|
|
Finance 2023
基于LASSO-VAR模型的外汇市场风险溢出网络研究
|
Abstract:
随着外汇市场越来越受到投资者的青睐，其风险分析研究也变得十分重要且非常必要。因此，本文探讨了2005年9月至2023年2月期间的全球外汇传递冲击溢出效应和网络连通性。我们使用波动率溢出指数和最小绝对收缩和选择算子向量自回归(LASSO-VAR)的方法来构建35种全球货币的网络连通性。总体而言，该研究发现全球外汇市场之间存在显著的溢出关联性(达到78.54%)，其中SGD (新加坡元)和CHF (瑞士法郎)是冲击的主要净传递者，连通性分别为96.99%和75.29%。相比之下，ARS (阿根廷比索)和INR (印度卢比)是冲击的主要净接收者，连通性分别为94.96%和79.15%。从动态上看，总溢出连通性对国际经济变化和危机的反应不同。这项研究的结果可以对全球外汇市场的风险分析做出额外贡献，这也将有助于对冲在危机期间的相关风险。
Widespread interest in foreign exchange markets among investors makes risk analysis study important and well-needed. Therefore, this paper explores the global foreign exchange return shock spillovers and network connectedness between September 2005 and February 2023. We use the volatility spillover index and the Least Absolute Shrinkage and Selection Operator-Vector Auto-regression (LASSO-VAR) approach to construct network connectedness of 35 global currencies. Overall, the study found a significant spillover connectedness among the global foreign exchange markets (78.54%), with SGD (Singapore Dollar) and CHF (Swiss Franc) as the major net transmitters, accounting for 96.99% and 75.29%, respectively. In contrast, ARS (Argentine Peso) and INR (Indian Rupee) are the major net receivers of shocks, with 94.96% and 79.15%, respectively. Dynamically, total spillover connectedness reacts differently to international economic changes and crises. The findings of this study can be an additional contribution to the risk analysis in the global foreign exchange markets, which will also be useful to hedge the risk during crisis periods.
| [1] | Omrane, W.B., Tao, Y. and Welch, R. (2017) Scheduled Macro-News Effects on a Euro/Us Dollar Limit Order Book around the 2008 Financial Crisis. Research in International Business and Finance, 42, 9-30.
https://doi.org/10.1016/j.ribaf.2017.05.003 |
| [2] | Kenourgios, D., Papadamou, S. and Dimitriou, D. (2015) Intraday Exchange Rate Volatility Transmissions across QE Announcements. Finance Research Letters, 14, 128-134. https://doi.org/10.1016/j.frl.2015.05.007 |
| [3] | Coudert, V., Couharde, C. and Mignon, V. (2015) On the Impact of Volatility on the Real Exchange Rate—Terms of Trade Nexus: Revisiting Commodity Currencies. Journal of Interna-tional Money and Finance, 58, 110-127.
https://doi.org/10.1016/j.jimonfin.2015.08.007 |
| [4] | Goddard, J., Kita, A. and Wang, Q. (2015) Investor Attention and FX Market Volatility. Journal of International Financial Markets, Institutions and Money, 38, 79-96. https://doi.org/10.1016/j.intfin.2015.05.001 |
| [5] | Hung, N.T. (2021) Volatility Behaviour of the Foreign Exchange Rate and Transmission among Central and Eastern European Countries: Evidence from the EGARCH Model. Global Business Review, 22, 36-56.
https://doi.org/10.1177/0972150918811713 |
| [6] | Van der Westhuizen, C., van Eyden, R. and Aye, G.C. (2022) Contagion across Financial Markets during Covid-19: A Look at Volatility Spillovers between the Stock and Foreign Exchange Markets in South Africa. Annals of Financial Economics, 17, 1-46. https://doi.org/10.1142/S2010495222500026 |
| [7] | Kenourgios, D., Papadamou, S. and Dimitriou, D. (2015) On Quantitative Easing and High Frequency Exchange Rate Dynamics. Research in International Business and Finance, 34, 110-125. https://doi.org/10.1016/j.ribaf.2015.01.003 |
| [8] | Kilic, E. (2017) Contagion Effects of U.S. Dollar and Chinese Yuan in Forward and Spot Foreign Exchange Markets. Economic Modelling, 62, 51-67. https://doi.org/10.1016/j.econmod.2017.01.005 |
| [9] | Salisu, A.A. and Ayinde, T.O. (2018) Testing for Spillovers in Naira Exchange Rates: The Role of Electioneering & Global Financial Crisis. Borsa Istanbul Review, 18, 341-348. https://doi.org/10.1016/j.bir.2018.07.007 |
| [10] | Boako, G. and Alagidede, P. (2017) Currency Price Risk and Stock Market Returns in Africa: Dependence and Downside Spillover Effects with Stochastic Copulas. Journal of Multination-al Financial Management, 41, 92-114.
