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Dec. 28 | Nonlinear and Nonstationary Time Series and Panel Data Models: A Personal Journey

Time:Dec.28(Monday),14:00-15:30Venue:Zoom Conference ID:681 7365 7833Speaker:Jiti Gao, Professor of Econometrics and Economic Statistics at Monash UniversitySummary:Using a number of examples, the speaker will discuss a wide class of nonlinear and nonstationary time series and panel data models as well as non- and semi-parametric methods employed in model building, estimation and specification testing.The datasets involved in the discussion include 1) time series data with trending behavior; and[Detail]
Dec. 7 | Linking risk management under expected shortfall to loss-averse behavior

Time:Dec.7(Monday),19:00-20:00Venue:Zoom Conference ID:647 178 489Speaker:An Chen, professor of School of Mathematics and Economics atUlm UniversitySummary:We introduce and solve an optimal asset allocation problem under a weighted expected shortfall (WES) constraint,which contains the risk management problem under an expected shortfall constraint of \cite{BasakShapiro2001} as a special case. Furthermore,we link our risk management problem under the WES constraint with an optimal asset allocatio[Detail]
Nov.23 | A hybrid deep learning approach for optimal insurance strategies

Time:Nov.23(Monday), 9:00-10:00Venue:Zoom Conference ID:691 9464 0501Speaker:Zhuo Jin, Senior Lecturer of Division of Business and Economics atThe University of MelbourneSummary:This work studies a deep learning approach to find optimal insurance strategies for insurance companies. Due to the randomness of the financial ruin time to terminate the control processes, a Markov chain approximation-based iterative deep learning algorithm is developed to study this type of infinite-horizon optimal con[Detail]
Oct. 30 | Predicting Returns with Text Data

Time:Oct. 30(Friday), 8:30-10:00Venue:Zoom Conference ID:635 4317 6839Speaker:Dacheng Xu, Professor ofEconometrics and Statistics at the University of Chicago Booth School of BusinessSummary:We introduce a new text-mining methodology that extracts information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs [Detail]
Oct. 26 | Deep Optimal Transport and Generative Learning

Professor Huang’s current research interests include high-dimensional statistics, bioinformatics, and statistical machine learning. Professor Huang has made contributions through innovative research and development of methods and algorithms to the areas of high-dimensional statistics, computational statistics, statistical genetics and genomics, semiparametric models, and survival. Many of his publications appeared in top-ranked journals, including Annals of Statistics, Bioinformatics, Biometrics[Detail]

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