讲座题目： Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?

主讲人：张耀杰，博士研究生，西南交通大学

讲座时间：2018年11月28日（周三）13：30-15：00

讲座地点：bat365中文官方网站大楼318室

邀请部门：金融学系、金融工程研究所

 

讲座概要：

In this paper, we use two prevailing shrinkage methods, the lasso and elastic net, to predict oil price returns with a large set of predictors. The out-of-sample results indicate that the lasso and elastic net models outperform a host of widely used competing models in terms of out-of-sample R-square and success ratio. In an asset allocation exercise, a mean-variance investor obtains positive and sizeable economic gains based on the return forecasts of the lasso and elastic net methods relative to both the benchmark forecasts and competing forecasts. We further investigate the source of predictability from a variable selection perspective. The lasso and elastic net methods are found to select powerful predictors and the ones that can provide complementary information. The OLS regression models based on the selected predictors also exhibit better out-of-sample performances than the competing models. In addition, our results are robust to various settings.

 

报告人简介：

张耀杰，男，浙江省嘉兴市人，现为西南交通大学经济管理学院博士研究生。博士研究生期间连续4年获得国家奖学金。主持过“服务科学与创新”四川省重点实验室项目“贷款保险定价模型研究”(2017.07-2018.06)、西南交通大学博士研究生创新基金项目“贷款保险定价模型及其应用研究”(2017.09-2018.10)，并参研多项国家级科研项目。主要从事金融学领域的研究，在金融工程、风险管理、金融计量、公司金融和金融预测等方面发表学术论文21篇，发表国际期刊论文的杂志包括Knowledge-Based Systems, Energy Economics, Economic Modelling, Physica A, The North American Journal of Economics and Finance, Applied Economics Letters等，国内期刊的杂志包括系统工程理论与实践，管理评论、运筹与管理、工业工程与管理和保险研究等。