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Abstract
In this paper, the Autoregressive Jump Intensity (ARJI) model with time-varying jumps is used to measure the daily exchange rate volatility and jump intensity of 13 Chinese manufacturing segments from January 1, 2001 to June 30, 2017. The statistical characteristics are analyzed and compared. We further explore the impact of international major payment currencies’ volatility on the industry-specific nominal effective exchange rate (INEER) risks for various industries in China. First, the results show that there are certain differences in exchange rate fluctuation and jump dynamics between different industries. The exchange rate volatility and jump intensity for paper, non-metal and metal industries are small, while for petroleum, rubber, electrical machinery and other industries are larger. Second, the U.S. dollar, German mark and Japanese yen have significantly different effects on exchange rate fluctuations and jump risks in various industries, and the degree of impact is weakened in turn. Finally, the analysis of the sub-sample shows that after the financial crisis, the impact of dollar and yen on the fluctuations of INEER for most industries has declined significantly, and the impact of mark has generally increased.
Highlights
1976 when Jamaica Accord formally recognized the legitimacy of floating exchange rate regimes, exchange rate volatility has gradually entered the field of public vision as an important source of macroeconomic uncertainty and has become the subject of research conducted by many scholars (Jorion, 1988; Bayoumi & Eichengreen, 1998; Devereux & Engel, 2002; Giannellis & Papadopoulos, 2011)
Zhu, & Li (2016) applied the Autoregressive Jump Intensity (ARJI) model to weekly bilateral exchange rate returns of 31 countries over the period 2001-2013 and examine the determinants of bilateral exchange rate risks. Their empirical results show that bilateral exchange rate risks can be significantly reduced by external financial liabilities and the development of domestic financial sectors will attenuate this effect. It can be seen from the above literature that the generalized autoregressive conditional heteroskedasticity (GARCH) model and the jump model have their own advantages in capturing the special fluctuations and non-normal distributions of returns, and they gradually merge into the GARCH-JUMP model, which include the Constant-GARCH, Models, ARJI models and other variants
In order to accurately estimate the impact of major international currencies on the competitiveness of different industries, Zou, You, & Fu (2016) construct industry-specific nominal effective exchange rate of eight manufacturing subdivision industries based on bilateral exchange rate of RMB and bilateral trade flows between China and its major trading partners
Summary
Since the collapse of the Bretton Woods agreement in the 1970s, especially since. 1976 when Jamaica Accord formally recognized the legitimacy of floating exchange rate regimes, exchange rate volatility has gradually entered the field of public vision as an important source of macroeconomic uncertainty and has become the subject of research conducted by many scholars (Jorion, 1988; Bayoumi & Eichengreen, 1998; Devereux & Engel, 2002; Giannellis & Papadopoulos, 2011). Many scholars have used the three basic models to explore the characteristics and sources of exchange rate fluctuations, there are still few articles on the determinants of foreign exchange rate jump risk. To the best of my knowledge, there is no literature trying to capture the large and non-diversifiable jump risk in industrial exchange rate fluctuations and explore its determinants.
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