宏观基本面中的稀疏成分与股市波动率预测  

Sparse Components in Macro Fundamentals andthe Prediction of Stock Market Volatility

作　　者：李伯龙 LI Bolong(School of Finance,Nankai University,Tianjin 300350,China)

机构地区：[1]南开大学金融学院,天津300350

出　　处：《运筹与管理》2023年第9期186-192,共7页Operations Research and Management Science

基　　金：国家留学基金项目(201906200008)。

摘　　要：利用滚动窗口与规则化回归的方法比较了我国宏观经济基本面中稀疏特征与稀疏因子对股市波动的预测作用,依据回归与预测结果分析稀疏成分预测波动率的机制。研究发现:稀疏因子预测波动率的精度较稀疏特征更高,稀疏特征预测方程包含的更多变量增大了预测方差;预测精度时变性较强且与股市波动负相关,表明引起我国股市震荡的因素一定程度上独立于基本面信息;稀疏特征与因子预测波动率的模式不同,特征预测中市盈率与商品房销售面积增长对波动率预测作用较强,因子预测中波动率自回归项预测作用显著,因子主要起到补充作用。本文研究结论能够为金融风险的防控提供参考。Financial volatility is one of the most fundamental issues in both academic research and market practice.It provides valuable reference for participants in investing,hedging and arbitraging.With its great importance,understanding its dynamics becomes challenging,especially after the financial crisis in 2008,which changes the public’s perception and expectation about financial market substantially,and which still has an influence on today’s global economy.The development of data science in recent years can serve methods of analyzing asset volatility in complex situations.In this paper,we investigate the classic volatility forecasting problem in a data-rich environment,focusing on the roles of sparse components in macro fundamentals on determining future stock market volatility.The analysis can not only show us the relative performance of predictors in different sparse forms,but also provide us with access to learning the dynamics of stock volatility with respect to the macroeconomic environment.Formally,the“sparse components”in this paper refer to the subsets of predictors extracted from a large set of macro variables.There are two kinds of sparse components under consideration depending on the extracting methods:The“sparse characteristics”are the predictors selected through linear shrinkage techniques,whereas the“sparse factors”are latent factors extracted from the macro variables using principal component analysis.To select the sparse characteristics,regularized regressions with the smoothly clipped absolute deviation(SCAD)penalization are employed.Robustness checks based on the least absolute shrinkage selection operator(LASSO)are also presented.These L1 regularization terms can reserve only the most relevant predictors in predictive regressions while eliminating irrelevant variables from the predicting process.Though the latent factors are linear combinations of all the macro variables,they are able to summarize a sufficient large proportion of variation in these variables and thus appear in th

关 键 词：股市波动预测 稀疏特征 稀疏因子 滚动窗口回归 规则化回归 

分 类 号：F832.5[经济管理—金融学]