【作者】 王灵芝；

【导师】 杨朝军；

【作者基本信息】 上海交通大学 ， 金融学， 2010， 博士

【摘要】 证券市场的流动性与流动性风险是现代微观金融领域研究的重要内容。论文以中国证券市场流动性风险为研究对象,在分析流动性风险内涵的同时,对两个重要概念进行了区分,流动性水平指的是流动性的大小,而流动性风险指的是流动性水平未来变化的不可预期性,在学术研究中常用波动率来刻画这种不确定性。本文提出了基于条件方差理论的流动性风险测度方法,并结合我国金融市场的现状,探讨了我国股市中流动性风险的影响因素与特征。流动性风险不同于其它的金融风险,它是一种交易风险,发生在买入或卖出资产的时候。本文以日间流动性风险研究为主线,并用少量篇幅研究了基于分时数据和逐笔成交数据的日内流动性风险。08年席卷全球的金融危机,使广大投资者特别是基金管理者体会到了流动性风险管理的重要性。在资产价格上涨的过程中,投资者往往容易忽略流动性风险,在市场急剧下跌甚至恐慌时,基金管理人为应对投资者的赎回,需要对资产进行变卖,大额买卖势必对股票价格产生冲击,投资组合的流动性风险增加,准确的测度并管理资产或投资组合的流动性风险有着重要的指导意义。本文主要工作及研究结论如下:首先,论文在区分流动性水平与流动性风险两个概念的基础上,对流动性风险的内涵进行分析,并基于其内涵与已有的流动性风险测度研究,探讨了适合中国指令驱动市场的流动性风险测度方法。时变的条件方差法在刻画金融时间序列的波动性特征方面具有优势,文中通过研究流动性测度指标的波动特性对日间流动性风险进行了量化,考察了我国股市中流动性风险的特性。其次,结合中国股票市场的特征,定性分析了影响我国市场流动性风险的主要因素,包括:投资者结构与投资行为,政策性调节,以及股市的波动三个方面。文中还通过构建适当的理论模型,实证分析了以上三个因素对市场流动性风险的影响:(1)投资者结构模式变迁与市场流动性风险。文中通过构建TGARCH模型,实证研究发现,机构投资者占主导后,我国市场的流动性风险显著降低。(2)政策性因素,例如央行基准利率、存款准备金率与印花税调整是否对市场的流动性状况等有所影响?文中通过构建恰当的虚拟变量,研究发现,政策性消息公布后的一周内,市场流动性风险显著增加。(3)在股市危机中,市场风险与流动性风险相互促进,螺旋式上升。文中采用动态条件相关(DCC)模型实证研究发现,危机爆发后,两者的相关性显著增强。再次,VaR方法是一种衡量和管理金融市场风险的新方法,用一个数字有效地量化一个投资组合正在承受的风险,简单易懂。本文基于Risk Metrics的移动平均模型和条件方差测度方法动态的测度了市场的流动性风险VaR,并采用kupiec失败率检验法对其有效性进行了检验。在极端情况下,金融时间序列具有较厚的尾部数据,前述VaR方法会低估投资组合实际承受的流动性风险,给投资者带来额外的损失。极值理论是通过研究极端样本事件对金融资产回报的“厚尾”分布建模,用于分析和解释极端事件,它是测量极端市场环境中风险损失的一种方法。分块样本最大法和超阈值模型是极值理论常用的两种方法,文中先后采用这两种方法对流动性变化率序列的尾部行为进行参数估计,计算出了相应的流动性风险VaR值,并进行了相应的有效性检验。此后,结合中国的证券市场背景,对流动性风险测度方法的应用性展开研究。主要研究内容包括以下三个方面:1)学术界对于流动性、流动性风险是否影响资产收益展开了广泛研究,然而却没有得到一致的结论。本文基于面板数据回归分析了收益率与流动性水平、流动性风险之间的关系。2)引起一个市场波动的信息会溢出到另一个关联市场,即存在着风险溢出效应,文中基于多元GARCH-BEKK模型研究了沪、美、港股之间的流动性风险溢出效应。3)金融危机背景下,管理基金的流动性风险已成为基金管理者的重要任务之一。现有对基金业绩评估的研究文献中,均不曾考虑基金所承受的流动性风险,本文尝试从流动性风险角度构建一个新的基金业绩评估方法。最后,基于日内高频数据的流动性反映了投资者的交易成本与主动性。文中从市场遭受冲击时投资者的策略博弈角度分析了流动性风险产生的原因;并基于分时数据和逐笔成交数据提出了适合中国指令驱动市场的日内流动性风险测度方法;在市场的某些阶段买卖双方流动性水平可能截然不同,国内首次将买方与卖方流动性两个概念区分,并考察了日内买卖流动性水平的非均衡与股市价格之间的关系;文中还基于逐笔成交数据,提出了结合流动性四维,即宽度、深度、弹性和成交时间因素的日内流动性综合测度指标,研究了该指标的自相关性、状态依赖性等特征,并采用时变的方差测度了逐笔成交数据的流动性风险。更多还原

