【作者】 王庆石； 彭宜钟；

【Author】 QINGSHI WANG YIZHONG PENG (Dongbei University of Finance and Economics)

【机构】 东北财经大学数量经济系；

【摘要】 本文通过对非线性随机贴现因子进行泰勒展开和多项式正交化推导出了无条件泰勒定价模型,并基于我国A股市场数据将该模型同Fama French三因子模型、Learning-CCAPM模型和ARCH类资产定价模型(经过检验,我国数据没有体现出ARCH效应)进行了全面比较。通过比较发现无条件泰勒定价模型对我国数据的拟合效果最为理想。而且,无条件泰勒定价模型所需要的数据很容易获得。因此,无条件泰勒定价模型在我国具有很高的应用价值。更多还原

【Abstract】 We derive a pricing model,the unconditional Taylor pricing model(NTPM) by taking a Taylor expansion on an unconditional discount factor(SDF) and omitting the high-order terms.Then we make comparison between NTPM and the Fama-French three-factor model,Learning-CCAPM model,ARCH related models(we find no powerful evidence for the existence of ARCH(l) disturbances in our sample) respectively based on the same da- ta sets from China’s A-share market.Eventually we find that NTPM outperforms its counterparts reasonably.NTPM’s need for data can easily be met.This is especially important for studies on China.更多还原