Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

双重障碍物对开闸式异重流运动特性的影响

异重流现象广泛存在于自然环境和水利工程中。现实工况中，底床设置障碍物是抑制异重流侵袭的有效手段，因此研究障碍物对异重流运动特性的影响具有实际工程价值。采用开闸式异重流实验，对比不同障碍物间距及高度对异重流最大扩散高度、头部速度等方面的影响，从而得到双重障碍物最优工程布置方案；分析异重流在障碍物上下游区域的速度剖面及其越过障碍物时的涡度场；对比无障、单一障碍物、双重障碍物工况对异重流掺混系数的影响。结果表明，双重障碍物最优布置方案为第一障碍物高于第二障碍物，障碍物间距要尽可能大，但应保证异重流遇到第二障碍物之前未恢复典型头部形态；在双重障碍物上游区域，异重流速度剖面出现畸变，畸变范围大致与障碍物等高，主流速度衰减明显，在障碍物下游区域，完成形态重塑的异重流，其速度剖面均存在明显的壁面区和射流区；相对于无障和单一障碍物工况，流经双重障碍物的异重流厚度明显变小；越障前后，异重流掺混系数呈现“M”型分布，越过第二障碍物后异重流的掺混速率明显大于第一障碍物。结果可为防治异重流灾害及保证水利工程安全等领域提供参考。     

带跳的随机波动模型的参数估计

讨论了带跳的随机波动模型中的参数估计问题,假设跳过程服从双指数跳,波动项服从Heston模型.首先借助Lee-Myland方法识别跳跃部分,运用极大似然估计方法对跳跃部分的参数进行估计.然后将扩散部分离散化之后用极大似然估计方法对扩散项的参数进行估计.最后,利用上证综合指数2015-2018年的历史数据进行实证分析,实验结果表明使用该方法能有效估计带跳的随机波动率模型的参数.     

钯纳米催化剂的合成及其在温和条件下对芳香族肟、酮和醛的催化还原

以对苯二胺为配体合成非均相钯纳米粒子催化剂(Pd-NPs),该钯催化剂的合成步骤简单,常温、常压下,氢气作为氢源,以水作为溶剂即可还原各种芳香族肟、酮和醛生成相应胺和醇,多数产物都有优秀的产率,部分高达99%,此外,该催化剂具有良好的可持续利用工业应用前景.     

纯黏土与砂-黏土混合物渗透特性差异及机理分析

根据重塑纯黏土和砂-黏土混合物试样的室内固结试验结果,反演了渗透系数,探讨了纯黏土和砂-黏土混合物的渗透特性差异.试验结果表明:常用于评价纯黏土渗透性状的归一化指标e/eL(孔隙比与液限状态孔隙比的比值)能拓展运用于砂-黏土混合物中,但e/eL与渗透系数之间呈现非线性关系,这显著不同于纯黏土的线性关系,其本质原因在于混合物在压缩过程中形成了砂骨架.骨架形成前,纯黏土和砂-黏土混合物的e/eL与渗透系数的2条关系曲线吻合良好,而在骨架形成后,两者的关系曲线存在明显的差异;引入砂-黏土混合物四相体系,从黏土孔隙比和砂孔隙比的角度阐述了砂骨架的形成机制,且对两者渗透性状的差异性进行了详细的机理解释;最后给出了一种可以预测纯黏土和砂-黏土混合物渗透系数的经验公式.