【摘要】：在金融市場(chǎng)全球化與衍生品交易不斷繁榮的背景下,多起金融危機(jī)事件的發(fā)生促使了VaR的誕生,它已成為市場(chǎng)風(fēng)險(xiǎn)的標(biāo)準(zhǔn)計(jì)量方法,在金融界與學(xué)術(shù)界廣受關(guān)注。雖然VaR具有綜合性、量化性、通俗性等優(yōu)點(diǎn),但仍然存在諸多缺陷,其中之一即為假設(shè)投資組合不論頭寸大小都能以當(dāng)前市場(chǎng)價(jià)格瞬間出清,從而忽略了流動(dòng)性風(fēng)險(xiǎn)。本文將流動(dòng)性風(fēng)險(xiǎn)納入VaR基本框架,并且分別基于高頻數(shù)據(jù)與超高頻數(shù)據(jù)構(gòu)建流動(dòng)性調(diào)整的VaR模型(La-VaR),最后作實(shí)證研究與對(duì)比分析。 本文的核心工作之一是針對(duì)高頻數(shù)據(jù)建立La-VaR模型。對(duì)高頻數(shù)據(jù)的La-VaR建模是源于BDSS模型基本框架,但是針對(duì)其正態(tài)分布假設(shè)、同方差假設(shè)、相對(duì)價(jià)差與中間價(jià)格不相關(guān)假設(shè)、市場(chǎng)風(fēng)險(xiǎn)與流動(dòng)性風(fēng)險(xiǎn)同步最大化假設(shè)、流動(dòng)性非動(dòng)態(tài)假設(shè)等缺陷作出了改進(jìn),不僅可計(jì)算單個(gè)資產(chǎn),還可計(jì)算投資組合的La-VaR。 具體地,首先構(gòu)建GJR-GARCH-EVT-kernel模型刻畫(huà)收益率序列的尖峰厚尾性、異方差性、波動(dòng)非對(duì)稱性以及上下尾極值分布特點(diǎn),并采用多元Copula模型捕捉不同資產(chǎn)序列之間的相關(guān)結(jié)構(gòu),接著采用蒙特卡羅模擬對(duì)收益率序列進(jìn)行一步預(yù)測(cè)。第二,類似地采用GJR-GARCH-EVT-kernel模型擬合相對(duì)價(jià)差的邊緣分布,然后分別在每個(gè)單一資產(chǎn)中,采用二元阿基米德Copula模型刻畫(huà)相對(duì)價(jià)差與收益率之間的相關(guān)結(jié)構(gòu),再基于預(yù)測(cè)的收益率序列生成相對(duì)價(jià)差的偽隨機(jī)數(shù)作為其一步預(yù)測(cè)序列。第三,通過(guò)多條路徑的預(yù)測(cè)最后得出交易價(jià)格的分位數(shù),進(jìn)一步便求出La-VaR值。 實(shí)證分析表明VaR存在一定程度上的風(fēng)險(xiǎn)低估,La-VaR計(jì)量的風(fēng)險(xiǎn)中流動(dòng)性風(fēng)險(xiǎn)比例一般為10%-20%,并且經(jīng)無(wú)條件覆蓋性、獨(dú)立性、條件覆蓋性等檢驗(yàn),La-VaR基本上不存在風(fēng)險(xiǎn)低估現(xiàn)象,要明顯優(yōu)于VaR模型。 本文的第二個(gè)核心工作是基于超高頻數(shù)據(jù)建立La-VaR模型。前述基于高頻數(shù)據(jù)的La-VaR模型實(shí)際上是對(duì)相等時(shí)間間隔序列進(jìn)行建模,但是超高頻數(shù)據(jù)與傳統(tǒng)時(shí)間序列相比存在著本質(zhì)區(qū)別,即非等時(shí)間間隔性,因此無(wú)法直接應(yīng)用傳統(tǒng)時(shí)間序列模型,需要引入持續(xù)期序列,并對(duì)非等時(shí)間間隔的收益率與相對(duì)價(jià)差進(jìn)行轉(zhuǎn)換后再建模。 具體地,首先建立WACD模型擬合持續(xù)期序列,并迭代預(yù)測(cè)多步持續(xù)期直至累計(jì)持續(xù)期之和達(dá)到高頻時(shí)間間隔(如1分鐘)。第二,采用持續(xù)期對(duì)超高頻數(shù)據(jù)序列進(jìn)行轉(zhuǎn)換后得到單位收益率與單位相對(duì)價(jià)差以滿足相等時(shí)間間隔性,再將其納入前述的GJR-GARCH-EVT-kernel-Copula框架中進(jìn)行參數(shù)估計(jì)與多步預(yù)測(cè),其步數(shù)與持續(xù)期的預(yù)測(cè)步數(shù)相同。第三,將多步預(yù)測(cè)的單位收益率與單位相對(duì)價(jià)差經(jīng)持續(xù)期轉(zhuǎn)換得到多步預(yù)測(cè)的分筆收益率與相對(duì)價(jià)差,再聚合得到預(yù)測(cè)的高頻時(shí)間間隔(如1分鐘)的收益率與相對(duì)價(jià)差。最后,通過(guò)多條路徑的預(yù)測(cè)得出交易價(jià)格的分位數(shù)進(jìn)而求出La-VaR值。 實(shí)證研究不僅能得出類似基于高頻數(shù)據(jù)計(jì)算La-VaR的結(jié)論,而且對(duì)于流動(dòng)性較差的資產(chǎn),VaR度量的風(fēng)險(xiǎn)存在顯著的低估現(xiàn)象,而基于超高頻數(shù)據(jù)的La-VaR能較準(zhǔn)確地反映流動(dòng)性風(fēng)險(xiǎn),不會(huì)產(chǎn)生低估。對(duì)比基于高頻與超高頻數(shù)據(jù)的計(jì)算結(jié)果后發(fā)現(xiàn)：后者的VaR、La-VaR失敗時(shí)間節(jié)點(diǎn)數(shù)與理論值更接近、波動(dòng)范圍更小,說(shuō)明超高頻數(shù)據(jù)包含更精確的市場(chǎng)信息,風(fēng)險(xiǎn)度量具有更高的準(zhǔn)確性與魯棒性。經(jīng)過(guò)檢驗(yàn)也表明,后者在無(wú)條件覆蓋性、條件覆蓋性上要占優(yōu),在獨(dú)立性檢驗(yàn)上兩者持平,總體上依然占優(yōu)。
[Abstract]:Under the background of the globalization of financial market and the prosperity of derivatives trading, a number of financial crisis incidents prompted the birth of VaR, which has become a standard measurement method of market risk and has attracted wide attention in the financial and academic circles. In this paper, liquidity risk is incorporated into the basic framework of VaR, and a Liquidity-Adjusted VaR model (La-VaR) is constructed based on high-frequency data and ultra-high-frequency data respectively. Finally, empirical research and comparative analysis are conducted.
One of the key tasks of this paper is to build a La-VaR model for high-frequency data. La-VaR modeling for high-frequency data is derived from the basic framework of BDSS model, but for its normal distribution assumption, the assumption of the same variance, the assumption that the relative price difference is not related to the intermediate price, the assumption of maximizing the synchronization of market risk and liquidity risk, the assumption of non-dynamic liquidity. Improvements such as defects can not only calculate individual assets, but also calculate La-VaR. of portfolios.
