跳跃风险、结构突变与原油期货价格波动预测

本文主要是为了检验原油期货市场是否存在明显的跳跃风险和结构突变,并重点调查这两个因素是否对原油期货价格波动有预测作用。在经典或前沿的HAR-RV、HAR-S-RV和PSlev模型中,本文同时考虑跳跃风险和结构突变因素,构建了HAR-RV-J-SB、HAR-S-RV-J-SB和PSlev-J-SB模型。接着,以WTI原油期货的5分钟高频交易数据作为实证样本,对以上模型进行实证分析。实证结果显示：原油期货市场存在明显的跳跃风险和结构突变现象;HAR-RV-J-SB、HAR-S-RV-J-SB和PSlev-J-SB模型对原油期货价格波动的样本外预测精度都明显高于与之相对应的HAR-RV、HAR-S-RV和PSlev模型,且其结果是稳健的。特别地,在HAR-C和LHAR-RV等其它现有HAR族模型中加入跳跃风险和结构突变因素,也能得到类似的结果。本文的研究表明跳跃风险和结构突变因素能显著提高现有绝大多数HAR族模型对原油期货价格的预测精度,所以在HAR族模型的构建中这两个因素不能被忽视。     

The impact of jumps and leverage in forecasting covolatility

ABSTRACT

The paper investigates the impact of jumps in forecasting covolatility, accommodating leverage effects. We modify the preaveraged truncated covariance estimator of Koike (2016 Koike, Y. (2016). Estimation of integrated covariances in the simultaneous presence of non-synchronicity, microstructure noise and jumps. Econometric Theory 32:533–611.[Crossref], [Web of Science ®] , [Google Scholar]) such that the estimated matrix is positive definite. Using this approach, we can disentangle the estimates of the integrated covolatility matrix and jump variations from the quadratic covariation matrix. Empirical results for three stocks traded on the New York Stock Exchange indicate that the cojumps of two assets have a significant impact on future covolatility, but the impact is negligible for forecasting weekly and monthly horizons.     

Bayesian quantile regression and variable selection for partial linear single-index model: Using free knot spline

Partial linear single-index model (PLSIM) has both the flexibility of nonparametric treatment and interpretability of linear term, yet existing literatures about it mainly focused on mean regression, and quantile regression analysis is scarce. Based on free knot spline approximation, we apply asymmetric Laplace distribution to implement Bayesian quantile regression, and perform variable selection in linear term and index vector via binary indicators. Our approach is exempt from regularity conditions in frequentist method, and could execute variable selection and quantile regression under mutual posterior correction, which is also the first work to implement them jointly for PLSIM in fully Bayesian framework. The numerical simulation manifests the superiority of our approach to previous methods, which embodied in better efficiency of variable selection, index vector estimates and link function approximation with different error distributions. For illustration of its application, we build a power consumption model of A₂/O process in wastewater treatment and emphatically analyze the impact of water quality factors.     

Automatic Bayesian quantile regression curve fitting

Quantile regression, including median regression, as a more completed statistical model than mean regression, is now well known with its wide spread applications. Bayesian inference on quantile regression or Bayesian quantile regression has attracted much interest recently. Most of the existing researches in Bayesian quantile regression focus on parametric quantile regression, though there are discussions on different ways of modeling the model error by a parametric distribution named asymmetric Laplace distribution or by a nonparametric alternative named scale mixture asymmetric Laplace distribution. This paper discusses Bayesian inference for nonparametric quantile regression. This general approach fits quantile regression curves using piecewise polynomial functions with an unknown number of knots at unknown locations, all treated as parameters to be inferred through reversible jump Markov chain Monte Carlo (RJMCMC) of Green (Biometrika 82:711–732, 1995). Instead of drawing samples from the posterior, we use regression quantiles to create Markov chains for the estimation of the quantile curves. We also use approximate Bayesian factor in the inference. This method extends the work in automatic Bayesian mean curve fitting to quantile regression. Numerical results show that this Bayesian quantile smoothing technique is competitive with quantile regression/smoothing splines of He and Ng (Comput. Stat. 14:315–337, 1999) and P-splines (penalized splines) of Eilers and de Menezes (Bioinformatics 21(7):1146–1153, 2005).     

