主讲人：Lin William Cong（Nanyang Technological University）

主持人：胡佳胤（北京大学国家发展研究院）

时间：2026年6月10日（周三）上午10:00-11:30

地点：国家发展研究院承泽园132教室

题目：Pathwise Dynamic Equilibrium and OTC Dealer Intermediation

摘要：

Modern macro-finance models increasingly combine heterogeneous agents with dynamic frictions that depend on histories. Existing HACT and sequence-space methods are powerful once the relevant state representation has been specified, but many frictions—investment adjustment, inventory costs, balance-sheet capacity, attention, and relationship capital—are naturally path-dependent. I develop a pathwise variational approach that derives Euler–Volterra marginal conditions for memory frictions before finite-state approximation, identifies when finite-state implementations are exact or incomplete, and clarifies when dynamic response matrices have scalar cost, value, or welfare content rather than only impulse-response content. The framework yields a dynamic Slutsky-style integrability condition for sequence-space responses, exact least-cost aggregation of heterogeneous convex frictions in scalar-objective cases, and an action-field representation for economies that do not aggregate into a representative planner. The action-field formulation keeps local household, firm, intermediary, distributional, memory, policy, and market-clearing margins separate, so failures of scalar aggregation can be attributed to economically meaningful wedges.

Time-permitting, I also discuss how I apply similar ideas to OTC markets, where trade is non-anonymous and investor–dealer identities matter. In a continuous-time model with heterogeneous investors and dealers, optimal-transport assignment, strategic spread posting, and persistent inventory costs, I derive an OTC Hosios condition: dealer entry is efficient when the dealer-side matching elasticity equals the dealer’s endogenous equilibrium share of match surplus. Own-inventory memory affects spreads and welfare levels but does not create an efficiency wedge because dealers internalize their own future inventory costs. Cross-dealer balance-sheet spillovers and relationship-specific trading memory do create wedges, modifying dealer-entry and assignment conditions. The model decomposes bid–ask spreads into matching, memory, and strategic components and provides testable implications for dealer concentration, platform design, relationship trading, and balance-sheet regulation.

主讲人介绍：

Cong is currently the President’s Chair Professor in Finance, Computing and Data Science at Nanyang Technological University and the Director of the Global Institute of Finance, Technology, and Society (GIFTS). A leading scholar at the intersection of financial economics, AI, and digital technologies, he has played a foundational role in building the fields of digital economics, (blockchain) platforms, tokenomics, generative modeling, and AI for social science.

He is an Editor at Management Science, a (senior) fellow at ABFER, CEPR, and NBER, a scientist faculty at IC3 and a co-founder for several international forums. His work has influenced research, industry practice, and policy debates worldwide on digital assets, monetary innovation, sustainability, AI/FinTech safety, and information design in finance, and has been featured in Bloomberg, CNN, CoinTelegraph, the Economist, Washington Post, etc. He has also advised leading financial institutions, technology firms, and regulatory authorities across North America and Asia, including serving as senior economic advisor or chief scientists for several FinTech companies.

Cong received his Ph.D. in Finance and M.S. in Statistics from Stanford University and holds degrees in Mathematics and Physics from Harvard University. He was formerly on the faculty of Cornell University and University of Chicago, and has taught and guest lectured at other universities such as Harvard, MIT, Stanford, The Study Center Gerzensee, Tsinghua, UBC, UC Berkeley, and Wharton.