﻿ 特质流动性风险与资产定价研究—基于中国A股市场数据 Empirical Research on Idiosyncratic Liquidity Risk and Asset Pricing—Based on Data of China’s A-Stock Market

Finance
Vol.08 No.04(2018), Article ID:26039,8 pages
10.12677/FIN.2018.84019

Empirical Research on Idiosyncratic Liquidity Risk and Asset Pricing

—Based on Data of China’s A-Stock Market

Jianfeng Liang, Qiwen Xu

Department of Finance, Lingnan (University) College, Sun Yat-sen University, Guangzhou Guangdong

Received: Jun. 28th, 2018; accepted: Jul. 16th, 2018; published: Jul. 23rd, 2018

ABSTRACT

This paper investigates the effect of idiosyncratic volatility of liquidity on asset pricing based on data of China’s A-stock market. Portfolio analysis and Fama-MacBeth two-stage regression methods are applied in the empirical analysis. It turns out that in China’s stock market, both idiosyncratic illiquidity and idiosyncratic volatility of liquidity present significant and stable positive correlations with the stock returns. Our findings in Chinese market are consistent with those of US markets, which bring future insights that idiosyncratic risk of liquidity matters in asset pricing.

Keywords:Idiosyncratic Liquidity Risk, Asset Pricing, Portfolio Analysis, Cross-Section Regression

—基于中国A股市场数据

1. 引言

2. 研究方法与数据说明

2.1. 研究方法

Amihud (2002) [1] 基于价格冲击测量维度定义了股票个股非流动性指标，指标定义如下：

${c}_{id}=\frac{|{r}_{id}|}{vo{l}_{id}}$ (1)

$illi{q}_{it}=\frac{1}{{D}_{it}}\ast \sum _{d=1}^{{D}_{it}}\text{ }{c}_{id}$ (2)

${c}_{id}={b}_{0i}+{b}_{1i}{C}_{Md}+{b}_{2i}\left({R}_{Md}-{r}_{fd}\right)+{e}_{id}$ (3)

$ivolli{q}_{it}=\frac{\sigma {\left({e}_{id}\right)}_{t}}{illi{q}_{it}}$ (4)

Acharya和Pedersen(2005)提出的LCAPM模型用于研究系统性流动性风险对资产定价的影响效果，该模型定义了三类系统性流动性风险因子，分别表示为 ${\beta }_{c}^{C}$${\beta }_{r}^{C}$${\beta }_{c}^{R}$ 。本文参照Akbas et al. (2011) [12] 的改良方法，引入以下(5)~(6)式两个回归方程分别拟合各贝塔值。首先将月内的个股非流动性水平日数据对相应的市场流动性水平变化值和市场超额收益率进行回归：

${c}_{id}={\alpha }_{i}+{\beta }_{ci}^{C}\Delta {C}_{Md}+{\beta }_{ci}^{R}\left({R}_{Md}-{r}_{fd}\right)+{u}_{id}$ (5)

${r}_{id}-{r}_{fd}={\alpha }_{i}+{\beta }_{ri}^{C}\Delta {C}_{Md}+{\beta }_{ri}^{R}\left({R}_{Md}-{r}_{fd}\right)+{u}_{id}$ (6)

2.2. 数据说明与描述性统计

Table 1. Descriptive statistics of variables

Table 2. Correlations of variables

3. 双变量组合分析

(a) (b)

Table 3. (a) Portfolios based on double-variables (SIZE, BM, TURN); (b) Portfolios based on double-variables (RET12M, ILLIQ, IVOL)

4. 横截面回归分析

${r}_{it+1}-{r}_{f}={r}_{0}+{r}_{1}illi{q}_{it}+{r}_{2}{\beta }_{rit}^{R}+{r}_{3}{\beta }_{cit}^{C}+{r}_{4}{\beta }_{rit}^{C}+{r}_{5}{\beta }_{cit}^{R}+{\epsilon }_{it+1}$ (7)

$\begin{array}{l}{r}_{it+1}-{r}_{f}={r}_{0}+{r}_{1}illi{q}_{it}+{r}_{2}{\beta }_{rit}^{R}+{r}_{3}{\beta }_{cit}^{C}+{r}_{4}{\beta }_{rit}^{C}+{r}_{5}{\beta }_{cit}^{R}+{r}_{6}Co{v}_{t}\left({r}_{it+1},{c}_{it+1}\right)\\ \text{}+{r}_{7}IVO{L}_{it}+{r}_{8}ivolli{q}_{it}+{\epsilon }_{it+1}\end{array}$ (8)

$\begin{array}{l}{r}_{it+1}-{r}_{f}={r}_{0}+{r}_{1}illi{q}_{it}+{r}_{2}{\beta }_{rit}^{R}+{r}_{3}{\beta }_{cit}^{C}+{r}_{4}{\beta }_{rit}^{C}+{r}_{5}{\beta }_{cit}^{R}+{r}_{6}Co{v}_{t}\left({r}_{it+1},{c}_{it+1}\right)\\ \text{}+{r}_{7}IVO{L}_{it}+{r}_{8}ivolli{q}_{it}+{r}_{9}SIZ{E}_{it}+{r}_{10}TUR{N}_{it}+{r}_{11}B{M}_{it}\\ \text{}+{r}_{12}RET12{M}_{it}+{\epsilon }_{it+1}\end{array}$ (9)

(7)式参照Acharya & Pedersen (2005) [5] 模型并引入个股非流动性因子illiq。(8)式进一步引入特质流动性风险因子ivolliq，检验在考虑了系统性流动风险后，特质流动性风险对资产定价的作用。(8)式同时也考虑了Cov(r, c)和特质风险IVOL因子的定价作用。(9)式则补充更多股票特征变量，检验特质流动性风险定价作用的变化。回归结果见表4

Table 4. Result of Fama-Macbeth regression

5. 结论

Empirical Research on Idiosyncratic Liquidity Risk and Asset Pricing—Based on Data of China’s A-Stock Market[J]. 金融, 2018, 08(04): 161-168. https://doi.org/10.12677/FIN.2018.84019

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