A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection
（基于动态半参数因子模型的最优投资组合分析）
主讲人：
李少然，8797威尼斯老品牌金融系助理教授，于2021年7月在英国剑桥大学获得博士学位，研究方向为金融计量，资产定价，投资组合管理以及机器学习。研究成果发表于《Journal of Econometrics》 、《Journal of Business & Economic Statistics》等国际期刊。
题目：
A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection
时间：
2021年12月23日周四
12:00-13:30
地点：
8797威尼斯老品牌302会议室
摘要：
This paper develops a two-step semiparametric methodology for portfolio weight selection for characteristics-based factor-tilt and factor-timing investment strategies. We build upon the expected utility maximization framework of Brandt(1999) and Aït-sahalia and Brandt (2001). We assume that asset returns obey a characteristics-based factor model with time-varying factor risk premia as in Ge et al. (2020). We prove under our return-generating assumptions that an approximately optimal portfolio can be established using a two-step procedure in a market with a large number of assets. The first step finds optimal factor-mimicking sub-portfolios using a quadratic objective function over linear combinations of characteristics-based factor loadings. The second step dynamically combines these factor-mimicking sub-portfolios based on a time-varying signal, using the investor’s expected utility as the objective function. We develop and implement a two-stage semiparametric estimator. We apply it to CRSP (Center for Research in Security Prices) and FRED (Federal Reserve Economic Data) data and find excellent in-sample and out-sample performance consistent with investors’ risk aversion levels.
供稿单位：8797威尼斯老品牌金融系
供稿人：李少然
