BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Doman Ryszard (Adam Mickiewicz University in Poznań, Poland)
Title
Forecasting the Dependence Between Polish Financial Returs
Source
FindEcon Monograph Series : advances in financial market analysis, 2007, nr 3, s. 45-58, rys., tab., bibliogr. s. 58
Issue title
Financial markets : principles of modeling forecasting and decision-making
Keyword
Modelowanie matematyczne, Kalkulacja stopy zwrotu, Złoty polski, Euro, Dolar amerykański (USD)
Mathematical modeling, Rate of return calculation, Polish Zloty, Euro, United States dollar (USD)
Abstract
In this chapter, for a given one-parameter copula family, we propose a parametric conditional copula model in which the copula parameter is allowed to evolve over time, and the evolution is governed by some specification involving Kendall's tau dependence measures of the marginal returns. The model is applied to modelling and forecasting the conditional dependence in the case of two pairs of Polish financial returns: exchange rates EUR/PLN and USD/PLN, and stock indices WIG20 and MIDWIG. (fragment of text)
Accessibility
The Main Library of the Cracow University of Economics
The Main Library of Poznań University of Economics and Business
Full text
Show
Bibliography
Show
  1. Bauwens E., Laurent S., Rombouts J.V.K. (2003), Multivariate GARCH Models: A Survey, Core Discusión Paper 1.
  2. Boyer B.H., Gibson M.S., Loretan M. (1999), Pitfalls in Tests for Changes in Correlations, International Finance Discussion Papers, 597, Washington, DC: Board of Governors of the Federal Reserve System.
  3. Dias A., Embrechts P. (2003), Dynamic Copula Models for Multivariate High-Frequency Data in Finance, Working paper, Zurich: Department of Mathematics, ETH.
  4. Embrechts P., McNeil A., Straumann D. (2002), Correlation and Dependence in Risk Management: Properties and Pitfalls, in: Risk Management: Value at Risk and Beyond, Cambridge: Cambridge University Press, 176-223.
  5. Engle R.F. (2002), Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models, Journal of Business and Economic Statistics, 20, 339-350.
  6. Fernández C., Osiewalski J., Steel M. F. J. (1995), Modelling and Inference with -Spherical Distributions, Journal of the American Statistical Association, 90, 1331-1340.
  7. Goorbergh R.W.J., van den Genest C., Werker B.J.M. (2005), Bivariate Option Pricing Using Dynamic Copula Models, Insurance: Mathematics and Economics, 37, 101-114.
  8. Hu L. (2004), Dependence Patterns across Financial Markets: A Mixed Copula Approach, Working paper, Department of Economics, The Ohio State University.
  9. Jondeau E., Rockinger M. (2002), Conditional Dependency of Financial Series: The Copula-GARCH Model, FAME Research Paper, 69.
  10. Lambert P., Laurent S. (2001), Modelling Financial Time Series Using GARCH-Type Models with a Skewed Student Distribution for the Innovations, Discussion Paper, 0125, Institut de Statistique, Université Catholique de Louvain.
  11. Lee T.H., Long X. (2005), Copula-Based Multivariate GARCH Model with Uncorrelated Dependent Standardized Returns, Riverside: Department of Economics, University of California.
  12. Nelsen R.B. (1999), An Introduction to Copulas, New York: Springer Verlag.
  13. Patton A.J. (2001), Modelling Time-Varying Exchange Rate Dependence Using the Conditional Copula, Working paper, San Diego: University of California.
  14. Schweizer B., Wolf E. F. (1981), On Nonparametric Measures of Dependence for Random Variables, Annals of Statistics 9, 879-885.
  15. Sklar A. (1959), Fonctions de repartition á n dimensions et leurs marges, Publicatons de Institut Statistique de Universite de Paris, 8, 229-231.
Cited by
Show
Language
eng
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu