Article ID Journal Published Year Pages File Type
7355520 International Review of Economics & Finance 2017 40 Pages PDF
Abstract
This paper examines the impact of public information flows on the volatility of the bilateral Chinese Renminbi-US dollar (RMB-USD) exchange rates in the spot, non-deliverable forward (NDF) and futures markets. By using the comprehensive RavenPack Dow Jones News Analytics database that captures Chinese and US macroeconomic news releases and their sentiment scores at high frequencies, we investigate the circumstances in which public news sentiment is related to the volatility of the above exchange rates. To account for the possibility of different volatility regimes in the RMB-USD volatility, a two-state Regime-Switching EGARCH-in-mean (RS-EGARCH-M) model that incorporates the effects of news sentiment is proposed. Our model suggests that news sentiment has a greater impact on reducing volatility persistence in the low-volatility regime (calm state) for all the NDF and futures exchange rates; in contrast, the impact is greater in the high-volatility regime (turbulent state) for the spot rate. Furthermore, depending on the news sources and sentiment, the marginal effects of news on these exchange rates can vary significantly. In particular, compared with the USD news releases, the RMB news releases have a stronger influence on the RMB-USD volatility. Moreover, the impact of negative news sentiment is greater than that of positive news sentiment. However, the effects of RMB-USD volatility on contemporaneous returns are mostly insignificant. Our RS-EGARCH-M model indicates that the estimated smoothing probability of the RMB-USD spot rate can produce consistent identification of the different economic states arising from changes in the macroeconomic and exchange rate policies of the Chinese government. These findings have important implications for China's exchange rate regime and the process of RMB internationalization.
Related Topics
Social Sciences and Humanities Economics, Econometrics and Finance Economics and Econometrics
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