本文關(guān)鍵詞： 流動性風(fēng)險 資本資產(chǎn)定價模型 H-L價差 F-M回歸檢測　出處：《江蘇大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在金融領(lǐng)域中,流動性作為一種影響金融資源分配的決定性因素被公認(rèn)為是引發(fā)股票價格變動的重要影響因子,對流動性的研究已然成為了確定股票定價問題方面的一個重要課題。然而,目前大多數(shù)的資產(chǎn)定價模型往往是在無風(fēng)險環(huán)境的前提下建立的,忽視了流動性風(fēng)險在資產(chǎn)定價中的重要作用,有違市場運作的客觀規(guī)律。本文研究的主要目的是探討在中國股票市場中流動性以及流動性風(fēng)險對股票超額收益的影響,并實證分析加入流動性風(fēng)險因子后的定價模型在股票市場中的適用性問題。本文首先在時間序列和截面上分別對上海股票市場各項因子和流動性進(jìn)行了分析,發(fā)現(xiàn)流動性因子在各因素中波動性最強,且這種不穩(wěn)定性有逐年遞增的趨勢。隨后利用高頻買賣價差指數(shù)作為流動性測度構(gòu)建流動性風(fēng)險因子"到#,并結(jié)合F-F三因素模型,建立無條件下的流動性調(diào)整資產(chǎn)定價模型,進(jìn)而對上海股票市場進(jìn)行分析。結(jié)果顯示系統(tǒng)流動性風(fēng)險以及定價模型在上海股票市場中的定價效果顯著,但流動性風(fēng)險$和#隨著待測股票市值的增大逐漸失去了對超額收益的解釋能力。由于買賣價差指數(shù)是一個高頻指數(shù),測量數(shù)據(jù)異常龐大。本文利用高低價差指數(shù)取代買賣價差,簡化了數(shù)據(jù)處理量,從而在更廣的樣本區(qū)間中進(jìn)行分析,實現(xiàn)對股票多年數(shù)據(jù)的處理。以2008年金融危機(jī)為例,利用定價模型分別在金融危機(jī)之前和之后驗證了流動性風(fēng)險對股票超額收益的影響。結(jié)果表明在熊市中流動性風(fēng)險的溢價影響比在牛市中更強烈。另外,從不同行業(yè)的實證結(jié)果來看,流動性風(fēng)險在鋼鐵行業(yè),農(nóng)林牧業(yè)這類低流動性,同時具有周期性的行業(yè)中對超額收益有較好的解釋作用。為了排除實驗的偶然性,本文利用其他兩種不同的流動性指數(shù)進(jìn)行驗證,結(jié)果依然準(zhǔn)確。最后通過復(fù)雜網(wǎng)絡(luò)技術(shù)發(fā)現(xiàn),自身流動性較低的股票其流動性風(fēng)險在網(wǎng)絡(luò)中的度較低,同時流動性風(fēng)險在相互影響上具有行業(yè)效應(yīng),流動性風(fēng)險容易在相同或相關(guān)的行業(yè)中形成相互影響。
[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.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.51;F224

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本文編號：1543922