Specifically, the GJR-GARCH-EVT-kernel model is first constructed to characterize the spike-tail, heteroscedasticity, volatility asymmetry and the distribution characteristics of the upper and lower tail extremes of the return series, and the multivariate Copula model is used to capture the correlation structure between different asset sequences. Then the Monte Carlo simulation is used to predict the return series in one step. Similarly, GJR-GARCH-EVT-kernel model is used to fit the marginal distribution of the relative price difference, and then the binary Archimedes Copula model is used to describe the correlation structure between the relative price difference and the yield in each single asset. Then the pseudo-random number of the relative price difference is generated based on the predicted yield sequence as its one-step prediction sequence. Three, through the prediction of multiple paths, we finally get the quantile of transaction price, and further calculate the La-VaR value.
Empirical analysis shows that VaR has a certain degree of underestimation of risk. The proportion of liquidity risk measured by La-VaR is generally 10%-20%, and the unconditional coverage, independence, conditional coverage tests show that La-VaR basically does not exist the phenomenon of underestimation of risk, which is obviously better than VaR model.
The second core work of this paper is to build a La-VaR model based on ultra-high frequency data. The La-VaR model based on high frequency data is actually to model the same time interval sequence, but ultra-high frequency data is essentially different from the traditional time series, that is, non-equal time interval, so it can not be directly applied to the traditional time series. In column model, we need to introduce duration sequence, and transform the unequal interval yield and relative price difference to model again.
Specifically, a WACD model is established to fit the duration sequence and iteratively predict the high frequency interval (such as 1 minute) from the sum of the multi-step duration to the cumulative duration. Secondly, the UHF data sequence is converted by the duration to obtain the unit yield and the unit relative price difference to satisfy the equal time interval, and then it is incorporated into the model. In the GJR-GARCH-EVT-kernel-Copula framework, the steps of parameter estimation and multi-step prediction are the same as those of duration prediction. Thirdly, the multi-step forecast of unit yield and unit relative price difference is converted into multi-step forecast of fractional yield and relative price difference by duration conversion, and the high frequency interval of prediction is obtained by aggregation. Yield and Relative Price Spread. Finally, the quantiles of the transaction price are predicted through multiple paths and the La-VaR value is calculated.
Empirical research can not only draw a conclusion similar to the calculation of La-VaR based on high-frequency data, but also significantly underestimate the risk of VaR measurement for assets with poor liquidity. La-VaR based on ultra-high-frequency data can accurately reflect the liquidity risk without underestimation. It is found that the number of VaR and La-VaR failure time nodes of the latter is closer to the theoretical value and the fluctuation range is smaller, which indicates that the UHF data contains more accurate market information and the risk measurement has higher accuracy and robustness. Ping, overall, is still dominant.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224

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