Smooth Transition Quantile Capital Asset Pricing Models with Heteroscedasticity

被引:0
作者
Cathy W. S. Chen
Simon Lin
Philip L. H. Yu
机构
[1] Feng Chia University,
[2] The University of Hong Kong,undefined
来源
Computational Economics | 2012年 / 40卷
关键词
Bayesian inference; CAPM; GARCH; Quantile regression; Skewed-Laplace distribution; Smooth transition; C11; C22; C51; C52;
D O I
暂无
中图分类号
学科分类号
摘要
Capital asset pricing model (CAPM) has become a fundamental tool in finance for assessing the cost of capital, risk management, portfolio diversification and other financial assets. It is generally believed that the market risks of the assets, often denoted by a beta coefficient, should change over time. In this paper, we model timevarying market betas in CAPM by a smooth transition regime switching CAPM with heteroscedasticity, which provides flexible nonlinear representation of market betas as well as flexible asymmetry and clustering in volatility. We also employ the quantile regression to investigate the nonlinear behavior in the market betas and volatility under various market conditions represented by different quantile levels. Parameter estimation is done by a Bayesian approach. Finally, we analyze some Dow Jones Industrial stocks to demonstrate our proposed models. The model selection method shows that the proposed smooth transition quantile CAPM–GARCH model is strongly preferred over a sharp threshold transition and a symmetric CAPM–GARCH model.
引用
收藏
页码:19 / 48
页数:29
相关论文
共 64 条
[1]  
Bacon D. W.(1971)Estimating the transition between two intersecting straight lines Biometrika 58 525-534
[2]  
Watts D. G.(1981)The relationship between return and market value of common stocks Journal of Financial Economics 9 3-18
[3]  
Banz R. W.(1982)An empirical quantile function for linear models with iid errors Journal of the American Statistical Association 77 407-415
[4]  
Bassett G.(1986)Generalized autoregressive conditional heteroscedasticity Journal of Econometrics 31 307-327
[5]  
Koenker R.(1992)ARCH modeling in finance; a review of the theory and empirical evidence Journal of Econometrics 52 5-59
[6]  
Bollerslev T.(1986)On estimating thresholds in autoregressive models Journal of Time Series Analysis 7 179-190
[7]  
Bollerslev T.(2009)Bayesian causal effects in quantiles: Accounting for heteroscedasticity Computational Statistics and Data Analysis 53 1993-2007
[8]  
Chou R. Y.(2006)Comparison of non-nested asymmetric heteroscedastic models Computational Statistics and Data Analysis 51 2164-2178
[9]  
Kroner K. F.(1982)Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation Econometrica 50 987-1008
[10]  
Chan K. S.(1992)The cross-section of expected stock returns Journal of Finance 47 427-465