资产组合非等间隔日内在险价值研究

当前对资产组合在险价值(VaR)的研究仅限于等间隔抽样数据的建模。本文提出资产组合的非等间隔日内在险价值(Irregularly Spaced Intraday Value at Risk,ISIVaR)研究方法,克服资产组合逐笔交易数据非等间隔且不同步问题,利用逐笔交易数据所包含的丰富市场微观结构信息对VaR进行估计。该方法基于更新时间方法将非同步的资产组合标值序列同步化;运用Copula理论建立资产组合的非等间隔日内波动率模型,并捕捉资产组合中各资产在截面上的相关关系;最后利用这种截面相关关系,使用蒙特卡洛模拟技术估计出资产组合的ISIVaR。实证部分利用真实的逐笔交易数据验证了上述方法的有效性。     

中美股市微观结构中的非对称效应分析

日收益数据的采用和研究必然会损失部分日内信息.本文尝试使用中美股市日内分笔超高频数据,通过非对称ACD模型对交易间隔等日内信息建模,结果显示收益率的上升会导致交易持续时间延长,这也是当前市场上“追涨杀跌”心理的一种表现.从三地系数值大小可以看出,这种现象在中国股票市场尤为显著.非对称模型一定程度上解释了公众心理导致了非对称效应的存在这一现象.     

我国股指期货市场与股票市场风险传导机理研究——基于双变量CAViaR方法

本文基于日内和日度数据使用双变量CAViaR模型估计了沪深300股指期货和标的指数的风险价值,并基于沪深300股指期货市场日内交易行为角度分析了股指期货和现货市场间日内与日间风险传导作用的差异。结果发现,期货市场日内风险的自相关特征显著高于现货市场,而两个市场的日间自相关性相差甚小,期货市场向现货市场的单向风险溢出效应仅体现在日内信息的传递过程中。与现货市场相比,期货交易的杠杆性以及当日回转交易便利了日内投机、套利交易,因而期货市场日内信息传递效率更高。     

基于ARMA-GARCH-SN模型的沪深300股指期货日内波动率研究与预测

运用五个交易日的股指期货高频数据(每秒两笔),本文主要研究了沪深300股指期货日内波动率特征并对日内波动率预测。研究发现高频股指期货日内收益率有明显的波动率聚集和条件异方差现象,但无尖峰厚尾现象,收益率序列分布符合有偏正态分布。因此,我们对时间序列建立了最优的ARMA-GARCH-SN模型,并对模型拟合充分性做了验证,拟合结果发现ARMA(1,2)-GARCH(1,1)-SN模型基本能够刻画股指期货高频日内波动特征,条件方差所受的冲击具有很强的持续性、日内波动也具有长记忆性,最后我们还利用自助法对高频股指期货日内波动率两步预测、利用滚动回归预测方法对样本做了样本内预测。预测结果表明,波动率预测结果能够较好地反映股指期货日内波动特征。     

期货市场日内VaR测度模型与应用

本文构建了两类日内VaR测度模型,一类是以超高频数据为基础,结合久期模型、波动模型和Monte Carlo模拟方法的综合日内VaR(IVaR)测度模型,另一类是以等时间间隔高频数据为基础并结合传统计量方法(历史模拟法与GARCH法)的日内VaR测度模型。然后运用上海燃料油期货市场数据进行了实证研究,结果表明:相对于传统计量方法,IVaR模型由于包含了更充分的市场信息,因而无论是多头头寸还是空头头寸时都具有更好的预测能力;IVaR方法估计的VaR值最小,说明IVaR模型比较适用于风险承受能力较强的投资者;IVaR模型对于空头头寸的管理更加严格;另外,IVaR模型的预测结果表明市场在日内具有开盘大,随后迅速衰减并趋于稳定的特征。     

中国新三板市场阶段性集合竞价制度的价格发现研究

市场微观结构理论表明交易机制对资产价格的形成过程具有重要影响。本文以中国新三板交易机制改革为背景,从理论上分析了阶段性集合竞价制度的市场出清过程。阶段性集合竞价制度的核心在于市场出清时间间隔的设定。本文构建了一个存在信息摩擦和知情交易者学习机制的集合竞价市场出清模型,讨论了市场出清时间间隔对价格发现效率、资产价值不确定性和流动性风险的影响。研究发现:(1)在完美信息条件下,如果对市场规模较大和价值波动率较高的资产设定较短的市场出清时间间隔,将会降低投资者的流动性风险,提升市场质量;(2)在不完美信息条件下,除市场规模和资产价值波动率之外,信息不对称程度和知情交易者比例也是影响最优市场出清频率的重要因素;(3)在不完美信息条件下,对价值波动率较低的资产缩短市场出清时间间隔才能降低流动性风险,这与完美信息条件下的结论相反。     

