An Empirical Study On Dynamic Behavior Of Short - Term Interest Rate In Chinese Market

Interest rate is one of the most important economic variables and has always been the focus of study in the financial market. The dynamic behavior of spot rate play a key role in the field of asset pricing and financial risk management. In the last twenty years, China has made a breakthrough in the reform of interest rate marketization, which leads to significant changes in the behavior of interest rate. For these reasons, the research on the spot rate in China is both of theoretical significance and of practical value.Having made enough comparison and analysis of the current interest rate stochastic modeling literature, we select the1-day repo rate in the interbank market as a representative of the spot rate and build various models to capture the mean-reverting phenomenon, time-varying volatilaty and jump characteristics of interest rate behavior. We use Markov Chain Monte Carlo method to calibrate the model.We have got the following conclusions:firstly, we found signficant mean-reverting characteristics in the spot rate with high reverting speed. This phenomenon make vasicek the best performer in the one-factor model class. Secondly, we built CKLS, Vasicek-GARCH(1,1) and CKLS-GARCH(1,1) model to analyze the volatility of spot rate. We found that the volatility clustering can be well captured by the GARCH(1,1) model and the level effect diminished after the GARCH(1,1) adjustment. Furthermore, we build Vasicek-JUMP and Vasicek-GARCH(1,1)-JUMP models, respectively for testing the existence of jump phenomenon and the relative relationship of GARCH(1,1) and jumps. Although the result verified the jump behavior, neither model are well specified after introducing the jump part. In this article, we also document the advantages of MCMC method over Maximum likelihood when dealing with the dynamic data.