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Extreme volatility dependence in exchange rates
Dependencia extrema de la volatilidad en los tipos de cambio
Dependência extrema da volatilidade nas taxas de câmbio
DOI:
https://doi.org/10.15446/cuadecon.v40n82.79400Palabras clave:
Exchange Rates, Volatility Modeling, Tail Dependence (en)taxa de câmbio, modelagem de volatilidade, dependência da cauda (pt)
tipo de cambio, modelación de volatilidad, dependencia de cola (es)
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This paper aims to analyse asymmetric volatility dependence in the exchange rate between the British Pound, Japanese Yen, Euro, and Mexican Peso compared to the U.S. dollar during different periods of turmoil and calm sub-periods between (1994-2018). GARCH and TARCH models are employed to model conditional
Este artículo analiza la dependencia asimétrica de la volatilidad de los tipos de cambio entre la libra esterlina, yen japonés, euro y peso mexicano en términos del dólar americano, en un periodo que comprende episodios de calma e incertidumbre (1994-2018). Los modelos GARCH y TARCH se emplean para modelar la volatilidad del tipo de cambio. Una vez que la volatilidad se estima, se calcula la dependencia de la cola superior e inferior, para cada subperiodo: 1994-1999, 2000-2007, 2007-2012, 2013-2018. La dependencia bivariada de la volatilidad cambiaria muestra alta dependencia en la cola inferior y baja dependencia en la
cola superior.
Este artigo analisa a dependência assimétrica da volatilidade das taxas de câmbio entre a libra esterlina, o iene japonês, o euro e o peso mexicano em relação ao dólar norte-americano, em um período que inclui episódios de calma e incerteza (1994-2018). Os modelos GARCH e TARCH são usados para modelar a volatilidade da taxa de câmbio. Uma vez que a volatilidade é estimada, calcula-se a dependência da cauda superior e inferior, para cada subperíodo: 1994-1999, 2000-2007, 2007-2012, 2013-2018. A dependência bivariada da volatilidade da taxa de câmbio mostra alta dependência na cauda inferior e baixa dependência na cauda superior.
Referencias
Arreola Hernandez, J., Hammoudeh, S., Nguyen, D. K., Al Janabi, M. A., & Reboredo, J. C. (2017). Global financial crisis and dependence risk analysis of sector portfolios: A vine copula approach. Applied Economics, 49(25), 2409-2427. doi: https://doi.org/10.1080/00036846.2016.1240346
Boako, G., Tiwari, A. K., Ibrahim, M., & Ji, Q. (2018). Analysing dynamic dependence between gold and stock returns: Evidence using stochastic and full-range tail dependence copula models. Finance Research Letters, 31, 391-397. doi: https://doi.org/10.1016/j.frl.2018.12.008
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994). ARCH models. Handbook of econometrics, 4, 2959-3038. https://doi.org/10.1016/S1573-4412(05)80018-2. Available at: http://www.u.arizona.edu/~rlo/readings/278762.pdf
Bouri, E., Gupta, R., Lau, C. K. M., Roubaud, D., & Wang, S. (2018). Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles. The Quarterly Review of Economics and Finance, 69, 297-307. doi: https://doi.org/10.1016/j.qref.2018.04.003
Casas Monsegny, M., & Cepeda Cuervo, E. (2008). Modelos ARCH, GARCH y EGARCH: aplicaciones a series financieras. Cuadernos de economía, 27(48), 287-319. http://www.scielo.org.co/scielo.php?pid=S0121-47722008000100011&script=sci_arttext&tlng=en
Fortin, I., & Kuzmics, C. (2002). Tail-dependence in stock-return pairs. Intelligent Systems in Accounting, Finance & Management, 11(2), 89-107. doi: https://doi.org/10.1002/isaf.216
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
Grover, V. (2015). Identifying Dependence Structure among Equities in Indian Markets using Copulas. Available at https://mpra.ub.uni-muenchen.de/66302/1/MPRA_paper_66302.pdf
Hamao, Y., Masulis, R. W., & Ng, V. (1990). Correlations in price changes and volatility across international stock markets. The review of financial studies, 3(2), 281-307. doi: https://doi.org/10.1093/rfs/3.2.281
Hsin, C. W. (2004). A multilateral approach to examining the comovements among major world equity markets. International review of financial analysis, 13(4), 433-462.
