Date of Degree


Document Type


Degree Name





Merih Uctum

Committee Members

Yochanan Shachmurove

Chun Wang

Subject Categories

Econometrics | Finance | International Economics | Macroeconomics


Spillover effect, GARCH, monetary policy, stock market, ThSVAR, volatility


Chapter1: With the rapid development and continuous advancement of economic globalization, the links between countries around the world have become increasingly tight. Among them, the United States, as the world's largest economy, its monetary policy is bound to cause significant spillover effects on other economies around the world. By constructing a Threshold SVAR model with monthly data from 1996 to 2019, this paper empirically investigates the spillover effects of US monetary policy on China's economy during different U.S policy regimes. The transmission mechanism of such effects has been tested through different channels including policy channel, trade channel, asset value channel and information channel. The estimated threshold values of the Fed Fund rates/Shadow rates are between 0.905-0.990, which coincides with the unconventional policy period of U.S monetary policy. Also, the responses of different channels to U.S. monetary shocks in the lower regime (unconventional time) are different from those responses in the upper regime (conventional time).

Chapter 2: In 2015, the benchmark stock market index in China - the Shanghai Stock Exchange Composite Index lost over 40% within two months. This stock market crash along with the 2007-2008 financial crisis are widely considered to be the greatest bubble bursts in Chinese Stock Market history and have attracted considerable attention to the study of Chinese stock market bubbles since then. To address such issue, this paper analyses the spillover effect of the U.S monetary policy on Chinese stock market bubbles. Firstly, the bubbles are constructed based on the long-term equilibrium relationship between stock prices and domestic macroeconomic fundamentals by employing Vector Error Correction model (VECM). Then a SVAR model is conducted to investigate the spillover effect of the U.S monetary policy on the bubbles by incorporating the shadow rates (Wu and Xia, 2016) for the periods before and after 2008 financial crisis. The full sample includes monthly data between 1996 and 2016, which contains all the major stock markets bubbles in China.

Chapter 3: This paper aims to study the volatility spillover effects as well as the dynamic conditional correlation between stock market returns in China and the U.S. Firstly, the analysis uses a vector autoregression with a bivariate BEKK-GARCH model to capture the asymmetric volatility transmissions between the two markets during the sample of 1996-2019. Then a VAR-DCC-GARCH model is employed to estimate the dynamic conditional correlation between these two market returns. Finally, linear regression and Granger Causality test are conducted to further explore the effect of the U.S policy rates on such correlation. In order to account for the U.S monetary stances during the unconventional period, a combination of Fed fund rates and Shadow rates developed by Wu and Xia (2016) is used as policy rates. The main empirical results suggest (1) evidence of unidirectional volatility spillover from the U.S. to China market; but no spillover from China to U.S; (2) the dynamics of the conditional correlations from the VAR-DCC-GARCH model exhibit increases in correlation between the stock returns of China and U.S after 2008 financial crisis and recent trade war; (3) a linear regression shows that there is negative relationship between U.S policy rates and the dynamic conditional correlation, with the correlation coefficient r=-0.62. Granger Causality test suggests that the U.S policy rates do cause the change of the conditional correlation but not the other way around.

This work is embargoed and will be available for download on Saturday, January 21, 2023

Graduate Center users:
To read this work, log in to your GC ILL account and place a thesis request.

Non-GC Users:
See the GC’s lending policies to learn more.