The paper analyzes the stylized facts of RMB exchange rate in the new normal economy based on the real background that the relationship between China and the world is getting closer and the volatility of exchange rate is increasing. The volatility of RMB exchange rate is tested empirically based on models of TGARCH, Leverage SV, Granger causality test and BVAR. And the further empirical tests are carried out by using the Markov model. And then the following trends of RMB exchange rate is analyzed. Conclusions are drawn as follows: first, the volatility range is becoming larger with the internationalization and marketization of RMB exchange rate, and the volatility of RMB exchange rate will transform from one-way appreciation to two-way appreciation; second, the leverage SV model is superior to the TGARCH model. T distribution is superior to N distribution. The leverage SV-T model, suited both with the U.S. dollar against RMB and the real effective RMB exchange rate index, is the best for measuring the RMB exchange rate volatility; third, the RMB exchange rate volatility has a strong persistence, and the volatility will increase with the RMB appreciation; fourth, to build a financial power requires the internationalization and marketization of exchange rate, and financial innovation is needed to make up the new driving factor in the new normal economy, so as to strengthen China's rule making and discourse authority in the international financial markets.
Key words
Internationalization of RMB /
Marketization of Exchange /
Leverage Stochastic Volatility /
Interaction /
Markov
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References
[1] 伍戈,裴诚. 境内外人民币汇率价格关系的定量研究[J]. 金融研究,2012（9）：62-73
[2] 张欣,崔日明. 基于非对称随机波动模型的人民币汇率波动特征研究[J]. 国际金融研究,2013（1）：28-37
[3] 赵华,燕焦枝. 汇改后人民币汇率波动的状态转换行为研究[J]. 国际金融研究,2010（1）：60-67
[4] Asai,M.,McAleer,M. and Yu,J. Multivariate Stochastic Volatility：A Review[J]. Econometric Reviews,2006（2-3）：145-175
[5] Cukierman,A. and Meltzer,A.H. A Theory of Ambiguity,Credibility,and Inflation under Discretion and Asymmetric Information[J]. Econometricsa,1986（5）：1099-1128
[6] Engel,C. and Hamilton,J.D. Long Swings in the Dollar：Are They in the Data and Do Markets Know It?[J]. The American Economic Review,1990（4）：689-713
[7] Friedman,M. Nobel Lecture：Inflation and Unemployment[J]. Journal of Political Economy,1977（3）：451-472
[8] Glosten,L.R.,Jagannathan,R. and Runkle,D.E. On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks[J]. The Journal of Finance,1993（5）：1779-1801
[9] Harvey,A.C. and Shephard,N. Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns[J]. Journal of Business & Economic Statistics,1996（4）：429-434
[10] Hamilton,J.D. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle[J]. Econometrica,1989（2）：357-384
[11] Klau,M.,and Fung,S.S. The New BIS Effective Exchange Rate Indices[R]. BIS Quarterly Review,2006 March
[12] Koop,G. and Korobilis,D. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics[R]. MPRA Paper,No.20125,2010
[13] Nakajima,J. and Omori,Y. Stochastic Volatility Model with Leverage and Asymmetrically Heavy-tailed Error Using GH Skew Student’s T-Distribution[J]. Computational Statistics & Data Analysis,2012（11）：3690-3704
[14] Ross,S.A.Information and Volatility：The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy[J]. The Journal of Finance,1989（1）：1-17
[15] Yu,J. On Leverage in a Stochastic Volatility Model[J]. Journal of Econometrics,2005（2）：165-178
[16] Zakoian,J.M. Threshold Heteroskedastic Models[J]. Journal of Economic Dynamics and control,1994（5）：931-955
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