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Working Paper
Technological innovation in mortgage underwriting and the growth in credit, 1985–2015
Foote, Christopher L.; Loewenstein, Lara; Willen, Paul S. (2019-11-01)
The application of information technology to finance, or ?fintech,? is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loan-processing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher ...
Working Papers , Paper 19-11

Conference Paper
Designing Resilient Monetary Policy Frameworks for the Future : Economic Policy Symposium, Jackson Hole, Wyoming, August 25-27, 2016
Kansas City, Federal Reserve Bank (2016)
Proceedings - Economic Policy Symposium - Jackson Hole

Working Paper
Consumer Bankruptcy, Mortgage Default and Labor Supply
Li, Wenli; Meghir, Costas; Oswald, Florian (2022-08-30)
We specify and estimate a lifecycle model of consumption, housing demand and labor supply in an environment where individuals may file for bankruptcy or default on their mortgage. Uncertainty in the model is driven by house price shocks, education specific productivity shocks, and catastrophic consumption events, while bankruptcy is governed by the basic institutional framework in the U.S. as implied by Chapter 7 and Chapter 13. The model is estimated using micro data on credit reports and mortgages combined with data from the American Community Survey. We use the model to understand the ...
Working Papers , Paper 22-26

Working Paper
Tokenization: Overview and Financial Stability Implications
Swem, Nathan; Gerszten, Jacob; Chuan, Grace; Carapella, Francesca (2023-09-08)
In this paper we outline tokenization, which is a new and rapidly growing financial innovation in crypto asset markets, and we discuss potential benefits and financial stability implications. Tokenization refers to the process of constructing digital representations (crypto tokens) for non-crypto assets (reference assets). As we discuss below, tokenizations create interconnections between the digital asset ecosystem and the traditional financial system. At sufficient scale, tokenized assets could transmit volatility from crypto asset markets to the markets for the crypto token's reference ...
Finance and Economics Discussion Series , Paper 2023-060

Working Paper
Credit Default Swaps in General Equilibrium: Spillovers, Credit Spreads, and Endogenous Default
Darst, Matt; Refayet, Ehraz (2016-04-19)
This paper highlights two new effects of credit default swap markets (CDS) in a general equilibrium setting. First, when firms' cash flows are correlated, CDSs impact the cost of capital{credit spreads{and investment for all firms, even those that are not CDS reference entities. Second, when firms internalize the credit spread changes, the incentive to issue safe rather than risky bonds is fundamentally altered. Issuing safe debt requires a transfer of profits from good states to bad states to ensure full repayment. Alternatively, issuing risky bonds maximizes profits in good states at the ...
Finance and Economics Discussion Series , Paper 2016-042

Working Paper
Technological Innovation in Mortgage Underwriting and the Growth in Credit: 1985-2015
Foote, Christopher L.; Loewenstein, Lara; Willen, Paul S. (2018-12-07)
The application of information technology to finance, or ?fintech,? is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s computerization allowed mortgage lenders to reduce loan-processing times and largely replace human-based assessment of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher ...
Working Papers (Old Series) , Paper 1816

Discussion Paper
Blockchain and Financial Market Innovation
Lewis, Rebecca; Ranjan, Rajeev; McPartland, John (2017-06-01)
Blockchain technology is likely to be a key source of future financial market innovation. It allows the creation of immutable records of transactions accessible by all participants in a network. A blockchaindatabase is made up of a number of blocks “chained” together through a reference in each block to the previous block. Each block records one or more transactions, which are essentially changes in the listed owner of assets. New blocks are added to the existing chain through a consensus mechanism in which members of the blockchain network confirm transactions as valid.While all are in ...
Policy Discussion Paper Series

Working Paper
Mixed Signals: Investment Distortions with Adverse Selection
Darst, Matt; Refayet, Ehraz (2019-06-21)
We study how adverse selection distorts equilibrium investment allocations in a Walrasian credit market with two-sided heterogeneity. Representative investor and partial equilibrium economies are special cases where investment allocations are distorted above perfect information allocations. By contrast, the general setting features a pecuniary externality that leads to trade and investment allocations below perfect information levels. The degree of heterogeneity between informed agents' type governs the direction of the distortion. Moreover, contracts that complete markets dampen the impact ...
Finance and Economics Discussion Series , Paper 2019-044

Report
Appendix for Financial Frictions and Fluctuations in Volatility
Kehoe, Patrick J.; Bai, Yan; Arellano, Cristina (2017-01-24)
This appendix contains five sections. Section 1 provides details for the comparative statics exercise performed in the simple example. Section 2 discusses extending the model to allow firms to default on the wages for managers. Section 3 describes the firm-level and aggregate data. Section 4 contains the details of the computational algorithm. Finally, Section 5 reports the results for our model with a lower labor elasticity.
Staff Report , Paper 538

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