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【学术报道】Asset selection based on high frequency Sharpe ratio

2021年10月28日下午,应www.1495.com数学科学学院统计与金融数学研究室邀请,复旦大学数据科学学院陈钊研究员为www.1495.com师生带来题为“Asset selection based on high frequency Sharpe ratio”的线上专题报告,数科院多位师生参加了本次报告会。

陈钊,复旦大学大数据学院青年研究员,2012年在中国科学技术大学获得博士学位。之后在美国普林斯顿大学,宾夕法尼亚州立大学从事博士后研究及研究型助理教授工作。科研成果发表在AoS, JASA, JoE,Statistica Sinica, Energy and buildings等期刊上。主要研究方向:高维统计推断,稳健回归,时间序列,非参数及半参数统计方法,以及将统计方法应用于建筑能源,生物信息,癌症研究等领域。

报告会开始,陈研究员分享了他近期的学术成果:基于高频sharpo比资产选择问题。陈研究员首先向大家介绍了资产选择问题的研究背景及他们成果的研究动机;然后在他们提出的sharpo比等框架下探讨基于高频数据投资组合的资产选择问题的方法及理论框架;最后通过模拟方法展示他们成果的有效性。潘教授对研究问题及解决过程进行了深入浅出的讲解,不但包括理论分析,并通过数值算例论证了方法的有效性。精彩的报告令在场师生收获颇丰。

最后的互动答疑环节,大家积极提出问题,陈研究员耐心地一一解答,此次线上报告在热烈的掌声中顺利结束。

Asset selection based on high frequency Sharpe ratio

SPEAKER:

Zhao Chen, Researcher Fellow,Associate Professor (Tenure-track), Ph.D. supervisor, Fudan University,

WHEN:

14:00, Oct. 28, 2021

WHERE:

Tencent meeting (ID: 126419298)

ABSTRACT: In portfolio choice problem, the classical Mean-Variance model in Markowitz (1952) relies heavily on the covariance structure among assets. As the number and types of assets increase rapidly, traditional methods to estimate the covariance matrix and its inverse suffer from the common issues in high or ultra-high dimensional analysis. To avoid the issue of estimating the covariance matrix with high or ultra-high dimensional data, we propose a fast procedure to reduce dimension based on a new risk/return measure constructed from intra-day high frequency data and select assets via Dependent Sure Explained Variability and Independence Screening (D-SEVIS). While most feature screening methods assume i.i.d. samples, by nature of our data, we make contribution to studying D-SEVIS for samples with serial correlation, specifically, for the stationaryα-mixing processes. Underα-mixing condition, we prove that D-SEVIS satisfies sure screening property and ranking consistency property. More importantly, with the assets selected through D-SEVIS, we will build a portfolio that earns more excess return compared with several existing portfolio allocation methods. We illustrate this advantage of our asset selection method with the real data from the stock market.

  • 更新时间

    2021年11月03日 16:01

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    数科院

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