一、报告题目

Machine learning high-dimensional volatilities over large financial systems

二、主讲人

宁宁

三、报告时间

2022年7月19日 09:30-11:30

四、报告地点

腾讯会议ID：873 393 528

五、摘要

Large financial systems are typically modeled by interacting particle systems (IPS) that are nonlinear stochastic processes with strongly interrelated components. Both the joint probability distribution and the marginal probability distributions are intractable, except very simple cases in very low dimensions (usually dimension being two). Calibration of the parameters of such a complicated model is indeed an extremely difficult problem requiring evaluations at many points in the parameter space to optimize (Carmona et al. (2009), Finance and Stochastics). This great methodological challenge has been conquered very recently in Ning and Ionides (2021a) through the iterated block particle filter algorithm, for learning high-dimensional parameters over partially observed, nonlinear, and interacted time series models on a general graph, which outperforms the iterated ensemble Kalman filter algorithm (Li et al. (2020), Science) and the iterated filtering algorithm (Ionides et al. (2015), PNAS) in corresponding scenarios. There are open and natural questions such as, how to practically learn high-dimensional parameters over complicated financial models which are usually continuous-time models with their unique properties (such as extra mutual correlations in driving Wiener processes), and especially how to incorporate and complement existing well-functioning algorithms. In this talk, I will generalize a well-established financial IPS to a general graphical setting, demonstrate methodologies on merging rigorous machine learning algorithms into financial paradigms, conduct high-dimensional volatility learning over a large portfolio of assets even covering private equities that have no market prices, and shed light on more accurate control and policymaking over large financial systems.

六、主讲人简介

Dr. Ning Ning holds a B.S. of Math in the China Math Base at Shandong University, a M.S. of Math in Dept. of Math at University of Southern California, and a PhD in the Dept. of Statistics and Applied Probability at UCSB. After graduation she worked one year as Postdoctoral Research Associate in the Dept. of Applied Math at the Univ. of Washington, Seattle and three years as Postdoctoral Research Fellow in the Dept. of Statistics at the University of Michigan, Ann Arbor. From Aug. 1st 2022, she will be Assistant Professor and PhD adviser in the Dept. of Statistics at Texas A&M University.

七、主办单位

非线性期望前沿科学中心

数学与交叉科学研究中心