A novel UMIDAS-SVQR model with mixed frequency investor sentiment for predicting stock market volatility

作者：

Highlights：

• The UMIDAS-SVQR model with mixed frequency investor sentiment is developed.

• It models support vector quantile regression on mixed frequency data via UMIDAS.

• It can be estimated by solving a quadratic programming problem.

• It has been successfully applied to predict Chinese stock market volatility.

摘要

•The UMIDAS-SVQR model with mixed frequency investor sentiment is developed.•It models support vector quantile regression on mixed frequency data via UMIDAS.•It can be estimated by solving a quadratic programming problem.•It has been successfully applied to predict Chinese stock market volatility.

论文关键词：Mixed frequency data,Support vector quantile regression (SVQR),UMIDAS-SVQR,Stock market volatility,Investor sentiment

论文评审过程：Received 13 December 2018, Revised 30 April 2019, Accepted 30 April 2019, Available online 1 May 2019, Version of Record 4 May 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.066