流动性调整资产定价模型在中国股票市场中的实证分析

发布时间：2018-02-27 19:02

本文关键词： 流动性风险资本资产定价模型 H-L价差 F-M回归检测　出处：《江苏大学》2017年硕士论文　论文类型：学位论文

【摘要】：在金融领域中,流动性作为一种影响金融资源分配的决定性因素被公认为是引发股票价格变动的重要影响因子,对流动性的研究已然成为了确定股票定价问题方面的一个重要课题。然而,目前大多数的资产定价模型往往是在无风险环境的前提下建立的,忽视了流动性风险在资产定价中的重要作用,有违市场运作的客观规律。本文研究的主要目的是探讨在中国股票市场中流动性以及流动性风险对股票超额收益的影响,并实证分析加入流动性风险因子后的定价模型在股票市场中的适用性问题。本文首先在时间序列和截面上分别对上海股票市场各项因子和流动性进行了分析,发现流动性因子在各因素中波动性最强,且这种不稳定性有逐年递增的趋势。随后利用高频买卖价差指数作为流动性测度构建流动性风险因子"到#,并结合F-F三因素模型,建立无条件下的流动性调整资产定价模型,进而对上海股票市场进行分析。结果显示系统流动性风险以及定价模型在上海股票市场中的定价效果显著,但流动性风险$和#随着待测股票市值的增大逐渐失去了对超额收益的解释能力。由于买卖价差指数是一个高频指数,测量数据异常庞大。本文利用高低价差指数取代买卖价差,简化了数据处理量,从而在更广的样本区间中进行分析,实现对股票多年数据的处理。以2008年金融危机为例,利用定价模型分别在金融危机之前和之后验证了流动性风险对股票超额收益的影响。结果表明在熊市中流动性风险的溢价影响比在牛市中更强烈。另外,从不同行业的实证结果来看,流动性风险在钢铁行业,农林牧业这类低流动性,同时具有周期性的行业中对超额收益有较好的解释作用。为了排除实验的偶然性,本文利用其他两种不同的流动性指数进行验证,结果依然准确。最后通过复杂网络技术发现,自身流动性较低的股票其流动性风险在网络中的度较低,同时流动性风险在相互影响上具有行业效应,流动性风险容易在相同或相关的行业中形成相互影响。
[Abstract]:In the field of finance, liquidity, as a decisive factor affecting the distribution of financial resources, is recognized as an important factor that causes the change of stock price. The study of liquidity has become an important issue in the determination of stock pricing. However, most of the current asset pricing models are often established on the premise of risk-free environment. The important role of liquidity risk in asset pricing is ignored, which is contrary to the objective law of market operation. The main purpose of this paper is to explore the effect of liquidity and liquidity risk on excess return of stock in Chinese stock market. And empirical analysis of the applicability of the pricing model with liquidity risk factor in the stock market. Firstly, this paper analyzes the factors and liquidity of Shanghai stock market in time series and section respectively. It is found that the liquidity factor is the most volatile among the factors, and the instability is increasing year by year. Then the liquidity risk factor is constructed by using the high frequency spread index as the liquidity measure, and the F-F three-factor model is combined with the liquidity risk factor. An unconditional liquidity adjusted asset pricing model is established to analyze the Shanghai stock market. The results show that the system liquidity risk and the pricing effect of the pricing model in Shanghai stock market are remarkable. But liquidity risk $and # gradually lost their ability to explain excess returns with the increase of market value, a stock to be tested. The measurement data is very large. In this paper, the high and low price difference index is used to replace the buying and selling spread, which simplifies the data processing, and then analyzes the data in a wider sample range, and realizes the processing of the stock data for many years. Taking the financial crisis of 2008 as an example, The effect of liquidity risk on excess return of stock is verified by pricing model before and after the financial crisis. The results show that the premium effect of liquidity risk in bear market is stronger than that in bull market. According to the empirical results of different industries, liquidity risk is low liquidity in steel industry, agriculture, forestry and animal husbandry, and has a better explanation for excess income in cyclical industries. In this paper, two different liquidity indices are used to verify that the results are still accurate. Finally, through the complex network technology, it is found that the liquidity risk of stocks with less liquidity is lower in the network. At the same time, liquidity risk has industry effect on mutual influence, and liquidity risk is easy to form mutual influence in the same or related industries.
【学位授予单位】：江苏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F832.51;F224

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