C4烃在甲乙酮溶剂体系中的相对挥发度

用静态法测定不同温度下正丁烷，丁烯－1，顺丁烯－2与甲乙酮溶剂体系的压力－组成数据，并与纯C4烃的饱和蒸汽压曲线比较，可以看出，甲乙酮溶剂改变了正丁烷与丁烯之间的相对挥发度，确定了萃取精馏分离丁烯与丁烷的关键组分为正丁烷与丁烯－1，其相对挥发度为1．1－1．2。     

中国股市多分形波动率建模及预测研究

本文考虑股市高频数据的日内效应和已实现波动率的测量误差修正了现有多分形波动率指标的构建方法，以HAR模型为基础构建新的多分形波动率预测模型.利用Diebold-Mariano检验和"模型信度设定"检验等方法综合评价了各种模型对我国沪深300股指的预测能力.结果表明：1）在相同模型范式下，赋权调整已实现波动率的样本外预测能力要优于已实现波动率，而本文提出的新的多分形波动率模型要显著优于其他模型；2）在相同波动率测度指标下，引入股价波动的跳跃成分和杠杆效应能进一步改善波动率模型的短期预测效果；3）最优和次优模型均是基于新多分形波动率方法构建的模型.     

已实现波动测度的有效性及实证分析

建立在高频金融时间序列基础上的已实现波动测度是资产价格过程中隐含波动的一致估计量,证明了已实现双幂变差波动测度是比已实现波动更有效的波动估计量,并利用中国股票市场的高频数据进行了实证分析,实证结果与理论分析相一致.     

人民币汇率波动特征及其对出口影响的研究

选取1995年1月3日至2012年11月2日美元兑人民币的日汇率数据,基于Eviews软件,建立条件异方差的GARCH(1,1)模型。结果表明:人民币汇率升值所受的冲击是持久的,对中国经济产生较大的影响。随后研究了人民币汇率的波动对中国进出口10大类商品的影响,建立了SVAR模型,结果表明:汇率波动对动植物油、脂及动植物蜡,非食用原料,矿物燃料润滑油及其有关原料,化学成品及其有关产品和机械及运输设备这5类商品的出口有较大影响,对另外4种商品的出口有一定的抑制作用,对未分类的商品出口还存在一定程度的促进作用。     

信息不对称框架下资产均衡定价模型分析

为揭示上市公司信息供给与公司资本成本之间的内在关联,从投资者的角度出发,考虑信息不对称情形下建立效用最大化模型求解资产均衡市场价格和公司资本成本.基于上述模型,分析了信息不对称情形下的资本成本的决定机制.通过分析,得出的主要结论为:公司资本成本和信息精度、信息的传播范围成反比,即信息的精度越大、传播范围越广,公司资本成本越小.所以上市公司为了降低资本成本应该全面充分的披露信息.     

基于跳扩散模型的石油价格长期趋势分析

分析了国际石油市场1986至2012年周价格形成机制的长期演变趋势.在讨论均衡理论基础上,以长期市场供求关系解释了国际油价长期波动现象.基于跳扩散模型拟合石油价格动态过程,利用结构变点检验和累积量估计方法进行了实证研究.历史数据分析表明石油价格具有高波动性、高强度跳跃性和上升漂移特征.此外,模型预测即使当前大幅增加石油投资,未来几年内石油价格变化仍会处于一种高频跳跃的上行阶段.     

Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting.     

Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility

This paper considers how information from the implied volatility (IV) term structure can be harnessed to improve stock return volatility forecasting within the state-of-the-art HAR model. Factors are extracted from the IV term structure and included as exogenous variables in the HAR framework. We found that including slope and curvature factors leads to significant forecast improvements over the HAR benchmark at a range of forecast horizons, compared with the standard HAR model and HAR model with VIX as IV information set.     

中国股市正反馈交易涨强不对称的定价能力

中国市场存在显著的正反馈交易,且追涨程度远超过杀跌程度.这种现象本文称之为正反馈交易的涨强不对称.本文旨在研究这种涨强不对称是否具有定价能力.本文在Fama-French三因子模型的基础上构建了反转因子、正反馈因子和涨强不对称因子,对2010年以前上市的全部A股从1998年1月至2016年10月的数据进行实证检验.本文发现,涨强不对称因子的表现显著区别于正反馈因子和反转因子;尽管单一来看正反馈因子、反转因子和涨强不对称因子都有一定的定价能力,但在多因子模型中正反馈因子和反转因子的定价能力很弱,只有涨强不对称因子有显著的定价效果;且这种定价能力不是因为追涨、杀跌、流动性溢价或投资者情绪造成的.总之,涨强不对称是一个有别于传统因子的新定价因子,且其定价能力可能源于市场补偿非理性投机带来的风险.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.