“Spillover Effects of the U.S. Monetary Policy on Emerging Market Economies”, with Jun Ma and Chengsi Zhang.

Abstract: This paper examines the spillover effects of United States monetary policy on select emerging market economies, specifically Brazil, Russia, India, China, and South Africa (BRICS), utilizing an external instrumental variable for identification. We employ a time-varying-parameter factor-augmented vector autoregressive model with stochastic volatility (TVP-FAVAR-SV) to analyze the dynamic responses of these emerging market economies’ macroeconomic indicators to shocks in US monetary policy over time. Our findings reveal that the spillover effects align with conventional wisdom and established economic theories. Specifically, a contractionary US monetary policy shock leads to the depreciation of emerging market currencies, an increase in their outputs, and a reduction in inflation. These effects exhibit time variation.

“Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor versus National Factor in a GARCH-MIDAS Model”, with Afees A. Salisu, Rangan Gupta, and Oguzhan Cepni. 

Abstract: The aim of this paper is to utilize the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to predict the daily volatility of state-level stock returns in the United States (US), based on the weekly metrics from the corresponding broad Economic Conditions Indexes (ECIs). In light of the importance of a common factor in explaining a large proportion of the total variability in the state-level economic conditions, we first apply a Dynamic Factor Model with Stochastic Volatility (DFM-SV) to filter out the national factor from the local components of weekly state-level ECIs. We find that both the local and national factors of the ECI generally tend to affect state-level volatility negatively. Furthermore, the GARCH-MIDAS model, supplemented by these predictors, surpasses the benchmark GARCH-MIDAS model with realized volatility (GARCH-MIDAS-RV) in a majority of states. Interestingly, the local factor often assumes a more influential role overall, compared to the national factor. Moreover, when the stochastic volatilities associated with the local and national factors are integrated into the GARCH-MIDAS model, they outperform the GARCH-MIDAS-RV in over 80 percent of the states. Our findings have important implications for investors and policymakers.  

"Does US Financial Uncertainty Spill Over through the (Asymmetric) International Credit Channel? The Role of Market Expectations", with Yu-Fan Huang, and Taining Wang. （Revise and Resubmit, Journal of International Money and Finance）

"Does the Yield Spread Still Forecast the GDP Growth? New Evidence Based on a New Approach", with Jun Ma and Chang-Jin Kim.

Abstract: In this paper, we propose a novel approach that is based on a type of constrained vector ARMA model that can deal with both the persistence of the yield spread variable and its potential structural breaks. More importantly, we show that our proposed framework permits a more general type of nonlinear Granger causality relationship that encompasses the linear Granger causality as a special case. Due to the attractive feature of the proposed two-step estimation procedure, our approach can conveniently account for potential structural breaks in the yield spread when testing the more general Granger causality relationship between the yield spread and the real GDP growth.

“The Control Function Approach in Predictive Regressions”, with Jun Ma and Chang-Jin Kim.

Abstract: In this paper, we apply the control function method to the predictive regressions. We use a two-step estimation procedure to deal with the biased coefficients issue and the generated regressor issue. Through the simulation, we show that our approach is almost identical to the bias correction procedure in Amihud and Hurvich (2004), when the error terms are i.i.d. When the disturbances are heteroscedastic, our method works better both in the size and the power of the test. We will apply our method to the prediction of stock returns with financial ratios.