp. 757-763.
  • Back A.D., Weigend A.S. (1997) A first application of independent component analysis to extracting structure from stock returns, Int. Journal of Neural Systems 8(4), pp. 473-484.
  • Bollerslev T., Chou R.Y., Kroner K.F. (1992) ARCH Modelling in Finance: A Review of the Theory and Empirical Evidence, Journal of Econometrics 52, pp. 5-59.
  • 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.
  • Comon P. (1994) Independent component analysis, a new concept?, Signal Processing 36, pp. 287-314.
  • Embrechts P., Klüppelberg C., Mikosch T. (1997) Modelling Extremal Events for Insurance and Finance. Berlin, Springer.
  • Harvey A.C. (2013) Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series, Cambridge University Press.
  • Hyvärinen A., Karhunen J., Oja E. (2001) Independent Component Analysis, John Wiley.
  • Jajuga K. (2001) Value at Risk, Rynek Terminowy 13, pp. 18-20.
  • Jolliffe T. (1986) Principal Component Analysis, Springer-Verlag.
  • Jorion P. (2001) Value at Risk, McGraw-Hill.
  • J.P. Morgan (1995) Riskmetrics Technical Document, 3rd ed., New York.
  • Karian Z.A., Dudewicz E.J., McDonald P. (1996) The extended generalized lambda distribution system for fitting distributions to data: history, completion of theory, tables, applications, the Final Word on Moment Fits, Communications in Statistics - Simulation and Computation 25, pp. 611-642.
  • Karvanen J., Eriksson J., Koivunen V. (2002) Adaptive Score Functions for Maximum Likelihood ICA. VLSI Signal Processing 32, pp. 83-92.
  • Kouontchou P., Maillet B. (2007) ICA-based High Frequency VaR for Risk Management ESANN'2007 proceedings - European Symposium on Artificial Neural Networks, pp. 385-390.
  • Markowitz H.M. (1952) Portfolio Selection, The Journal of Finance 7, pp. 77-91.
  • Shiryaev A.N. (1999) Essentials of stochastic finance: facts, models, theory, World Scientific, Singapore.
  • Szupiluk R., Wojewnik P., Zabkowski T. (2004) Model Improvement by the Statistical Decomposition, Lecture Notes in Computer Science 3070, pp. 1199-1204.
  • Wu E.H., Yu P.L., Li W.K. (2006) Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-GARCH) models, International Journal of Neural Systems 16(5), pp. 371-382.
Typ dokumentu
Bibliografia
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