高频视角下中国股市动态VaR预测模型研究

如何充分挖掘交易数据中有价值的信息对金融风险管理极其重要,现有研究中基于低频波动模型的风险测度方法几乎已经做到了极致,而能达到的预测效果却并不稳健,对高频波动模型的研究相对比较匮乏。那么高频模型能否从高频数据中挖掘出更有价值的信息以便用于风险管理之中呢？本研究通过建立12个低频和9个高频波动模型对上证综指进行样本外动态VaR的滚动预测发现,高频模型相对于低频模型具有更好的稳定性,并且在多数情况下高频模型优于低频模型;多头与空头的风险预测效果具有显著差异,多头风险在高风险情况下高频模型表现出色,低风险情况下并不理想,空头风险则在所有情况下都表现较好。     

基于MRS-GARCH模型的中国股市波动率估计与预测

基于误差项服从正态分布、t分布、广义误差分布的GARCH族模型和MRS-GARCH模型对中国股市波动的结构变化特征进行了实证研究。结果表明,中国股市存在显著的高、低波动状态,两种波动状态的ARCH和GARCH项系数存在较大差异;高、低波动状态均具有较长的持续时间,低波动状态的持续时间长于高波动状态的持续时间,且中国股市更易于从高波动状态转向低波动状态;MRS-GARCH模型预测效果总体上优于GARCH族模型,基于正态分布的MRS-GARCH模型短期预测效果较好。     

经济政策不确定性与短期利率波动——基于BHK-L-MIDAS模型的实证研究

本文在BHK模型的基础上，考虑短期利率波动杠杆效应和EPU对短期利率波动的影响，构建了一个包含杠杆效应和EPU的混频短期利率模型，即BHK-L-MIDAS模型对短期利率波动进行建模与预测。采用上海银行间同业拆借利率(SHIBOR)和中国EPU指数数据对构建的BHK-L-MIDAS模型进行实证分析，结果表明：短期利率波动存在“反向杠杆效应”;EPU对短期利率波动具有显著负向的影响；BHK-L-MIDAS模型相较于竞争模型(BHK和BHK-MIDAS模型)获得了更好的样本内数据拟合效果。基于三种损失函数以及模型置信集(MCS)检验对模型样本外短期利率波动预测能力的分析表明：BHK-L-MIDAS模型相比BHK模型和BHK-MIDAS模型具有更高的样本外预测精度，且BHK-L-MIDAS模型在不同预测窗口表现出的预测能力具有稳健性。最后，VaR分析表明BHK-L-MIDAS模型在短期利率市场风险度量方面的经济价值。     

基于ARMA-GARCH模型的恒指隐含波动率指数预测及其在期权交易中的应用

基于ARMA-GARCH模型,并结合均值回归效应,溢出效应和周内效应,本文研究了恒指隐含波动率指数(VHSI)能否被预测及预测是否有助于期权投资实践的问题.研究结果验证了香港股市具有均值回归的特性,标准普尔500指数对恒指隐含波动率指数有明显的溢出效应.此外,恒指隐含波动率指数呈现出周一上涨,周五下跌的特征,具有明显的周内效应.最后,本文运用ARMA-GARCH模型对恒指隐含波动率指数进行预测,并结合实际的市场数据做了期权交易模拟.结果显示,ARMA-GARCH模型比ARMA模型更适合对恒指隐含波动率进行建模;考虑了均值回归效应,溢出效应和周内效应之后,ARMAGARCH模型对恒指隐含波动率指数的预测能力显著提高,并且预测结果有助于期权交易获得较好的收益.     

Mixed effect models for absolute log returns of ultra high frequency data

Considering absolute log returns as a proxy for stochastic volatility, the influence of explanatory variables on absolute log returns of ultra high frequency data is analysed. The irregular time structure and time dependency of the data is captured by utilizing a continuous time ARMA(p,q) process. In particular, we propose a mixed effect model class for the absolute log returns. Explanatory variable information is used to model the fixed effects, whereas the error is decomposed in a non‐negative Lévy driven continuous time ARMA(p,q) process and a market microstructure noise component. The parameters are estimated in a state space approach. In a small simulation study the performance of the estimators is investigated. We apply our model to IBM trade data and quantify the influence of bid‐ask spread and duration on a daily basis. To verify the correlation in irregularly spaced data we use the variogram, known from spatial statistics. Copyright © 2006 John Wiley & Sons, Ltd.     

