Dynamic Stock Markets Clustering

• Autorzy: Anna Czapkiewicz , Artur Machno

Warianty tytułu

Dynamiczne grupowanie stóp zwrotu

• Języki publikacji: EN

Abstrakty

W pracy przedstawiono dwa podejścia, służące do badania dynamicznej zależności pomiędzy finansowymi szeregami czasowymi. W pierwszym podejściu do badania zależności między szeregami czasowymi wykorzystano funkcje kopuli oraz dynamikę sterowaną ukrytym procesem Markowa, natomiast drugie podejście wykorzystuje wielowymiarowe procesy auto-regresyjne. W wyniku zastosowania obu podejść otrzymano dynamiczne korelacje pomiędzy badanymi szeregami czasowymi, które stanowiły podstawę do konstrukcji dynamicznego grupowania rynków finansowych.(abstrakt oryginalny)

Słowa kluczowe

EN

Financial markets; Time-series; Markov process

PL

Rynki finansowe; Szeregi czasowe; Procesy Markowa

• Czasopismo: Zeszyty Naukowe Wyższej Szkoły Ekonomii i Informatyki w Krakowie
• Rocznik: 2014
• Numer: z. 10
• Strony: 41--51

Twórcy

autor

Anna Czapkiewicz

• AGH University of Science and Technology Kraków, Poland

autor

Artur Machno

• AGH University of Science and Technology Kraków, Poland

Bibliografia

• Aielli G., Consistent estimation of large scale dynamic conditional correlations, University of Messina, Department of Economics, Statistics, Mathematics and Sociology, Working paper n. 47, 2008.
• Bartram S.M., Taylor S.J., Wang Y.H., The Euro and European financial market dependence, Journal of Banking & Finance, 2007, vol. 31, pp. 1461-1481.
• Bastos J.A., Caiado J., Clustering global equity markets with variance ratio tests, Centre for Applied Mathematics and Economics, Technical University of Lisbon, Portugal, 2011.
• Bollerslev Т., Modeling the Coherence in Short-Run Nominal Exchange Rates: A Midtivariate Generalized ARCH Model, Review of Economics and Statistics, 1990, vol. 72, pp. 498-505.
• Bonanno G., Lillo F., Mantegna R., Level of complexity in financial markets, Physica A. 2001, vol. 299, pp. 16-27.
• Cappiello L., Engle, R., Sheppard, K., Asymmetric dynamics in the correlations of global equity and bond returns, Journal of Financial Economics, 2006, vol. 4, pp. 537-572.
• Caiado J., Crato N., A GARCH-based method for clustering of financial time series: International stock markets evidence, Forthcoming in: Proceedings of the XIIth Applied Stochastic Models and Data Analysis International Conference, 2007.
• Chollete, L. Heinen, A. and Valdesogo, A., Modeling international financial returns with a multivariate regime switching copula. Journal of Financial Econometrics, 2009, vol. 7(4), pp. 437-480.
• Engle, R.F., Dynamie conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business and Economic Statistics, 2002, vol. 20, pp. 339-350.
• Hamilton J., Time series analysis, Princeton Univ. Press, 1994.
• Jondeau E., Rockinger M., The Copula-GARCH model of conditional dependencies: An international stock market application, Journal of International Money and Finance, 2006, vol. 25, pp. 827-853.
• Kenourgios D., Samitas A., Paltalidis N., Financial crises and stock market contagion in a multivariate time--varying asymmetric framework, Journal of International Financial Markets, Institutions & Money, 2011, vol. 21(1), pp. 92-106.
• Mantegna R.N., Hierarchical structure in financial markets, The European Physical Journal B, 1999, vol. 11, pp. 193-197.
• Musetti A.T.Y., Clustering methods for financial time series, Spring, 2012.
• Okimoto Т., New evidence of asymmetric dependence structures in international equity markets, Journal of Financial and Quantitative Analysis, 2008, vol. 43, pp. 787-815.
• Otranto E., Classifying the Markets Volatility with ARMA Distance Measures, Quademi di Statistica, 2004, vol. 6, pp. 1-19.
• Patton A.J., Modelling asymmetric exchange rate dependence. International Economic Review, 2006, vol. 47, pp. 527-556.
• Patton A.J., Copula-based Models for Financial Time Series, Handbook of financial time series, 2009.
• Pelletier D., Regime-switching for dynamic correlation, Journal of Econometrics, 2006, vol. 131, pp. 445-473.
• Piccolo, A distance measure for classifying ARIMA models, Journal of Time Series Analysis, 1990, vol. 11, pp. 153-164.
• Rodriguez J.C., Measuring Financial Contagion: A Copula Approach, Journal of Empirical Finance, 2007, vol. 14(3), pp. 401-423.
• Tse Y.K., Tsui A.K.C., A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations, Journal of Business and Economic Statistics, 2002, vol. 20, pp. 351-362.