Abstract
Foreign exchange rates is a significant factor affecting foreign transactions such as trade and investment. Foreign exchange rates, especially EUR/USD and GBP/USD, have a high fluctuation in recent years and lead a severe risk to investors. In this study, we consider a hedging strategy as a tool for offsetting the potential losses of investors. We introduce two classes of Markov Switching correlation model, namely MS-CCC-GARCH and MS-DCC-GARCH to compute the optimal hedge ratios and portfolio weights in the foreign exchange rates (EUR/USD and GBP/USD) for the period of 2013–2018. We also compare the performance of these two models with CCC-GARCH, DCC-GARCH models. The results show that MS-DCC-GARCH perform better for EUR/USD and GBP/USD spot and futures pairs. We finally complement our analysis by computing the dynamic hedge ratio and optimal portfolio weight, the result shows that the hedge ratios for both currencies are mostly remaining closely to 1 over the sample periods. However, we notice that, in some periods, the hedge ratios are particularly low in the low volatility market regime.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmad, W., Sadorsky, P., Sharma, A.: Optimal hedge ratios for clean energy equities. Econ. Modell. 72, 278–295 (2018)
Bauwens, L., Storti, G.: A component GARCH model with time varying weights. Stud. Nonlinear Dyn. Econ. 13(2), 1–31 (2009)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)
Bollerslev, T.: Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Rev. Econ. Stat. 72, 498–505 (1990)
Chang, C.L., González-Serrano, L., Jimenez-Martin, J.A.: Currency hedging strategies using dynamic multivariate GARCH. Math. Comput. Simul. 94, 164–182 (2013)
Chodchuangnirun, B., Yamaka, W., Khiewngamdee, C.: A regime switching for dynamic conditional correlation and GARCH: application to agricultural commodity prices and market risks. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 289–301. Springer, Cham, March 2018
Da Silva Filho, O.C., Ziegelmann, F.A., Dueker, M.J.: Modeling dependence dynamics through copulas with regime switching. Insur. Math. Econ. 50(3), 346–356 (2012)
Engle, R.F., Sheppard, K.: Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH (No. w8554). National Bureau of Economic Research (2001)
Hamilton, J.D.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357–384 (1989)
Held, L., Ott, M.: On p-values and Bayes factors. Annu. Rev. Stat. Appl. 5, 393–419 (2018)
Hsu, C.C., Tseng, C.P., Wang, Y.H.: Dynamic hedging with futures: a copula-based GARCH model. J. Futur. Mark. 28(11), 1095–1116 (2008)
Johnson, L.L.: The theory of hedging and speculation in commodity futures. Rev. Econ. Stud. 27(3), 139–151 (1960)
Pastpipatkul, P., Yamaka, W., Sriboonchitta, S.: Analyzing financial risk and co-movement of gold market, and Indonesian, Philippine, and Thailand stock markets: dynamic copula with Markov-switching. In: Causal Inference in Econometrics, pp. 565–586. Springer (2016)
Vovk, V.G.: A logic of probability, with application to the foundations of statistics. J. R. Stat. Soc. Ser. B (Methodol.) 55, 317–351 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rakpho, P., Yamaka, W., Sriboonchitta, S. (2019). Markov Switching Dynamic Multivariate GARCH Models for Hedging on Foreign Exchange Market. In: Kreinovich, V., Thach, N., Trung, N., Van Thanh, D. (eds) Beyond Traditional Probabilistic Methods in Economics. ECONVN 2019. Studies in Computational Intelligence, vol 809. Springer, Cham. https://doi.org/10.1007/978-3-030-04200-4_57
Download citation
DOI: https://doi.org/10.1007/978-3-030-04200-4_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04199-1
Online ISBN: 978-3-030-04200-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)