光華講壇——社會名流與企業(yè)家論壇第6563期

主題：Time-Varying Factor Selection: A Sparse Fused GMM Approach

主講人：香港城市大學(xué)經(jīng)濟及金融系 崔麗媛助理教授

主持人：西南財經(jīng)大學(xué)金融學(xué)院 呂永健副教授

時間：6月14日 14：00-17：00

舉辦地點：西南財經(jīng)大學(xué)格致樓618A

主辦單位：金融學(xué)院 中國金融研究院 科研處

主講人簡介：

崔麗媛，香港城市大學(xué)經(jīng)濟及金融系助理教授。她在武漢大學(xué)獲應(yīng)用數(shù)學(xué)和金融學(xué)雙學(xué)士學(xué)位；在美國康奈爾大學(xué)（Cornell University）獲經(jīng)濟學(xué)博士學(xué)位。她的主要研究方向是金融計量經(jīng)濟學(xué)、資產(chǎn)定價等。她曾以第一作者在《Management Science》、《International Economic Review》、《Journal of Econometrics》、《Journal of Environmental Economics and Management》、《經(jīng)濟研究》等上發(fā)表論文。曾主持過5項香港研究資助局（GRF）基金和1項國家自然科學(xué)基金青年項目。

內(nèi)容簡介：

This paper proposes a new approach for estimating a time-varying coefficient model under the GMM framework. Our sparse fused GMM (SFGMM) method provides simultaneous specification and estimation for time-varying parameters, heterogeneous structural breaks, and time-varying sparsity of a potentially high dimension of covariates. We derive large sample properties for our estimator with and without prior knowledge of structural changes and test the conditional stochastic discount factor (SDF) model. Our method addresses the “factor zoo” challenge by providing a new perspective for time-varying factor selection. First, our asymptotic theory on the time-varying specified model suggests rejecting the fixed model hypothesis, indicating the significant factors and their identities change over time. Second, we find the collective explanatory power of risk factors is high during periods of high interest rates or high inflation but declines when market liquidity is low. Third, the SFGMM strategy achieves the best risk- adjusted investment performance in the past four decades for out-of-sample performance comparison. Finally, we evaluate the unsynchronized factor discovery to accommodate real-time academic publication timings and find many factors are no longer selected or significant after publication.