风险的时间尺度--对中国股市的实证分析

在金融风险管理中，投资者常常采用不同时间间隔的交易数据来估算资产的风险．投资界在投资实践中经常使用时间间隔的平方根规则将短期风险转换成长期风险，这种缩放比例关系在高频收益率是独立同分布下是有效的．文章采用中国股票市场数据进行实证分析，结果表明：中国股票市场(与大多数其它市场一样)的日收益率是非独立同分布的，使用缩放比例关系进行风险转换是不适当的，可能会引导投资者得出错误的推断．     

中国期市收益率波动与交易量和持仓量关系的实证研究

用计量经济模型对我国的两个期货合约的收益率波动进行了多层次的实证研究．结果表明：交易量与收益率波动的关系是正相关，持仓量与收益率波动之间的关系是负相关；将交易量、持仓量分割为预期和未预期两部分，发现未预期部分对收益率波动有更大的影响，且未预期部分本身的正、负变动对收益率波动的作用程度是不对称的；大的持仓量能减缓收益率波动．     

钢铁渗硅层孔隙成因的探讨

根据金相显微镜、显微硬度计、X-射线衍射仪、离子微分析仪及精密膨胀仪的测试结果,认为渗硅层孔隙的成因,还与被渗材料中的非金属夹杂物和渗层中的内应力有关。由此,提高渗硅温度、延长渗硅保温时间,以及采用膨胀系数和渗层相近、无同素异构转变、体心立方晶格的金属材料作为被渗材料,可减少孔隙的数量和改善孔隙的形状。     

混合GARCH模型下股票市场跳跃形态分析

在跳跃扩散模型下,假定跳跃强度服从门限自回归模型(self-threshold autoregressive model,SETAR)以反映跳跃强度的结构性突变,并使用GARCH模型描述收益率波动扩散形态.以受波动率影响的跳跃强度控制跳跃行为发生概率,并以受跳跃行为影响的GARCH模型控制正常扩散过程,构建了SETARGARCH模型.以上证房地产指数为例,实证研究发现,股指存在门限效应,GARCH效应明显,跳跃突变发生的概率为35.21%.资产收益率总体方差中有较大的部分是由跳跃行为异常所引起.历史波动率直接影响未来跳跃强度预期,历史跳跃行为干扰加剧了当期收益率波动扩散,波动率扩散和跳跃行为具有双向反馈机制.     

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.     

基于小世界网络的协同人工股市模型

以小世界人际关系网络为基础,采用多主体方法构建了一个投资者之间相互交流和模仿的人工股市模型.模型中,羊群行为会造成投资者对信息过度反应,从而使市场表现为过度波动;而当投资者对股票的注意力也随股票价格的变动而改变时,市场涌现波动丛集、泡沫和崩溃等典型事实,并且这些异象和典型事实对系统的大小具有鲁棒性.     

我国贸易条件波动与经济增长关系的实证分析

运用协整及格兰杰因果关系检验,采用我国1981—2007年数据,对价格贸易条件的水平及波动与我国经济增长的关系进行了实证分析。结果表明:价格贸易条件恶化是经济增长的格兰杰原因,而经济增长不是价格贸易条件恶化的格兰杰原因,贸易条件波动的降低对我国经济增长有正面影响。消费率相对较低,投资增速过快,生产相对过剩,而出于就业与经济增长的压力,以汇率管制、出口退税等方式增加出口,正是我国在该时期贸易条件恶化、出口不断增加和经济快速增长现象同时并存的原因。因此我国应采取理顺贸易条件对消费、投资和储蓄的影响机制,使贸易条件的改善最终成为经济增长的动力。     

Sparse Bayesian vector autoregressions in huge dimensions

We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced-form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation-by-equation estimation. Second, we apply recently developed global–local shrinkage priors to the VAR coefficients to cure the curse of dimensionality. Third, we utilize recent innovations to sample efficiently from high-dimensional multivariate Gaussian distributions. This makes simulation-based fully Bayesian inference feasible when the dimensionality is large but the time series length is moderate. We demonstrate the merits of our approach in an extensive simulation study and apply the model to US macroeconomic data to evaluate its forecasting capabilities.     

基于需求预测的店中店模式决策

基于Yue等人的需求预测模型, 研究了产品替代度、市场波动以及渠道成员需求预测精度对店中店模式和 传统模式下渠道成员收益的影响, 并以此为基础说明了制造商在不同情况下应如何选择销售模式. 研究表 明: 第一, 在产品替代度较小时, 制造商和零售商均倾向于选择店中店这一销售模式, 反之, 则双方更倾 向于选择传统模式; 第二, 当市场波动较大时, 制造商更倾向于采用店中店模式, 反之, 则双方更倾向于 选择传统模式, 同时在一定的市场波动范围内, 采用店中店模式对渠道双方均有利; 第三, 店中店模式下 制造商的需求预测精度越高, 制造商越倾向于采用店中店模式, 且该需求预测提高到一定的程度后, 采用 店中店模式对渠道双方均有利.     

Using CAViaR Models with Implied Volatility for Value‐at‐Risk Estimation

This paper proposes value‐at risk (VaR) estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market's expectation of risk. Forecast‐combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models—a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residuals. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P 500 daily returns. Copyright © 2012 John Wiley & Sons, Ltd.