本文關(guān)鍵詞： 流動(dòng)性風(fēng)險(xiǎn) 資本資產(chǎn)定價(jià)模型 H-L價(jià)差 F-M回歸檢測(cè)　出處：《江蘇大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：在金融領(lǐng)域中,流動(dòng)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(jià)格變動(dòng)的重要影響因子,對(duì)流動(dòng)性的研究已然成為了確定股票定價(jià)問(wèn)題方面的一個(gè)重要課題。然而,目前大多數(shù)的資產(chǎn)定價(jià)模型往往是在無(wú)風(fēng)險(xiǎn)環(huán)境的前提下建立的,忽視了流動(dòng)性風(fēng)險(xiǎn)在資產(chǎn)定價(jià)中的重要作用,有違市場(chǎng)運(yùn)作的客觀規(guī)律。本文研究的主要目的是探討在中國(guó)股票市場(chǎng)中流動(dòng)性以及流動(dòng)性風(fēng)險(xiǎn)對(duì)股票超額收益的影響,并實(shí)證分析加入流動(dòng)性風(fēng)險(xiǎn)因子后的定價(jià)模型在股票市場(chǎng)中的適用性問(wèn)題。本文首先在時(shí)間序列和截面上分別對(duì)上海股票市場(chǎng)各項(xiàng)因子和流動(dòng)性進(jìn)行了分析,發(fā)現(xiàn)流動(dòng)性因子在各因素中波動(dòng)性最強(qiáng),且這種不穩(wěn)定性有逐年遞增的趨勢(shì)。隨后利用高頻買(mǎi)賣(mài)價(jià)差指數(shù)作為流動(dòng)性測(cè)度構(gòu)建流動(dòng)性風(fēng)險(xiǎn)因子"到#,并結(jié)合F-F三因素模型,建立無(wú)條件下的流動(dòng)性調(diào)整資產(chǎn)定價(jià)模型,進(jìn)而對(duì)上海股票市場(chǎng)進(jìn)行分析。結(jié)果顯示系統(tǒng)流動(dòng)性風(fēng)險(xiǎn)以及定價(jià)模型在上海股票市場(chǎng)中的定價(jià)效果顯著,但流動(dòng)性風(fēng)險(xiǎn)$和#隨著待測(cè)股票市值的增大逐漸失去了對(duì)超額收益的解釋能力。由于買(mǎi)賣(mài)價(jià)差指數(shù)是一個(gè)高頻指數(shù),測(cè)量數(shù)據(jù)異常龐大。本文利用高低價(jià)差指數(shù)取代買(mǎi)賣(mài)價(jià)差,簡(jiǎn)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(shí)現(xiàn)對(duì)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(jià)模型分別在金融危機(jī)之前和之后驗(yàn)證了流動(dòng)性風(fēng)險(xiǎn)對(duì)股票超額收益的影響。結(jié)果表明在熊市中流動(dòng)性風(fēng)險(xiǎn)的溢價(jià)影響比在牛市中更強(qiáng)烈。另外,從不同行業(yè)的實(shí)證結(jié)果來(lái)看,流動(dòng)性風(fēng)險(xiǎn)在鋼鐵行業(yè),農(nóng)林牧業(yè)這類(lèi)低流動(dòng)性,同時(shí)具有周期性的行業(yè)中對(duì)超額收益有較好的解釋作用。為了排除實(shí)驗(yàn)的偶然性,本文利用其他兩種不同的流動(dòng)性指數(shù)進(jìn)行驗(yàn)證,結(jié)果依然準(zhǔn)確。最后通過(guò)復(fù)雜網(wǎng)絡(luò)技術(shù)發(fā)現(xiàn),自身流動(dòng)性較低的股票其流動(dòng)性風(fēng)險(xiǎn)在網(wǎng)絡(luò)中的度較低,同時(shí)流動(dòng)性風(fēng)險(xiǎn)在相互影響上具有行業(yè)效應(yīng),流動(dòng)性風(fēng)險(xiǎn)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F832.51;F224

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本文編號(hào)：1543922