The Factors that Influence Exchange-Rate Risk: Evidence in China

ABSTRACT

Exchange-rate volatility plays an important role in both macroeconomic and financial development. In this paper, we measure the exchange-rate risk based on the conditional autoregressive value at risk (CAViaR). By establishing a Markov regime-switching model, we explore the factors that influence China’s exchange-rate risk in different regimes. The results show that trade balance, investor attention, and the interaction between policy uncertainty and investor attention have an asymmetric effect on exchange-rate risk in different regimes. More specifically, the impact of trade balance on exchange-rate risk has a linear trend, investor attention to exchange-rate risk is U-shaped, and the interaction between policy uncertainty and investor attention has an inverted-U-shaped effect on exchange-rate risk. Therefore, it is necessary to improve the monitoring mechanism of the exchange-rate risk regime. Specifically, when the exchange-rate risk is low, we can continue to release positive news and attract more investor attention. Under medium risk, we can promote exports by formulating supporting policies and use online tools to release accurate news to guide investors. Under a high-risk regime, while implementing the policies which encourage exports, the government should also introduce policy interventions for guiding investors to return rational expectations.

KEY WORDS:

• exchange-rate risk
• investor attention
• Markov regime model
• policy uncertainty
• trade balance

Notes

1. Generally speaking, both trade and investment are important methods of international capital flow. Accordingly, trade balance and FDI net flow (FDI minus OFDI) are widely used to reflect the conditions of China’s monthly international capital flow. Since FDI mainly refers to long- and medium-term investment, the impact of the FDI net flow on exchange-rate risk is expected to be relatively small theoretically. Additionally, we collect the monthly series of China’s trade balance and FDI net flow from China’s National Bureau of Statistics and Ministry of Commerce, respectively. It is founded that China’s trade balance and FDI net flow during the sample period are always positive, but the former is much greater than the latter. Thus, we use trade balance to describe China’s monthly capital flow in this paper.

2. The complete derivation and proof of the two tests are given in Engle and Manganelli (2004: 370–371, 377–380).

3. We test different values of G, such as 5, 15, and 20, and obtain the same result as for G = 10.

Additional information

Funding

This work was supported by the “the Fundamental Research Funds for the Central Universities” in UIBE [CXTD9-07].