Copula Approach in Multivariate Exchange Rate Analysis of Developing Countries in Eastern Europe

Sergei V. Busygin
1. Novosibirsk state university
2. Institute of economics and industrial engineering SB RAS
sergei257@gmail.com
Roman O. Sharypov
1. Novosibirsk state university
The material was received by the Editorial Board: 14/05/2019
Abstract
The work is aimed at modelling and analyzing joint behavior of currency exchange rates in 3 developing European countries – the Check Republic, Hungary, and Poland with the use of Copula functions. The study focuses only on inand Archimedean Copula families were estimated, and pair-Copula (vine) constructions of different families were considered. The work reveals the most appropriate in terms of consistency to the available data model based on the multivariate elliptical Student Copula. The work estimates the parameters of inherent interconnection narrowness of the currency rates under study based on the selected model built on the daily data in relation to RUB over the period 2007–2017.
Besides, the paper considers two approaches to interval forecasting – with the use of correlated multiplier function and wave function which accounts the most likely value range as of the given date. The time-horizon of the study was 30 days. In the conclusion of the work, a comparative analysis of the proposed approaches was carried out, and the comparison was made between the forecasts and actual figures.

Keywords
currency exchange rate, interdependence, Copula function, interval forecast

Funding
The research was carried out with the plan of IEIE SB RAS, project XI.173.1.1. № АААА-А17-117022250125-4

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References
  1. Groshev O. Time varying vine copulas for multivariate returns. Quantile, 2014, тo. 12, p. 53–67. (in Russ.)
  2. Embrechts P., Dias A. Dynamic copula models for multivariate high-frequence data in finance. Preprint. University of Warwick, UK, 2014.  
  3. Deng L., Ma C., Yang W. Portfolio Optimization via Pair Copula-GARCH-EVT-CVaR Model.Systems Engineering Procedia, 2011, vol. 2, p. 171–181 DOI 10.1016/j.sepro.2011.10.020
  4. Patton A. J. Copula Methods for Forecasting Multivariate Time Series. Elsevier B.V., Handbookof Economic Forecasting, 2013. vol. 2, pt. B, p. 899–960. DOI 10.1016/B978-0-444-62731-5.00016-6
  5. Creal D., Koopman S. J., Lucas A. Generalized autoregressive score models with applications.Journal of Applied Econometrics, 2013, vol. 28, p. 777–795. DOI 10.1002/jae.1279
  6. Genest C., Remillard B., Beaudoin D. Goodness-of-fit tests for copulas: A review and apower study. Insurance: Mathematics and Economics, 2009, vol. 44, no. 2, p. 199–213.
  7. Chen Y.-T. Moment Tests for Density Forecast Evaluation in the Presence of Parameter EstimationUncertainty. Journal of Forecasting, 2011, vol. 30, p. 409–450.
  8. Rivers D., Vuong Q. Model Selection Tests for Nonlinear Dynamic Models. The EconometricsJournal, 2002, vol. 5, no. 1, p. 1–39. DOI 10.1111/1368-423X.t01-1-00071
  9. Penikas H. Hierarchical copulas in investment portfolio risk modeling. Applied Econometrics, 2014, vol. 35, no. 3, p. 18–38.
  10. Sklar A. Fonctions de repartition a n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Universite de Paris, 1959, vol. 8, p. 229–231.
  11. Nelsen R. B. An introduction to Copulas. New York, Springer, 2006, 276 p.
  12. Antonov I. N., Knyazev A. G., Lepekhin O. A. Copula models of the joint distribution of exchange rates. World of Economics and Management, 2016, vol. 16, no. 4, p. 20–38. (in Russ.)
  13. Fantazzini D. Analysis of Multidimensional Probability Distributions with Copula Functions.Part 1. Applied Econometrics, 2011, vol. 22, no. 2, p. 98–134 (in Russ.)
  14. Penikas H. Financial applications of copula-models. Journal of the New Economic Association,2010, no. 7, p. 24–44 (in Russ.)
  15. Blagoveschensky Yu. Basics of copula’s theory. Applied Econometrics, 2012, no. 26 (2), p. 113–130 (in Russ.)
  16. Brechmann E. C., Schepsmeier U. Modeling Dependence with C- and D-Vine Copulas: TheR Package CDVine. Journal of Statistical Software, 2013, vol. 52 (3), p. 1–27.
  17. Yan J. Enjoy the Joy of Copulas: With a Package copula. Journal of Statistical Software,2007, vol. 21 (4), p. 1–21. 
  18. Kojadinovic I., Yan J. Modeling Multivariate Distributions with Continuous Margins Usingthe copula R Package. Journal of Statistical Software, 2010, Vol. 34 (9), p. 1–20. 
References: Busygin S. V., Sharypov R. O. Copula Approach in Multivariate Exchange Rate Analysis of Developing Countries in Eastern Europe. World of Economics and Management. 2019. vol. 19, no. 3. P. 58–72. DOI: 10.25205/2542-0429- 2019-19-3-58-72