This study explores how foreign competition impacts the capital structure of domestic firms. While import competition is associated with a decrease in domestic firms’ leverage, we propose a novel perspective concerning the positive effect of inward foreign direct investment (FDI) on leverage. FDI competition can boost demand for debt via productivity spillover to domestic firms, and also increase supply of debt by inducing lenders to herd toward foreign investors. Using Chinese firm-level data, we find that the positive effects of industry inward FDI on domestic firms’ leverage are more pronounced in high-tech industries and industries where foreign investors exhibit a high degree of herding behavior. Our instrument variable approach, employing industry exchange rates and import tariffs, supports these findings. Additionally, we reveal that the positive effect of FDI on local firms’ leverage is amplified when the firms have stronger absorptive capacities, receive foreign capital, and experience more human capital transfers from foreign rivals.

This paper examines whether China’s switch back and forth from fixed to floating exchange rates in 2005 and 2008 changed the contribution to stock price discovery by foreign and domestic investors. During that time, mainland investors could only trade the RMB-denominated A-shares in the domestic Shanghai and Shenzhen markets, while the dual-listed HKD-denominated H-shares were available only to overseas investors. Using intraday data on overlapping trading hours, we find that the switch from a fixed rate to managed floating in July 2005 increased the H-shares’ contribution to price discovery; while the exchange rate regime reversal in July 2008 allowed the domestic stocks to regain their dominance in information shares. These results imply that, in a market subject to restrictions on capital flows, a flexible exchange rate regime increases the propensity of investors to trade foreign-issued stocks to speculate on the RMB exchange rate, which raises overseas investors’ contribution to price discovery.

In this paper we use the new developed Markov Switching Unit Root test to examine the status of real and financial integration of China, Japan, the European Union, and the United States based on the empirical validity of real interest parity, uncovered interest parity, and relative purchasing power parity. We found strong evidence in favour of those parity conditions and hence concluded that real and financial integration between China and other four countries was well established.

Vertical fragmentation of product value chain across borders is the driving force of growing economic interdependency in East Asia. A common currency, not flexible exchange rates between national currencies, would reduce flexibility in relative prices within East Asia. Its impact would be far greater for exports that have stronger production network linkage. In order to test the hypothesis, the paper estimates the effect of a common currency on China's processing and ordinary exports separately. The distinction is necessary because the processing exports, unlike the ordinary exports, are produced along the regional production networks, with final stages of assembly and exporting being increasingly concentrated in China. The short-run dynamics indicate that the effect on China's processing exports is more than double the corresponding effect on China's ordinary exports. The long-run effect on the processing exports of intra-regional RER flexibility, which is otherwise the lack of a regional currency, is almost nine times as large as the long-run effect of a unilateral RMB appreciation. By contrast, the corresponding long-run effect is statistically insignificant for the case of ordinary exports that are produced primarily by using local inputs. The long-run coefficient of this intra-regional RER flexibility implies that the actual volume of processing exports is 20 percent below the potential. The magnitudes of these effects are consistent with the hypothesis that a common currency would further integrate East Asian production networks and promote regional economic integration.

This paper estimates switching autoregressive conditional heteroskedasticity (SWARCH) time series models for weekly returns of nine Asian forward exchange rates. We find two regimes with different volatility levels, whereby each regime displays considerable persistence. Our analysis provides evidence that the knock-on effects from China’s currency forwards markets upon other Asian countries have been modest, in that little evidence exists for co-dependence of volatility regimes.

This paper proposes a path-dependent approach for estimating maximum appreciations of the renminbi expected by the market based on first-passage-time distributions. Using market data of the renminbi spot exchange rates, non-deliverable forward rates and currency option prices from 21 July 2005 (the reform of the exchange rate regime) to 28 February 2008 for model parameters, the maximum appreciations of the renminbi estimated under the proposed approach show that the market expected another large movement of the exchange rate during the 14 months after the reform. Subsequently, the few occasions of appreciations beyond the expected maximums coincided with the trade-related issues and speculations of greater momentum of appreciation allowed by the authorities. The PBoC's measures were however largely incorporated into the derivatives' prices. The proposed approach can be used to gauge the range of appreciations of the renminbi anticipated in the market and to identify any exchange rate movements beyond market expectations.

It is often documented, based on autocorrelation, variance ratio and power spectrum, that exchange rates approximately follow a martingale process. Because autocorrelation, variance ratio and spectrum check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insigniÞcant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange rate changes, and if so, whether nonlinear time series models admissible in the gap can outperform the martingale model in out-of-sample forecasts. Applying the generalized spectral tests of Hong (1999) to Þve major currencies, we Þnd that the changes of exchange rates are often serially uncorrelated, but there exists strong nonlinearity in conditional mean, in addition to the well-known volatility clustering. To forecast the conditional mean, we consider the linear autoregressive, autoregressive polynomial, artiÞcial neural network and functional-coefficient models, as well as their combination. The functional coefficient model allows the autoregressive coefficients to depend on investment positions via an moving average technical trading rule. We evaluate out-of-sample forecasts of these models relative to the martingale model, using four criteria– the mean squared forecast error, the mean absolute forecast error, the mean forecast trading return, and the mean correct forecast direction. White’s (2000) reality check method is used to avoid data-snooping bias. It is found that suitable nonlinear models, particularly their combination, do have superior predictive ability over the martingale model for some currencies in terms of certain forecast evaluation criteria.

Numerous studies have shown that the simple random walk model outperforms all structural and time series models in forecasting the conditional mean of exchange rate changes. However, in many important applications, such as risk management, forecasts of the probability distribution of exchange rate changes are often needed. In this paper, we develop a nonparametric portmanteau evaluation procedure for out-of-sample density forecast and provide a comprehensive empirical study on the out-of-sample performance of a wide variety of time series models in forecasting the intraday probability density of two major exchange rates－Euro/Dollar and Yen/Dollar. We find that some nonlinear time series models provide better density forecast than the simple random walk model, although they underperform in forecasting the conditional mean. For Euro/Dollar, it is important to model heavy tails through a Student-t innovation and asymmetric time-varying conditional volatility through a regime-switching GARCH model for both in-sample and out-of-sample performance; modeling conditional mean and serial dependence in higher order moments (e.g.,conditional skewness), although important for in-sample performance, does not help out-of-sample density forecast. For Yen/Dollar, it is also important to model heavy tails and volatility clustering, and the best density forecast model is a RiskMetrics model with a Student-t innovation. As a simple application, we Þnd that the models that provide good density forecast generally provide good forecast of Value-at-Risk.