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1. Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol

   https://doi.org/10.4230/LIPIcs.AFT.2024.27  

   Bastankhah, Mahsa; Nadkarni, Viraj; Jin, Chi; Kulkarni, Sanjeev; Viswanath, Pramod (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Böhme, Rainer; Kiffer, Lucianna (Ed.)

   Decentralized finance (DeFi) borrowing and lending platforms are crucial to the decentralized economy, involving two main participants: lenders who provide assets for interest and borrowers who offer collateral exceeding their debt and pay interest. Collateral volatility necessitates over-collateralization to protect lenders and ensure competitive returns. Traditional DeFi platforms use a fixed interest rate curve based on the utilization rate (the fraction of available assets borrowed) and determine over-collateralization offline through simulations to manage risk. This method doesn't adapt well to dynamic market changes, such as price fluctuations and evolving user needs, often resulting in losses for lenders or borrowers. In this paper, we introduce an adaptive, data-driven protocol for DeFi borrowing and lending. Our approach includes a high-frequency controller that dynamically adjusts interest rates to maintain market stability and competitiveness with external markets. Unlike traditional protocols, which rely on user reactions and often adjust slowly, our controller uses a learning-based algorithm to quickly find optimal interest rates, reducing the opportunity cost for users during periods of misalignment with external rates. Additionally, we use a low-frequency planner that analyzes user behavior to set an optimal over-collateralization ratio, balancing risk reduction with profit maximization over the long term. This dual approach is essential for adaptive markets: the short-term component maintains market stability, preventing exploitation, while the long-term planner optimizes market parameters to enhance profitability and reduce risks. We provide theoretical guarantees on the convergence rates and adversarial robustness of the short-term component and the long-term effectiveness of our protocol. Empirical validation confirms our protocol’s theoretical benefits. 

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2. Bank Types, Inclusivity, and Paycheck Protection Program Lending During COVID-19

   https://doi.org/10.1177/08912424231163485  

   Cassell, Mark_K; Schwan, Michael; Schneiberg, Marc (March 2023, Economic Development Quarterly)

   How do differences in bank or lending institution type shape access to credit for small businesses in poor and/or minority communities in the United States? Banking systems are populated by lenders that differ qualitatively in their organizational forms, business models and missions, and that connect—or fail to connect—to small business borrowers and local communities in divergent ways. The authors analyze data on the Paycheck Protection Program and its over 11 million loans made to businesses across the United States to trace how these differences shaped the flow of credit to poor and minority communities. The authors find substantial differences across seven lender types, both in their propensities to avoid or lend to firms in traditionally marginalized communities, and in how much they lend to poor and majority–minority communities relative to their nonpoor and majority White counterparts. From this variety within American banking, the authors identify two potential pathways for more inclusive lending. 

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3. Real Effects of Search Frictions in Consumer Credit Markets

   https://doi.org/10.1093/rfs/hhac088  

   Argyle, Bronson; Nadauld, Taylor; Palmer, Christopher (November 2022, The Review of Financial Studies)

   Matvos, Gregor (Ed.)

   We show that search frictions in credit markets affect accepted interest rates and loan sizes and distort consumption. Using data on car loan applications and originations not intermediated by car dealers, we isolate quasi-exogenous variation in both the costs and benefits to searching for credit. After identifying lender-specific policies that price risk discontinuously, we study the differential response to offered interest rates by borrowers who face high and low search costs. High-search-cost borrowers are 10$$\%$$ more likely to accept loan offers with higher markups, consequently originating smaller loans and purchasing older and less expensive cars than lower-search-cost borrowers. 

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4. Reshaping Global Trade: The Immediate and Long-Run Effects of Bank Failures

   https://doi.org/10.1093/qje/qjac016  

   Xu, Chenzi (April 2022, The Quarterly Journal of Economics)

   Abstract I show that a disruption to the financial sector can reshape the patterns of global trade for decades. I study the first modern global banking crisis originating in London in 1866 and collect archival loan records that link multinational banks headquartered there to their lending abroad. Countries exposed to bank failures in London immediately exported significantly less and did not recover their lost growth relative to unexposed places. Their market shares within each destination also remained significantly lower for four decades. Decomposing the persistent market-share losses shows that they primarily stem from lack of extensive-margin growth, as the financing shock caused importers to source more from new trade partnerships. Exporters producing more substitutable goods, those with little access to alternative forms of credit, and those trading with more distant partners experienced more persistent losses, consistent with the existence of sunk costs and the importance of finance for intermediating trade. 

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5. A copula model for marked point process with a terminal event: An application in dynamic prediction of insurance claims

   https://doi.org/10.1214/24-AOAS1902  

   Yang, Lu; Shi, Peng; Huang, Shimeng (December 2024, The Annals of Applied Statistics)

   Accurate prediction of an insurer’s outstanding liabilities is crucial for maintaining the financial health of the insurance sector. We aim to develop a statistical model for insurers to dynamically forecast unpaid losses by leveraging the granular transaction data on individual claims. The liability cash flow from a single insurance claim is determined by an event process that describes the recurrences of payments, a payment process that generates a sequence of payment amounts, and a settlement process that terminates both the event and payment processes. More importantly, the three components are dependent on one another, which enables the dynamic prediction of an insurer’s outstanding liability. We introduce a copula-based point process framework to model the recurrent events of payment transactions from an insurance claim, where the longitudinal payment amounts and the time-to-settlement outcome are formulated as the marks and the terminal event of the counting process, respectively. The dependencies among the three components are characterized using the method of pair copula constructions. We further develop a stagewise strategy for parameter estimation and illustrate its desirable properties with numerical experiments. In the application we consider a portfolio of property insurance claims for building and contents coverage obtained from a commercial property insurance provider, where we find intriguing dependence patterns among the three components. The superior dynamic prediction performance of the proposed joint model enhances the insurer’s decision-making in claims reserving and risk financing operations. 

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