主讲人：
杨雨成（苏黎世大学和瑞士金融学院助理教授）
主持人：
（北大经院）李博
时间：
2024年11月15日（周五）
10:00-11:30
地点：
北京大学光华管理学院新楼478会议室
主讲人简介：
Yucheng Yang is an Assistant Professor of Finance at the University of Zurich and Swiss Finance Institute. He has been a visiting faculty at Yale and NYU. His research interests span macroeconomics, finance, and machine learning. He has developed deep learning methods to study quantitative macro and finance models with rich heterogeneity. Professor Yang has received the CICF Yihong Xia Best Paper Award, CES Gregory Chow Best Paper Award, and a Swiss SNF Grant on "Heterogenous Agent Macro-Finance: Models and Methods". He holds a Ph.D. from Princeton University and dual Bachelor degrees from Peking University.
摘要：
We develop a new method for characterizing global solutions to search and matching models with aggregate shocks and heterogeneous agents. We formulate general equilibrium as a high dimensional partial differential equation (PDE) with the distribution as a state variable. Solving this problem has previously been intractable because the distribution impacts agent decisions through the matching mechanism rather than through aggregate prices. We overcome these challenges by developing a new deep learning algorithm with efficient sampling in a high dimensional state space. This allows us to study search markets that are not "block recursive" and compute variables (e.g. wages and prices) that were previously unattainable. In applications to labor search models, we show that distribution feedback plays a more important role when aggregate shocks have an asymmetric impact across agents. Business cycles have a "cleansing" effect by amplifying positive assortative matching in recessions, and the magnitude of the countercyclicality depends on the bargaining process between workers and firms. In applications to OTC markets, we show how default risk impacts bond prices across different maturities.