【Abstract】 Stock market’s liquidity and liquidity risk are important parts in the modern micro financial fields. This thesis chooses liquidity risk in China’s stock market as the research object. Firstly, we analyzed the implication of liquidity risk and pointed out that“liquidity risk means the uncertainty of liquidity’s change in the future”, volatility is a good way to capture this character in academic. The thesis put up with a method to measure the liquidity risk by using of the conditional variance theory. With the background of Chinese financial market, we discussed the factors which influenced the liquidity risk of China’s stock market. Liquidity risk, which occurs at your buying or selling the assets, is different to other financial risk, it’s a trading risk. This paper mainly considered the interday liquidity risk, and the intraday liquidity risk was a useful supplement. The world-wide financial crisis broken at 2008, which made most of the investors and especially the fund manager realize the importance of managing the liquidity risk. In the rising process of asset price, investors often ignore the asset’s liquidity risk, but when there is a sudden drop or even a panic, the mutual fund managers have to sell off the stocks they are holding to cope with the investor’s redemption, a large amount of transaction certainly will strike the stock’s price, which increased the portfolio’s liquidity risk. Measure and manage the assets or portfolio’s liquidity risk exactly are very instructive.The main parts and conclusions of this dissertation are as follows:Firstly, after clarifying the concepts of liquidity level and liquidity risk, this dissertation analyzed the meaning of liquidity risk. Based on the presented liquidity risk measurement theory, the thesis probes a method to measure liquidity risk, which is fitting for China’s order driven stock market. The thesis studied the volatility characteristic and measured the interday liquidity risk of China’s stock market by use of the conditional variance method. Secondly, this dissertation analyzed the main factors qualitatively, which have impaction to the liquidity risk, combing the features of China’s Stock market, Such as, investor’s structure and behavior, adjustment policy of macro-economy, and stock market’s volatility. Furthermore, this paper composed property model and empirically studied the influence of these factors to the market’s interday liquidity risk.(1)Transition of investors’structure to market’s liquidity risk. Through a dummy variable in TGARCH model, the empirically study found that: the institutional investor leading market had a notable lower market liquidity risk. (2) Do policy factors, such as benchmark interest rate, reserve ratio and stamp duty’s adjustment, have any impaction on the market’s liquidity condition? Through a proper dummy variable in EGARCH model, the empirical study show that, in the first week after the policy news announced, the liquidity risk increased notably. (3) In the stock market crisis, market risk and liquidity risk help each other,upward spirally. By use of the dynamic conditional correlation (DCC) model, it was found that, after the financial crisis broken out, the correlation of market risk and liquidity risk increased.Thirdly, Value at Risk is a new method to measure and manage financial risk, it’s easy to understand the risk that a portfolio is bearing by use of a digit. Based on Risk Metrics’moving average model and conditional variance method, the thesis dynamically computed the VaR of market’s liquidity risk,and used kupiec’s failure ratio to test it’s validity. Under the extreme conditions, financial time series always have much thicker tail data, the liquidity risk of a portfolio could be underestimated, which brings about non-necessary loss to the investors. By modeling the tail of assets’return series, extreme value theory can be used to analyze and explain the extreme events, which is a method to measure down-half loss in the extreme condition and can be exactly describe the quantiles of distribution. Block maxima method (BMM) and peaks over the thresholds are two common methods in the extreme value theory, the thesis used these methods to study the tail behavior of the liquidity change series and estimate the parametrics, computed the VaR of liquidity risk and proceeded corresponding effectiveness test.Forthly, the dissertation researched the appliance of liquidity risk combing with the China’s stock market. Three contents were considered: (1) Whether liquidity and liquidity risk affect the assets’return or not is a popular topic in the academic, but there isn’t a coincident result. So this paper studied the correlation among return, liquidity and liquidity risk by choosing panel data’s regression method. (2) The information arouse volatility of one market can spill over to another related market, that is volatility spillover effects, based on the multivariate GARCH-BEKK model this thesis studied the liquidity risk spillover effect among American, Hongkong and Shanghai stock market. (3) In the background of financial crisis, manage the fund’s liquidity risk is a main task for fund’s manager. But the presented literatures for mutual fund’s performance evaluation ignored the liquidity risk, the paper tried to put up with a valuation method based on liquidity risk adjustment.At last, intraday liquidity risk reports the initiative of the investors’trading, the dissertation measured and charactered the intraday liquidity risk, and studied the source of liquidity risk from the view of investors’strategy game when the market was suffering a strike; This paper put up a method to measure the intraday liquidity risk based on the time division data and trader by trader data;The seller and buyer could have totally different liquidity level in some stage, the thesis distinguished notion of the seller and buyer’s liquidity and studied the relation between stock’s price and the unbalanced liquidity level of investors. Furthermore, the dissertation put up with an intraday liquidity risk composite measure index, which is containing the liquidity’s four dimensions, i.e. bread, depth, resilience and waiting time and studied its traits, such as, autocorrelation and state dependence and measured the value of intraday liquidity risk based on the trader by trader data using the time varied variance method.更多还原