预付期权与公司风险承担
Prepayment Option and Firm Risk-taking
讲座信息
主讲人
孙博教授
弗吉尼亚大学
日期和时间
2025年6月24日（周二）
10:30-12:00
地点
综合教学楼D804会议室
We study the pervasive adoption of prepayment options in debt contracts, a phenomenon that persisted robustly even during periods of near-zero interest rates when traditional hedging motives were substantially attenuated. We develop an agency theoretic framework of debt contracting demonstrating that the option to prepay—representing opportunities to refinance when uncertainty about project quality abates over time—provides ex ante incentives for prudent risk-taking, thereby improving firm value. Leveraging the supervisory Federal Reserve Y-14Q data on individual loan contracts, we document data patterns on the take-up, pricing, and ex post performance of bank loans that are consistent with a disciplining role of prepayment options. These findings suggest that prepayment options may serve as effective substitutes for traditional collateral-based lending by establishing dynamic state-contingent incentives that address moral hazard concerns. Moreover, we identify a strategic complementarity between prepayment options and macroprudential regulation, whereby regulatory measures that enhance the risk sensitivity of loan pricing can amplify the disciplinary effect of prepayment options.
展开剩余60%主讲人简介
孙博教授
弗吉尼亚大学
孙博是全球经济与市场领域的副教授，目前担任《Journal of Money》《 Credit and Banking》的副主编。孙教授对循证决策有着浓厚的兴趣，她就信息摩擦对经济的影响，尤其是对合约设计、金融市场交易和实体经济活动的影响，开展理论和实证研究。她的研究成果发表在《American Economic Review》《Journal of Monetary Economics》《International Economic Review》《Journal of Economic Literature》《 American Economic Journal: Microeconomics》等权威学术期刊上。在加入Darden商学院之前，孙博曾是美联储理事会的首席经济学家。她还于2011年至2014年在北京大学光华管理学院任教。她拥有弗吉尼亚大学经济学博士学位和北京大学金融学学士学位。
教机器学习经济学
Teaching Economics to the Machines
讲座信息
主讲人
陈晖教授
麻省理工大学
日期和时间
2025年6月26日（周四）
10:30-12:00
地点
综合教学楼D804会议室
Structural models in economics often suffer from a poor fit with the data and demonstrate suboptimal forecasting performances. Machine learning models, in contrast, offer rich flexibility but are prone to overfitting and struggle to generalize beyond the confines of training data. We propose a transfer learning framework that incorporates economic restrictions from a structural model into a machine learning model. Specifically, we first construct a neural network representation of the structural model by training on the synthetic data generated by the structural model and then fine-tune the network using empirical data. When applied to option pricing, the transfer learning model significantly outperforms the structural model, a conventional deep neural network, and several alternative approaches for bringing in economic restrictions. The out-performance is more significant i) when the sample size of empirical data is small, ii) when market conditions change relative to the training data, or iii) when the degree of structural model misspecification is likely to be low.
主讲人简介
陈晖教授
麻省理工大学
陈晖现任麻省理工学院斯隆管理学院野村金融讲席教授。他的研究领域是资产定价及其与公司金融的关系。陈教授尤其专注于宏观经济和利率期限结构、信用风险、融资及投资决策之间的相互影响。他近期的研究项目包括应用经济周期模型来解释企业融资行为和公司债券定价，以及不完全市场对创业融资和投资的影响分析。陈晖教授于2000年获得中山大学经济金融学学士学位，2002年获得密歇根大学数学硕士学位，2007年获得芝加哥大学金融学博士学位。
转自：深圳高等金融研究院
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