价格跳跃行为视角下中国金融期货开盘效应的实证研究

基于JV‐T OD非参数方法和高频数据识别中国股指、国债期货日内价格跳跃行为,证明其开盘效应的存在,根据市场预期理论改进HAR‐CJ模型分析开盘效应的内在机制,主要结论有：（1）中国金融期货市场日内开盘效应明显,开盘跳跃行为是价格日内波动的重要组成部分;（2）股指期货日内价格的午盘和盘中跳跃行为是其显著区别于国债期货的日内分布特征;（3）期现市场开盘共同发生“常规性”跳跃时,期货市场开盘效应对现货市场的引导作用明显;（4）市场前期波动行为可以在一定程度上解释交易日内开盘效应,但相较而言,跳跃变差比连续波动的贡献稍弱。据此,提出为机构投资者和市场监管者提供相关风险管理、优化市场结构的建议。     

成就目标定向理论在蹲踞式跳远教学中的实验研究

通过文献资料法、实验法、数理统计法等对宿州学院体育学院2010级本科生两组男学生进行教学实验,通过整理数据并对比后发现:在进行蹲踞式跳远教学中,实验组学生在技术、成绩上都明显优于对照组的学生.说明成就目标定向理论优于传统教学方法.     

女子跳远运动员起跳过程中摆动腿技术的研究

通过查阅有关文献资料,了解目前我国女子跳远助跑最后一步和起跳阶段有关参数的运动生物力学研究现状.主要对高水平女子跳远运动员的助跑最后一步和起跳过程中摆动腿的摆动作用及其相关因素进行分析与研究,找出我国女子跳远运动员在该技术上的差距及其原因,并对当今世界女子跳远摆动腿技术的发展趋势进行概括性的分析与总结.     

Bayesian dynamic models for survival data with a cure fraction

In this paper, we propose a new class of semi-parametric cure rate models. Specifically, we construct dynamic models for piecewise hazard functions over a finite partition of the time axis. Allowing the size of partition and the levels of baseline hazard to be random, our proposed models provide a great flexibility in controlling the degree of parametricity in the right tail of the survival distribution and the amount of correlations among the log-baseline hazard levels. Several properties of the proposed models are derived, and propriety of the implied posteriors with improper noninformative priors for regression coefficients based on the proposed models is established for the fixed partition of the time axis. In addition, an efficient reversible jump computational algorithm is developed for carrying out posterior computation. A real data set from a melanoma clinical trial is analyzed in detail to further demonstrate the proposed methodology.     

Bayesian model selection for D‐vine pair‐copula constructions

In recent years analyses of dependence structures using copulas have become more popular than the standard correlation analysis. Starting from Aas et al. ( 2009 ) regular vine pair‐copula constructions (PCCs) are considered the most flexible class of multivariate copulas. PCCs are involved objects but (conditional) independence present in data can simplify and reduce them significantly. In this paper the authors detect (conditional) independence in a particular vine PCC model based on bivariate t copulas by deriving and implementing a reversible jump Markov chain Monte Carlo algorithm. However, the methodology is general and can be extended to any regular vine PCC and to all known bivariate copula families. The proposed approach considers model selection and estimation problems for PCCs simultaneously. The effectiveness of the developed algorithm is shown in simulations and its usefulness is illustrated in two real data applications. The Canadian Journal of Statistics 39: 239–258; 2011 © 2011 Statistical Society of Canada     

基于股价跳跃限制的高管股票期权激励定价研究

如何合理地评估高管股票期权的价值仍然是业界和学界的难题之一。在传统的基于BS期权定价模型的基础上，考虑我国股票市场的涨跌停限制的制度设计，运用三点概率分布将这一制度特征刻画在股价跳跃过程之中，提出了引入跳跃限制的股权价值定价模型。以实施股权激励计划公司为训练样本并对未来期权价值进行模拟，模拟结果表明，中国证券市场的涨跌停制度对于高管股票期权价值是有影响的，增加股价跳跃限制的定价模型降低了传统BS模型对股权价值的高估程度，这将有利于进一步优化高管股票期权激励的定价模型。