A Non-linear Estimation of the Capital Asset Pricing Model: The Case of Japanese Automobile Industry Firms
A Non-linear Estimation of the Capital Asset Pricing Model: The Case of Japanese Automobile Industry Firms
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资本资产定价模型的非线性估计：日本汽车工业企业的案例
DOI：
10.11114/afa.v3i2.2331
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Chikashi Tsuji
中科院分区：
文献类型：
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作者：
Chikashi Tsuji;Chikashi Tsuji;Chikashi Tsuji;Chikashi Tsuji;Chikashi Tsuji
本文利用日本主要汽车工业公司的月股票收益率，定量地检验了非线性资本资产定价模型（CAPM）。应用最大似然法，我们得出以下有趣的发现。(1)首先，在股票收益率分布具有厚尾的情况下，我们的非线性CAPM是非常有效的。由于我们的非线性CAPM的参数很好地捕捉厚尾收益率分布，非线性模型估计得到可靠的估计β值。(2)其次，在股票收益服从正态分布的情况下，我们的非线性CAPM也是有效的。由于我们的非线性CAPM的参数通过调整其自由度参数值也很好地捕捉了正态分布的收益，因此非线性模型估计与标准线性CAPM的估计类似地得出可靠的beta估计。(3)最后，我们进一步进行了Wald测试的基础上，从标准的CAPM和我们的非线性CAPM的估计，我们认为，在股票收益率分布的情况下，基于我们的非线性CAPM的估计的Wald测试应更可靠的基础上，从标准的线性CAPM的估计的Wald测试。
This paper quantitatively examines a non-linear capital asset pricing model (CAPM) by using monthly stock returns of major automobile industry firms in Japan. Applying the maximum likelihood method, we derive the following interesting findings.(1) First, in the case where the distribution of stock returns has a fat-tail, our non-linear CAPM is highly effective. Because the parameters of our non-linear CAPM well capture fat-tailed return distributions, the non-linear model estimation derives reliable estimates of beta values.(2) Second, in the case where stock returns are normally distributed, our non-linear CAPM is also effective. Since the parameters of our non-linear CAPM also well capture normally distributed returns by adjusting its degrees of freedom parameter value, the non-linear model estimation similarly derives reliable beta estimates as those derived from the standard linear CAPM.(3) Finally, we further conduct the Wald tests based on the estimators from the standard CAPM and our non-linear CAPM, and we suggest that in the case where the distribution of stock returns has a fat-tail, the Wald test based on the estimators from our non-linear CAPM shall be more reliable than the Wald test based on the estimators from the standard linear CAPM.