Estimation of integrated volatility in stochastic volatility models

In the framework of stochastic volatility models we examine estimators for the integrated volatility based on the pth power variation (i.e. the sum of pth absolute powers of the log‐returns). We derive consistency and distributional results for the estimators given high‐frequency data, especially taking into account what kind of process we may add to our model without affecting the estimate of the integrated volatility. This may on the one hand be interpreted as a possible flexibility in modelling, for example adding jumps or even leaving the framework of semimartingales by adding a fractional Brownian motion, or on the other hand as robustness against model misspecification. We will discuss possible choices of p under different model assumptions and irregularly spaced data. Copyright © 2005 John Wiley & Sons, Ltd.     

基于M-LSTM的股票指数日内交易量分布预测研究

针对现有预测建模方法难以高效提取日内交易量分布复杂变化规律,影响VWAP策略执行效果的问题,本文提出一种MEMD分解下基于LSTM-Attention的股市指数日内交易量分布预测方法M-LSTM。首先,运用MEMD方法将区间多维交易量时序同步分解为若干个独立的本征模态函数(IMF);其次,对各维度分解中高频IMF进行去噪与重构,构建基于LSTM-Attention神经网络的日内交易量分布预测模型,并深入分析股票指数不同走势阶段下模型预测的有效性;最后,分别采用M-LSTM、ARIMA以及SVR等主流方法,对上证指数等四个代表性指数的日内交易量分布进行预测。实验结果表明:M-LSTM预测误差更小,是一种更有效的股票指数日内交易量分布预测方法。     

基于机器学习的智能TWAP和VWAP算法的研究及应用

TWAP与VWAP算法为两类较常见的经典交易算法.传统的VWAP算法在TWAP算法的基础上,大多使用预测日内成交量分布的方法指导算法下单.传统成交量分布的预测效果严重依赖于市场交易惯性,但交易量分布受到日内诸多突发因素的影响,导致算法对市场突发状况的应对能力较弱.本文对传统TWAP与VWAP算法进行改进,利用滚动的1分钟粒度高频实时资金博弈数据,基于Logistic分类器训练量价模型,以该预测结果为入参构建最优化期望执行均价模型,求出当下各个价格档位对应委托数量的最优解.通过相对高频的分钟级价格预测机制,保证算法实时跟踪市场行情走势并寻求相对优势的交易机会.该算法经测试可以稳定地跑赢市场均价,具备推广应用的可行性.     

Concentrated Equilibrium and Intraday Patterns in Financial Markets

Abstract

We introduce endogenous participation of market makers into a Kyle-type model with long-lived asymmetric information. In our model with plausible parameter values, the trading volume and price volatility show a U-shaped intraday pattern, often observed in actual financial markets. It will be shown that the pattern is caused not only by the trading behaviour of liquidity traders but also by that of market makers. Our findings shed new light on the stylized fact of the trade concentration at the opening and closing periods.     

Estimating Value-at-Risk and Expected Shortfall using the intraday low and range data

Value-at-Risk (VaR) is a popular measure of market risk. To convey information regarding potential exceedances beyond the VaR, Expected Shortfall (ES) has become the risk measure for trading book bank regulation. However, the estimation of VaR and ES is challenging, as it requires the estimation of the tail behaviour of daily returns. In this paper, we take advantage of recent research that develops joint scoring functions for VaR and ES. Using these functions, we present a novel approach to estimating the two risk measures based on intraday data. We focus on the intraday range, which is the difference between the highest and lowest intraday log prices. In contrast to intraday observations, the intraday low and high are widely available for many financial assets. To alleviate the challenge of modelling extreme risk measures, we propose the use of the intraday low series. We draw on a theoretical result for Brownian motion to show that a quantile of the daily returns can be estimated as the product of a constant term and a less extreme quantile of the intraday low returns, which we define as the difference between the lowest log price of the day and the log closing price of the previous day. In view of this, we use estimates of the VaR and ES of the intraday low returns to estimate the VaR and ES of the daily returns. We provide empirical support for the new proposals using data for five stock indices and five individual stocks.     

Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting

Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data.