01讲座题目

A Robust Variance Bound on Stochastic Discount Factors Under Model Uncertainty

02报告人

贾越珵

03内容摘要

This paper examines how model uncertainty affects the tightness of variance bounds. We define model uncertainty as the uncertainty induced by the representative investor’s use of different forecasting model specifications, where each specification implies a distinct variance bound. Empirically, we show that pooling these models together generates a wide band of variance bounds, with each bound representing a different opinion on model-implied pricing kernels. We then prove in theory and in model simulations that (i) a higher degree of model uncertainty leads to a wider band of bounds, and (ii) a Bayesian average of all bounds, which incorporates the impact of model uncertainty, always lies within this band. We apply this Bayesian-average bound to evaluate the performance of three leading classes of asset-pricing models and find that the long-run-risk rare-disaster model of Nakamura, Steinsson, Barro, and Ursua (2013) performs best.

04报告人简介

贾越珵，永利集团官网中国金融发展研究院副教授。主要研究领域包括资产定价，深度学习，大宗商品，和量化投资实践。学术论文发表于Journal of Banking and Finance (3篇), Journal of Empirical Finance，European Financial Management (2篇), Pacific-Basin Finance Journal等国际权威刊物。另有多篇论文在国际顶级和权威期刊送审后修订并重新提交。2018年以来深耕量化投资领域，在头部量化私募任核心投研与股票量化策略投资经理，管理多只私募产品。精通Python和C++编程在量化投资实践中的应用。

05时间

12月3日（周三）中午12：30-13:30

06地点

学院南路校区学术会堂（南楼）608

撰稿：邱南沙

审稿：汪雪菲

编辑：沈嘉怡

审核：王颖