Ji, Q., Liu, B. Y., Zhao, W. L., & Fan, Y. (2018). Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS. International Review of Financial Analysis. In Press. doi: https://doi.org/10.1016/j.irfa.2018.08.002
Joshi, P. (2012). Financial crisis and volatility behaviour of stock markets of Asia. Quest-Journal of Management and Research, 2(2), 35-44. Available at SSRN: https://ssrn.com/abstract=2325355
Kenourgios, D., Samitas, A., & Paltalidis, N. (2011). Financial crises and stock market contagion in a multivariate time-varying asymmetric framework. Journal of International Financial Markets, Institutions and Money, 21(1), 92-106. doi: https://doi.org/10.1016/j.intfin.2010.08.005
King, M. A., & Wadhwani, S. (1990). Transmission of volatility between stock markets. The Review of Financial Studies, 3(1), 5-33. doi: https://doi.org/10.1093/rfs/3.1.5
Kocaarslan, B., Sari, R., Gormus, A., & Soytas, U. (2017). Dynamic correlations between BRIC and US stock markets: The asymmetric impact of volatility expectations in oil, gold and financial markets. Journal of Commodity Markets, 7, 41-56. doi: https://doi.org/10.1016/j.jcomm.2017.08.001
Li, X., & Wei, Y. (2018). The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method. Energy Economics, 74, 565-581. doi: https://doi.org/10.1016/j.eneco.2018.07.011
Lin, W. L., Engle, R. F., & Ito, T. (1994). Do bulls and bears move across borders? International transmission of stock returns and volatility. Review of Financial Studies, 7(3), 507-538. doi: https://doi.org/10.1093/rfs/7.3.507
Liu, G., Long, W., Zhang, X., & Li, Q. (2019). Detecting financial data dependence structure by averaging mixture copulas. Econometric Theory, 35(4), 1-39. doi: https://doi.org/10.1017/S0266466618000270
Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. The journal of finance, 56(2), 649-676. doi: https://doi.org/10.1111/0022-1082.00340
Malliaris, A. G., & Urrutia, J. L. (1992). The international crash of October 1987: causality tests. Journal of Financial and Quantitative Analysis, 27(3), 353-364.
Mensi, W., Hammoudeh, S., Shahzad, S. J. H., & Shahbaz, M. (2017). Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method. Journal of Banking & Finance, 75, 258-279. doi: https://doi.org/10.1016/j.jbankfin.2016.11.017
Nelsen, R. B. (2005). Copulas and quasi-copulas: an introduction to their properties and applications. In Logical, algebraic, analytic and probabilistic aspects of triangular norms (pp. 391-413). Elsevier Science BV.
Nikoloulopoulos, A. K., Joe, H., & Li, H. (2012). Vine copulas with asymmetric tail dependence and applications to financial return data. Computational Statistics & Data Analysis, 56(11), 3659-3673. doi: https://doi.org/10.1016/j.csda.2010.07.016
Pappadà, R., Durante, F., & Torelli, N. (2018). A graphical tool for copula selection based on tail dependence. In Classification,(Big) Data Analysis and Statistical Learning (pp. 211-218). Springer, Cham. https://doi.org/10.1007/978-3-319-55708-3_23
Patton, A. J. (2001). Modelling time-varying exchange rate dependence using the conditional copula, UC San Diego: Department of Economics, UCSD. Retrieved from https://escholarship.org/uc/item/01q7j1s2
Patton, A. J. (2004). On the out-of-sample importance of skewness and asymmetric dependence for asset allocation. Journal of Financial Econometrics, 2(1), 130-168. doi: https://doi.org/10.1093/jjfinec/nbh006
Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556. doi: https://doi.org/10.1111/j.1468-2354.2006.00387.x
Rodriguez, J. C. (2007). Measuring financial contagion: A copula approach. Journal of empirical finance, 14(3), 401-423.
Shahzad, S. J. H., Mensi, W., Hammoudeh, S., Rehman, M. U., & Al-Yahyaee, K. H. (2018). Extreme dependence and risk spillovers between oil and Islamic stock markets. Emerging Markets Review, 34, 42-63. doi: https://doi.org/10.1016/j.ememar.2017.10.003
Susmel, R., & Engle, R. F. (1994). Hourly volatility spillovers between international equity markets. Journal of international Money and Finance, 13(1), 3-25. doi: https://doi.org/10.1016/0261-5606(94)90021-3
Taylor, S. (1986). Modelling Financial Time Series. Wiley.
Wen, F., & Liu, Z. (2009). A copula-based correlation measure and its application in Chinese stock market. International Journal of Information Technology & Decision Making, 8(04), 787-801.
Yao, C. Z., & Sun, B. Y. (2018). The study on the tail dependence structure between the economic policy uncertainty and several financial markets. The North American Journal of Economics and Finance, 45, 245-265. doi: https://doi.org/10.1016/j.najef.2018.03.005
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955.
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