Summary:
The RMB was included in the Special Drawing Rights (SDR) currency basket on October 1, 2016, following the Dollar, Pound, Yen, and Euro. However, the U.S. has increased protectionist trade policies and tariff barriers since 2018. On March 22, the U.S. announced a large-scale tariff on Chinese imports. The exchange market's CNY faces a turbulent environment, and China's economy is experiencing only moderate growth. As a result, the RMB is showing a weak trend and increased pressure. Fluctuations in the CNY have become the market's primary concern. Therefore, the volatility spillover between the CNY and other international markets, volatility clustering, and the degree of RMB must be explored to prevent and avoid volatile risk with the CNY. The volatility spillover effect stipulates that an economy's exchange market is more likely to spill over into other economies' exchange markets when its currency has a higher degree. In addition, other economies show a volatility clustering trend. The comparison between the RMB market and other major currency markets shows the degree of RMB in global economic patterns. Therefore, we use the SDR currency basket's exchange market to identify the relationship between volatility spillover and volatility clustering in real time and identify the international degree of RMB. A nonlinear model with structural changes is best suited to describe the characteristics of the exchange market's volatility, given the various time and political characteristics of major global currencies' co-movement. The Markov switching model can depict the structural change in time series data and internalize the structural change into regime change. The model can describe the regime state in different stages and the nonlinear transition of a regime state in each stage. It is suitable for measuring the stage-switching of volatility spillover between exchange rate markets and the dynamic volatility clustering paths. Therefore, we construct a nonlinear model of Markov switching bivariate ARCH (MSBIARCH). We enumerate all possible time-varying dynamic causal relationships between the two variables based on the model and exchange rate yield data of the CNY, USD, GBP, JPY, and EUR exchange markets. Four regional states are also defined according to the four time-varying dynamic causalities to screen the exchange markets' volatility spillover and volatility clustering in real-time. The results show that exchange markets transmit fluctuations via “economic fundamentals”, “market sentiment”, and “market expectations”. In general, the volatility spillover between CNY and USD exchange markets moves in both directions, while the volatility spillover between CNY and GBP, JPY, and EUR exchange markets moves in one direction. However, over time, the exchange markets' regimes change, thus changing the characteristics of the volatility spillover and clustering situation. Furthermore, the volatility spillover among exchange markets usually occurs in periods with extreme economic events, irregular events, and policy promulgation events, matching when volatility clustering begins to occur. Finally, the volatility spillover of international exchange markets leads to the volatility clustering of CNY exchange market, while the volatility spillover of the CNY exchange market strengthens the volatility clustering of the international exchange markets. Thus, the international degree of RMB has room for improvement, which means that fluctuations in the international exchange market are more likely to affect the CNY market. China should improve the exchange market's efficiency while being vigilant against contagion from the international exchange markets, especially during extreme economic events, irregular events, and policy promulgation events. The paper contributes to the literature in four ways. First, we identify the international degree of RMB in the SDR currency basket based on the volatility spillover relationships and volatility clustering trends of different exchange markets. Second, we examine the volatility spillover relationship between different exchange markets over time using data on extreme economic events, irregular events, and policy promulgation events. The volatility clustering trends over time and political periods are also considered. Third, we theoretically analyze the characteristics of volatility clustering under the volatility spillover effect from the perspectives of economic fundamentals and market psychology and discuss the correlation between volatility spillover and volatility clustering. Fourth, for the first time a nonlinear MSBIARCH model based on the latest MSC model is constructed and designed, which provides a reference for model selection in a time-varying estimation framework and new applications for high-frequency time series data.
隋建利, 刘碧莹. SDR货币篮子中人民币的国际化定位——汇率市场波动传染与波动聚类的实时甄别[J]. 金融研究, 2020, 485(11): 1-20.
SUI Jianli, LIU Biying. International Degree of RMB in the SDR Currency Basket:Real-time Examination of Volatility Spillover and Volatility Clustering. Journal of Financial Research, 2020, 485(11): 1-20.
