主题：Statistical Inference on Term Structure of Interest Rate Models with Bond Prices
澳大利亚国立大学助理教授，北京大学光华管理学院经济学博士，曾是澳大利亚莫纳什大学、美国爱荷华州立大学访问学者；主要从事金融统计和金融计量等方面的教学与研究工作；曾在Journal of Econometrics、Journal of Business and Economic Statistics、Journal of American Statistical Association、Statistica Sinica等国际知名期刊发表多篇论文。
Affine term structure models account for a wide range of interest rate models and have been used to describe the dynamic of bond prices in finance.The first part of this paper considers improving estimating parameters of diffusion processes for interest rates by incorporating information in bond prices. This is designed to improve the estimation of the drift parameters, which are known to be subject to large estima- tion errors. It is shown that having the bond prices together with the short rates leads to more efficient estimation of all parameters for the interest rate models. It enhances the estimation efficiency of the maximum likelihood estimation based on the interest rate dynamics alone. The combined estimation based on the bond prices and the interest rate dynamics can also provide inference to the risk premium parameter. Simulation experiments were conducted to confirm the theoretical properties of the estimators concerned.The second part of this paper proposes methods to determine the number of latent factors in the affine term structure models, which is an unresolved issue in the literatures of multi-factor modeling. By minimizing a proper penalized criterion function, the number of latent factors can be consistently estimated. It is also found that the proposed method is effective in identifying which bond prices are subject to pricing errors. The identification is required in the affine model estimation. Simulation experiments were conducted to confirm the theoretical properties of different penalty criteria.Finally, this paper analyzes the overnight Fed fund rates together with the U.S. Treasury bond prices by using the proposed methods.