一种新的金融动态横截面估计方法——基于中国股票市场条件定价模型评估的应用与扩展

大多数资产定价模型常常用静态横截面回归(the static cross-sectional regression)进行定价表现评估,从而投资组合回报率的时间变化性并不能被时变的风险承载或者(和)时变的风险溢价所解释.本文从经济学的角度,运用一种新的金融动态横截面回归(the dynamic cross-sectional regression),首次考察了基于中国股票市场和美国股票市场的条件资产定价模型的定价表现:股票市场投资组合回报率的时变性是否能被时变的风险溢价所解释.本文发现,短期收益反转和流通市值加权市场换手率为条件变量的条件资本资产定价模型和基于消费的条件资本资产定价模型,能更好的解释中国股票投资组合的回报时变性,其时变性主要来自于时变的风险溢价.另外,本文发现一些拥有持续(persistence)和缓慢变化(slow-moving)特性的条件变量更能够解释横截面投资组合的时变回报.

A new dynamic cross-sectional evaluation of asset pricing models: Application and extension of the evaluation on Chinese Stock Market conditional models

Most recent conditional asset pricing models are evaluated by the static Fama-MacBeth cross-sectional regressions, therefore the time-varying risk cannot be evaluated by constant risk loading and risk premiums.This paper, from the economic perspectives, applies a brand-new method-The dynamic cross-sectional regression-To investigate the performances of conditional asset pricing models: whether the time-varying returns can be explained by the time-varying risk premiums.Theoretically, this paper evidences that returns on assets depend on the linear risk premium function and innovations of the economy.Empirically, the paper tests the conditional asset pricing models' pricing performances based on Chinese and US stock markets.The paper finds that the short-term reversal rate and the turnover rate as the conditional variables can help CAPM and CCAPM to explain several test assets' time-varying returns.Moreover, this paper also tests the classic conditional asset pricing models in explaining different assets' time-varying returns.The paper finds that the persistent and slow-moving conditional variables can be better candidates for our conditional asset pricing models.