SiCp/A1FGM微观组织对冲击韧性的影响

功能梯度材料具有良好的力学性能，有广泛的应用前景。通过观测SiCp/AI功能梯度复合材料的微观组织，并对增强体尺寸分别为5、7、10μm的SiCp/AI功能梯度复合材料的冲击韧性进行了测试。结果表明：显微组织显示各梯度层之间界面模糊，粘结良好，没有明显缺陷；5μmSiCp/A1FGM的冲击韧性较好；在合理的增强体尺寸范围内，颗粒尺寸越小，材料冲击韧性越好。     

On Estimating the Integrated Co-Volatility Using Noisy High-Frequency Data with Jumps

In this article, we consider the estimation of covariation of two asset prices which contain jumps and microstructure noise, based on high-frequency data. We propose a realized covariance estimator, which combines pre-averaging method to remove the microstructure noise and the threshold method to reduce the jumps effect. The asymptotic properties, such as consistency and asymptotic normality, are investigated. The estimator allows very general structure of jumps, for example, infinity activity or even infinity variation. Simulation is also included to illustrate the performance of the proposed procedure.     

Nonparametric estimation of jump characteristics under market microstructure noise

This article proposes a simple nonparametric method to estimate the jump characteristics in asset price with noisy high-frequency data. We combine the pre-averaging approach and the threshold technique to identify the jumps, and then propose the pre-averaging threshold estimators for the number and sizes of jumps occurred. We further present the asymptotic properties of the proposed estimators. The Monte Carlo simulation shows that the estimators are robust to microstructure noise and work very well especially when the data frequency is ultra-high. Finally, an empirical example further demonstrates the power of the proposed method.     

股权控制权市场公司治理研究述评——微观结构视角

控制权市场微观结构视角的公司治理理论作为对传统企业理论研究的深化,借助信息经济学和博弈论工具对主流经济学分析框架进行了拓展,针对公司控制权市场交易、配置和调整的过程细节,准确描述了股份公司中所有权和控制权分离情形下股东内部控制、要约收购、接管竞标等不同控制和治理机制的实际运作,对信息不完全和存在交易成本条件下的控制权市场交易展开分析,从控制权市场微观结构视角阐明了股东对管理者实施有效控制的机制、策略和条件。股权控制权市场的行为金融问题、股权控制权市场与环境因素之间的交互作用问题和股权控制权市场中的法律保护与管制政策设计问题将是未来该领域理论研究关注的焦点。     

The Relationship between the Volatility of Returns and the Number of Jumps in Financial Markets

We propose a methodology to employ high frequency financial data to obtain estimates of volatility of log-prices which are not affected by microstructure noise and Lévy jumps. We introduce the “number of jumps” as a variable to explain and predict volatility and show that the number of jumps in SPY prices is an important variable to explain the daily volatility of the SPY log-returns, has more explanatory power than other variables (e.g., high and low, open and close), and has a similar explanatory power to that of the VIX. Finally, the number of jumps is very useful to forecast volatility and contains information that is not impounded in the VIX.     

Faster Convergence to the Estimation of Quadratic Variation with Microstructure Noise

The continuous quadratic variation of asset return plays a critical role for high-frequency trading. However, the microstructure noise could bias the estimation of the continuous quadratic variation. Zhang et al. (2005 Zhang, L., Mykland, P., Ait-Sahalia, Y. (2005). A tale of two time scales: determining integrated volatility with noisy high-frequency data. J. Amer. Statist. Assoc. 100(472):1394–1411.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) proposed a batch estimator for the continuous quadratic variation of high-frequency data in the presence of microstructure noise. It gives the estimates after all the data arrive. This article proposes a recursive version of their estimator that outputs variation estimates as the data arrive. Our estimator gives excellent estimates well before all the data arrive. Both real high-frequency futures data and simulation data confirm the performance of the recursive estimator.     

MIDAS Regressions: Further Results and New Directions

We explore mixed data sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider applications in macroeconomics and finance, among other areas. The regressions combine recent developments regarding estimation of volatility and a not-so-recent literature on distributed lag models. We study various lag structures to parameterize parsimoniously the regressions and relate them to existing models. We also propose several new extensions of the MIDAS framework. The paper concludes with an empirical section where we provide further evidence and new results on the risk-return trade-off. We also report empirical evidence on microstructure noise and volatility forecasting.