谈“言不尽意”及其方法论意义

言、意关系问题是中国古代哲学的一个大焦点，道家在这个问题上的见解是深刻且富于创建性的。道家的“言不尽意”论经历了从老子经庄子到魏晋玄学的逐步展开、深化和明朗化的发展过程。“言不尽意”论在否定性的结论背后揭示了认识过程中的某些辩证特征，值得人们深入思考。“言不尽意”直接为道家的直觉方法和中国哲学的“负的方法”提供了认识论基础。     

GAS-SKST-F模型及其在高频多元波动率预测中的应用

假定日收益率服从多元有偏学生t分布、已实现协方差矩阵服从矩阵F分布，本文构建了一种新的得分驱动模型：GAS-SKST-F模型。在该有偏厚尾多元波动率模型中，我们基于广义自回归得分(GAS)模型的基本思想对收益率和已实现协方差矩阵进行联合动态设定，协方差矩阵的更新过程依赖于收益率分布和已实现协方差矩阵分布联合似然函数的得分函数。已实现协方差测度在协方差矩阵的更新过程中发挥了重要的作用。基于20支上证50成分股高频数据的实证分析研究结果显示，与GAS-N-Wishart模型和GAS-tF模型相比，无论样本内还是样本外，GAS-SKST-F模型有着更加良好的样本内估计和样本外预测能力。     

Inference in Arch and Garch Models with Heavy–Tailed Errors

ARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be particularly heavy tailed. However, little is known about properties of ARCH or GARCH models in the heavy–tailed setting, and no methods are available for approximating the distributions of parameter estimators there. In this paper we show that, for heavy–tailed errors, the asymptotic distributions of quasi–maximum likelihood parameter estimators in ARCH and GARCH models are nonnormal, and are particularly difficult to estimate directly using standard parametric methods. Standard bootstrap methods also fail to produce consistent estimators. To overcome these problems we develop percentile–t, subsample bootstrap approximations to estimator distributions. Studentizing is employed to approximate scale, and the subsample bootstrap is used to estimate shape. The good performance of this approach is demonstrated both theoretically and numerically.