https://doi.org/10.1016/j.mulfin.2017.06.001 |
| [11] | Kumar, S., Tiwari, A.K., Chauhan, Y. and Ji, Q. (2019) De-pendence Structure between the Brics Foreign Exchange and Stock Markets Using the Dependence-Switching Copula Approach. International Review of Financial Analysis, 63, 273-284. https://doi.org/10.1016/j.irfa.2018.12.011 |
| [12] | Wang, G.J., Wan, L., Feng, Y., et al. (2023) Interconnected Multi-layer Networks: Quantifying Connectedness among Global Stock and Foreign Exchange Markets. International Review of Financial Analysis, 86, Article ID: 102518.
https://doi.org/10.1016/j.irfa.2023.102518 |
| [13] | Mensi, W., Ali, S.R.M, Vo, X.V., et al. (2022) Multiscale De-pendence, Spillovers, and Connectedness between Precious Metals and Currency Markets: A Hedge and Safe-Haven Analysis. Resources Policy, 77, Article ID: 102752.
https://doi.org/10.1016/j.resourpol.2022.102752 |
| [14] | Mensi, W., Shafiullah, M., Vo, X.V., et al. (2022) Asymmet-ric Spillovers and Connectedness between Crude Oil and Currency Markets Using High-Frequency Data. Resources Policy, 77, Article ID: 102678.
https://doi.org/10.1016/j.resourpol.2022.102678 |
| [15] | Nekhili, R., Mensi, W. and Vo, X.V. (2021) Multiscale Spillovers and Connectedness between Gold, Copper, Oil, Wheat and Currency Markets. Resources Policy, 74, Article ID: 102263.
https://doi.org/10.1016/j.resourpol.2021.102263 |
| [16] | Andrada-félix, J., Fernandez-Perez, A. and Sosvilla-Rivero, S. (2020) Distant or Close Cousins: Connectedness between Cryptocurrencies and Traditional Currencies Volatilities. Journal of International Financial Markets, Institutions and Money, 67, Article ID: 101219. https://doi.org/10.1016/j.intfin.2020.101219 |
| [17] | Raza, S.A., Ahmed, M. and Aloui C. (2022) On the Asymmet-rical Connectedness between Cryptocurrencies and Foreign Exchange Markets: Evidence from the Nonparametric Quan-tile on Quantile Approach. Research in International Business and Finance, 61, Article ID: 101627. https://doi.org/10.1016/j.ribaf.2022.101627 |
| [18] | Diebold, F.X. and Yilmaz, K. (2009) Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119, 158-171.
https://academic.oup.com/ej/article/119/534/158-171/5089555
https://doi.org/10.1111/j.1468-0297.2008.02208.x |
| [19] | Diebold, F.X. and Yilmaz, K. (2012) Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28, 57-66. https://doi.org/10.1016/j.ijforecast.2011.02.006 |
| [20] | Diebold, F.X. and Yilmaz, K. (2014) On the Network Topol-ogy of Variance Decompositions: Measuring the Connectedness of Financial Firms. Journal of Econometrics, 182, 119-134. https://doi.org/10.1016/j.jeconom.2014.04.012 |
| [21] | Demirer, M., Diebold, F.X., Liu, L., et al. (2018) Es-timating Global Bank Network Connectedness. Journal of Applied Econometrics, 33, 1-15. https://doi.org/10.1002/jae.2585 Https://onlinelibrary.wiley.com/doi/full/10.1002/jae.2585 |
| [22] | Huynh, T.L.D., Nasir M.A. and Nguyen, D.K. (2020) Spillovers and Connectedness in Foreign Exchange Markets: The Role of Trade Policy Uncertainty. The Quarterly Re-view of Economics and Finance, 87, 191-199.
https://doi.org/10.1016/j.qref.2020.09.001 |
| [23] | Fasanya, I.O., Oyewole, O. and Adekoya, O.B., et al. (2020) Dy-namic Spillovers and Connectedness between Covid-19 Pandemic and Global Foreign Exchange Markets. Economic Re-search-Ekonomska Istra？ivanja, 34, 2059-2084.
https://doi.org/10.1080/1331677X.2020.1860796 |
| [24] | Wang, G.J., Xie, C., Jiang, Z.Q., et al. (2016) Who Are the Net Senders and Recipients of Volatility Spillovers in China’s Financial Markets?. Finance Research Letters, 18, 255-262. https://doi.org/10.1016/j.frl.2016.04.025 |
| [25] | Kang, S.H. and Lee, J.W. (2019) The Network Connected-ness of Volatility Spillovers across Global Futures Markets. Physica A: Statistical Mechanics and Its Applications, 526, Article ID: 120756.
https://doi.org/10.1016/j.physa.2019.03.121 |
| [26] | Diebold, F.X. and Yilmaz, K. (2014) On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms. Journal of Econometrics, 182, 119-134. |
| [27] | Yi, S., Xu, Z. and Wang, G.J. (2018) Volatility Connectedness in the Cryptocurrency Market: Is Bitcoin a Dominant Cryptocurrency? International Review of Financial Analysis, 60, 98-114.
https://linkinghub.elsevier.com/retrieve/pii/s1057521918304095
https://doi.org/10.1016/j.irfa.2018.08.012 |
| [28] | Diebold, F.X., Liu, L. and Yilmaz, K. (2017) Commodity Connect-edness. SSRN Electronic Journal, 1-27.
Https://www.nber.org/papers/w2368
https://doi.org/10.3386/w23685 |
