基于中國股市高頻數據的流動性風險研究
發(fā)布時間：2018-09-04 17:08
【摘要】：在金融市場全球化與衍生品交易不斷繁榮的背景下,多起金融危機事件的發(fā)生促使了VaR的誕生,它已成為市場風險的標準計量方法,在金融界與學術界廣受關注。雖然VaR具有綜合性、量化性、通俗性等優(yōu)點,但仍然存在諸多缺陷,其中之一即為假設投資組合不論頭寸大小都能以當前市場價格瞬間出清,從而忽略了流動性風險。本文將流動性風險納入VaR基本框架,并且分別基于高頻數據與超高頻數據構建流動性調整的VaR模型(La-VaR),最后作實證研究與對比分析。 本文的核心工作之一是針對高頻數據建立La-VaR模型。對高頻數據的La-VaR建模是源于BDSS模型基本框架,但是針對其正態(tài)分布假設、同方差假設、相對價差與中間價格不相關假設、市場風險與流動性風險同步最大化假設、流動性非動態(tài)假設等缺陷作出了改進,不僅可計算單個資產,還可計算投資組合的La-VaR。 具體地,首先構建GJR-GARCH-EVT-kernel模型刻畫收益率序列的尖峰厚尾性、異方差性、波動非對稱性以及上下尾極值分布特點,并采用多元Copula模型捕捉不同資產序列之間的相關結構,接著采用蒙特卡羅模擬對收益率序列進行一步預測。第二,類似地采用GJR-GARCH-EVT-kernel模型擬合相對價差的邊緣分布,然后分別在每個單一資產中,采用二元阿基米德Copula模型刻畫相對價差與收益率之間的相關結構,再基于預測的收益率序列生成相對價差的偽隨機數作為其一步預測序列。第三,通過多條路徑的預測最后得出交易價格的分位數,進一步便求出La-VaR值。 實證分析表明VaR存在一定程度上的風險低估,La-VaR計量的風險中流動性風險比例一般為10%-20%,并且經無條件覆蓋性、獨立性、條件覆蓋性等檢驗,La-VaR基本上不存在風險低估現象,要明顯優(yōu)于VaR模型。 本文的第二個核心工作是基于超高頻數據建立La-VaR模型。前述基于高頻數據的La-VaR模型實際上是對相等時間間隔序列進行建模,但是超高頻數據與傳統(tǒng)時間序列相比存在著本質區(qū)別,即非等時間間隔性,因此無法直接應用傳統(tǒng)時間序列模型,需要引入持續(xù)期序列,并對非等時間間隔的收益率與相對價差進行轉換后再建模。 具體地,首先建立WACD模型擬合持續(xù)期序列,并迭代預測多步持續(xù)期直至累計持續(xù)期之和達到高頻時間間隔(如1分鐘)。第二,采用持續(xù)期對超高頻數據序列進行轉換后得到單位收益率與單位相對價差以滿足相等時間間隔性,再將其納入前述的GJR-GARCH-EVT-kernel-Copula框架中進行參數估計與多步預測,其步數與持續(xù)期的預測步數相同。第三,將多步預測的單位收益率與單位相對價差經持續(xù)期轉換得到多步預測的分筆收益率與相對價差,再聚合得到預測的高頻時間間隔(如1分鐘)的收益率與相對價差。最后,通過多條路徑的預測得出交易價格的分位數進而求出La-VaR值。 實證研究不僅能得出類似基于高頻數據計算La-VaR的結論,而且對于流動性較差的資產,VaR度量的風險存在顯著的低估現象,而基于超高頻數據的La-VaR能較準確地反映流動性風險,不會產生低估。對比基于高頻與超高頻數據的計算結果后發(fā)現：后者的VaR、La-VaR失敗時間節(jié)點數與理論值更接近、波動范圍更小,說明超高頻數據包含更精確的市場信息,風險度量具有更高的準確性與魯棒性。經過檢驗也表明,后者在無條件覆蓋性、條件覆蓋性上要占優(yōu),在獨立性檢驗上兩者持平,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.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.51;F224
本文編號：2222810
[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.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.51;F224
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