Adler, M., and B. Dumas. 1983. “International Portfolio Choice and Corporation Finance: A Synthesis”, <i>Journal of Finance</i>, 38(3):925～984.<br />
[18]
Arouri, M., J. Jouinin, and D. K. Nguyen. 2011. “Volatility Spillovers Between Oil Prices and Stock Sector Returns: Implications for Portfolio Management”, <i>Journal of International Money and Finance</i>, 30(7):1387～1405.<br />
[19]
Balcilar, M., R. Gupta, C. Kyei, and M. E. Wohar. 2016. “Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility？ Evidence from a Nonparametric Causality - in - Quantiles Test”, <i>Open Economies Review</i>, 27(2):229～250.<br />
[20]
Barro, R. J. 2006. “Rare Disasters and Asset Markets in the Twentieth Century”, <i>Quarterly Journal of Economics</i>, 121(3):823～866.<br />
[21]
Brana, S., A. Campmas, and L. Lapteacru. 2019. “(Un) Conventional Monetary Policy and Bank Risk - taking: A Nonlinear Relationship”, <i>Economic Modelling</i>, 81(9):576～593.<br />
[22]
Brümmer, B., O. Korn, K. Schlü?ler, and T. J. Jaghdani. 2016. “Volatility in Oil Seeds and Vegetable Oils Markets: Drivers and Spillovers”, <i>Journal of Agricultural Economics</i>, 67(3):685～705.<br />
[23]
Chamon, M., M. Garcia, and L. Souza. 2017. “FX Interventions in Brazil: A Synthetic Control Approach”, <i>Journal of International Economics</i>, 108(9):157～168.<br />
[24]
Chang, C., Z. Liu, and M. M. Spiegel. 2015. “Capital Controls and Optimal Chinese Monetary Policy”, <i>Journal of Monetary Economics</i>, 74(9):1～15.<br />
[25]
De, V. G., E. Trachanas, and Y. Luo. 2018. “Revisiting the Bi - directional Causality Between Debt and Growth: Evidence from Linear and Nonlinear Tests”, <i>Journal of International Money & Finance</i>, 83(5):55～74.<br />
[26]
Diebold, F. X., and K. Yilmaz. 2009. “Measuring Financial Asset Return and Volatility Spillovers with Application to Global Equity Markets”, <i>Economic Journal</i>, 119(1):158～171.<br />
[27]
Diebold, F. X., and K. Yilmaz. 2014. “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms”, <i>Journal of Econometrics</i>, 182(1):119～134.<br />
[28]
El - Shagi, M., A. Lindner, and G. V. Schweinitz. 2016. “Real Effective Exchange Rate Misalignment in the Euro Area: A Counterfactual Analysis”, <i>Review of International Economics</i>, 24(1):37～66.<br />
[29]
Forbes, K. J., and R. Rigobon. 2002. “No Contagion, Only Interdependence: Measuring Stock Market Comovements”, <i>Journal of Finance</i>, 57(5):2223～2261.<br />
[30]
Georgiadis, G. 2017. “To Bi, Or Not to Bi？ Differences Between Spillover Estimates from Bilateral and Multilateral Multi - country Models”, <i>Journal of International Economics</i>, 107(7):1～18.<br />
[31]
Hamilton, J. D. 1994. “Time Series Analysis”, Princeton University Press.<br />
[32]
Huo, R., and A. D. Ahmed. 2016. “Volatility Spillover Effects: Evaluating the Impact of Shanghai - Hong Kong Stock Connect”, <i>Economic Modelling</i>, 61(2):260～272.<br />
[33]
Jondeau, E., E. Jurczenko, and M. Rockinger. 2018. “Moment Component Analysis: An Illustration with International Stock Markets”, <i>Journal of Business & Economic Statistics</i>, 36(2):576～598.<br />
[34]
Kaminsky, G. L., and C. M. Reinhart. 1998. “Financial Crisis in Asia and Latin America: Then and Now”, <i>American Economic Review</i>, 88(2):444～448.<br />
[35]
Kaminsky, G. L., and C. M. Reinhart. 2000. “On Crises, Contagion, and Confusion”, <i>Journal of International Economics</i>, 51(1):145～168.<br />
[36]
Kandil, M. 2015. “The Adverse Effects of Real Exchange Rate Variability in Latin America and the Caribbean”, <i>Journal of Applied Economics</i>, 18(1):99～120.<br />
[37]
Kim, C. J., and C. R. Nelson. 1999. “State - space Models with Regime Switching: Classical and Gibbs - sampling Approaches with Applications”, Cambridge, MA: The MIT Press.<br />
[38]
King, M., and S. Wadhwani. 1990. “Transmission of Volatility Between Stock Markets”, <i>Review of Financial Studies</i>, 3(1):5～33.<br />
[39]
King, M., E. Sentana, and S. Wadhwani. 1994. “Volatility and Links Between National Stock Markets”,<i> Econometrica</i>, 62(4):901～933.<br />
[40]
Kodres, L. E., and M. Pritsker. 2002. “A Rational Expectations Model of Financial Contagion”, <i>Journal of Finance</i>, 57(4):769～799.<br />
[41]
Lagoa, S. 2017. “Determinants of Inflation Differentials in the Euro Area: Is the New Keynesian Phillips Curve Enough？” <i>Journal of Applied Economics</i>, 20(1):75～103.<br />
[42]
Lean, H. H., and K. T. Teng. 2013. “Integration of World Leaders and Emerging Powers into the Malaysian Stock Market: A DCC - MGARCH Approach”, <i>Economic Modelling</i>, 32(5):333～342.<br />
[43]
Ning, Y., Y. M. Wang, and C. W. Su. 2017. “How Did China's Foreign Exchange Reform Affect the Efficiency of Foreign Exchange Market？” <i>Physica A: Statistical Mechanics and its Applications</i>, 483(10):219～226.<br />
[44]
Omrane, W. B., and C. Hafner. 2015. “Macroeconomic News Surprises and Volatility Spillover in Foreign Exchange Markets”, <i>Empirical Economics</i>, 48(2):1～31.<br />
[45]
Psaradakis, Z., M. O. Ravn, and M. Sola. 2005. “Markov Switching Causality and the Money - output Relationship”, <i>Journal of Applied Econometrics</i>, 20(5):665～683.<br />
[46]
Solnik, B. H. 1974. “An Equilibrium Model of the International Capital Market”, <i>Journal of Economic Theory</i>, 8(4):500～524.<br />
[47]
Stulz, R. 1981. “A Model of International Asset Pricing”, <i>Journal of Financial Economics</i>, 9(4):383～406.<br />
[48]
Swanson, N. R. 1998. “Money and Output Viewed Through a Rolling Window”, <i>Journal of Monetary Economics</i>, 41(3):455～474.<br />
[49]
Taylor, M. 2013. “The Future of International Liquidity and the Role of China”, <i>Journal of Applied Corporate Finance</i>, 25(2):86～94.<br />
[50]
Yuan - Hung, H. K. 2008. “Student - t Distribution Based VAR - MGARCH: An Application of the DCC Model on International Portfolio Risk Management”, <i>Applied Economics</i>, 40(13):1685～1697.
