蛋播视频一区,无码鲁丝一区二区,精品 久久 五月天,国产老熟女,五月草草在线观看,中文日韩欧美,情色一区二区三区,欧美日韩亚洲激情在线,亚洲制服在线香蕉

報告題目：Positive Definite High-dimensional Covariance Estimation Under a General Factor Model with High-

frequency Data

報告人：崔麗媛 香港城市大學

報告時間：2022年10月25日(周二) 16:30-18:00

報告地點：中科院數(shù)學與系統(tǒng)科學研究院南樓N204；

騰訊會議ID：375-8612-5504

內(nèi)容摘要

This paper proposes a novel large-dimensional positive definite covariance (LDPDC) estimator for high-frequency data under a general factor model framework. We demonstrate an appealing connection between LDPDC and a weighted group LASSO penalized least squares estimator. LDPDC improves the traditional principal component analysis by allowing for weak factors, whose signal strengths are relatively weak compared to the idiosyncratic component. Even when microstructure noise and asynchronous trading are present, LDPDC achieves a guarded positive definiteness without deteriorating convergence rates. To make LDPDC fully operational, we provide an extended simultaneous alternating direction method of multipliers algorithm to solve the resultant constrained convex minimization problem. We offer a data-driven algorithm to select involved tuning parameters in practice optimally. Empirically, we study the monthly high-frequency covariance structure of the stock constituents of the S&P 500 index from 2008 to 2016, based on which we construct statistical high-frequency factor returns. We use all traded stocks from NYSE, AMEX, and NASDAQ stock markets to construct 12 high-frequency firm characteristic-based economic factors. We further examine the out-of-sample performance of LDPDC through vast portfolio allocations, which deliver significantly reduced out-of-sample portfolio risk and enhanced Sharpe ratios. The success of our approach helps justify the usefulness of machine learning techniques in finance.

主講人簡介

崔麗媛，現(xiàn)為香港城市大學經(jīng)濟與金融系助理教授。2010年本科畢業(yè)于武漢大學數(shù)學與應用數(shù)學專業(yè)，2017年獲得美國康奈爾大學經(jīng)濟學博士學位。主要研究方向包括金融計量經(jīng)濟學，高維協(xié)方差矩陣分析，高頻交易，非參數(shù)統(tǒng)計建模等。