Session 2: Financial Regulation

Regulation shapes the boundaries of firms. When prudential standards bind asymmetrically across subsidiaries of an integrated organization, internal capital markets become a mechanism for regulatory arbitrage. We study this in U.S. banking, where holding companies encompass both heavily regulated depository institutions and lightly regulated nonbank affiliates. Following Basel III in 2015, holding companies extract equity from nonbank subsidiaries to recapitalize their banks. Bank subsidiaries accumulate 5–8 percentage points more excess capital than comparable standalone banks, through internal transfers; consolidated equity, assets, and lending are unchanged. Within the same organization, banks become safer while nonbank affiliates experience declining capital ratios, deteriorating credit quality, and aggressive expansion into consumer lending. Risk is shifted rather than eliminated, leaving the consolidated organization exposed to nonbank distress. We calibrate stress scenarios to 2008-scale losses on nonbank assets. If parents were to recapitalize distressed subsidiaries, 4—6% of holding companies would exhaust their capital buffers. For the most exposed institutions, the apparent improvement in bank safety is substantially overstated once the implicit liability to nonbank affiliates is accounted for. Organizational structure is a fundamental determinant of regulatory outcomes.

We study whether AI methods applied to large-scale portfolio holdings data can improve macroprudential financial regulation. We build a graph-based deep learning model tailored to security-level data on the holdings of financial intermediaries. The architecture incorporates economic priors and learns latent representations of both assets and investors from the network structure of portfolio positions. Applied to the universe of non-bank financial intermediaries, covering nearly $40 trillion in wealth, the model substantially outperforms existing approaches in out-of-sample forecasts of intermediary trading behavior, including in crisis episodes. The model has more than ten times the explanatory power for the cross-sectional variation in asset returns during stress events compared to traditional approaches, and it outperforms existing systemic risk metrics at the institution level. Its learned representations show that the holdings network encodes rich, economically interpretable information about fire-sale vulnerability. The architecture is fully inductive, producing informative estimates even when entire asset classes or investors are withheld from training. We embed our empirical approach into a macroprudential optimal policy framework to formalize why these objects matter for policy and welfare. We show that even in an equilibrium environment subject to the Lucas critique, the predictive information from the model improves welfare by sharpening the cross-sectional targeting of policy interventions, and we demonstrate a complementarity between prediction and structural knowledge.

Despite the secular shift to nonbank lending, banks retain a distinctive advantage in providing credit lines (Kashyap, Rajan, and Stein, 2002). We document a new pattern in nonbank lending: business development companies (BDCs) extend substantial credit commitments to borrowers, with commitment-to-asset ratios comparable to those of banks. These off-balance-sheet commitments are concentrated, exposing BDCs to undiversified borrower liquidity shocks—a risk they manage with credit lines provided by banks. We develop a model of layered liquidity insurance along the credit chain: the BDC pools borrower firms to partially diversify idiosyncratic shocks and uses a bank credit line to backstop residual liquidity risk. In equilibrium, the nonbank optimally provides only partial liquidity insurance, which generates a novel externality whereby firms sharing the constrained liquidity pool inefficiently underinvest in liquidity management.

Financial systems channel funds from savers to borrowers through a mix of bank- and market-based intermediation. We show that financial-sector structure is shaped, in large part, by real-sector characteristics that generate demand for heterogeneous savings and borrowing instruments. Across countries, economies with younger, wealthier, and more unequal household sectors rely less on banks, as do economies whose firm sectors feature wider size distributions, more intangible-intensive production, and larger service sectors. Using U.S. microdata, we show that these patterns reflect financial decisions that vary systematically across households and firms. Motivated by this evidence, we develop and estimate a model with heterogeneous households and firms in which different types of intermediaries compete to satisfy savings and borrowing demand. We estimate how household and firm demand varies with real-sector characteristics using U.S. microdata, and calibrate remaining parameters to match asset prices and aggregate balance sheets. Counterfactuals show that rising household wealth, aging, and wealth concentration since the 1980s reduce the role of bank deposits, while firm heterogeneity primarily affects the composition of credit demand. These forces provide a demand-side foundation for the growth of alternative financing: a wealthier household sector demands more market-based claims, while changes in firm composition shift relative demand from bank toward nonbank finance, including forms of private credit and private equity. Cross-country real-sector counterfactuals explain a meaningful component of international variation in bank funding and credit shares, while leaving substantial residual dispersion, especially on the credit side.

How is homebuying affected by relative changes in the price of mortgage credit? We study it using a natural experiment where the government-sponsored enterprises' (GSEs) guarantee fee (g-fee) pricing changed differently across borrower groups. We find large elasticities of home purchases to changes in fee-adjusted GSE interest rate, particularly in tight housing markets. These results suggest that interest rate subsidy programs can meaningfully induce demand in tight market conditions. Our elasticities also discipline the extensive-margin elasticity in macroeconomic models of housing and mortgage credit. We use a structural model calibrated to our estimates to evaluate alternative g-fee proposals.

Collateral constraints limit household migration to expensive locations by restricting financing for home purchases. Such endogenous location choice amplifies the impact of relaxing household borrowing constraints. Using China’s cash-based shantytown renovation program (2015-2018) as a natural experiment, we provide evidence that cash resettlement—by converting illiquid shanty houses into cash—facilitated household location upgrading and raised house prices in more expensive locations. A dynamic spatial model with collateral constraints confirms household migration responses to the cash transfer. Quantitatively, endogenous migration amplifies household housing expenditure responses by around 40%, and is able to explain more than 20% of the housing price growth in 2016-2020.

We study the causes and consequences of bank runs. By applying large language models to historical newspapers, we create a comprehensive database of bank runs in U.S. history with information on 3,984 runs on individual banks from 1863 to 1934. Our novel data allow us to establish that runs are considerably more likely in weak banks but also occur in strong banks, especially in response to negative news about the real economy or the broader banking system. However, runs typically only result in failure for banks with poor fundamentals. Strong banks survive runs through various mechanisms, including signaling strength, interbank cooperation, and temporary suspension. At the local level, runs on banks with poor fundamentals translate into substantially larger declines in deposits, lending, and manufacturing activity than runs on strong banks. Our findings imply that poor fundamentals are central to explaining both when runs occur and when they have severe economic effects, tempering the view that small shocks can generate discontinuous jumps to bad equilibria through self-fulfilling run dynamics.

Using administrative data covering the universe of retail loans in Brazil, we document two facts that challenge standard models of non-exclusive lending. First, universal default is rare: 84% of borrowers who default on one credit card continue to repay other cards. Second, borrowers systematically prioritize repayment, favoring cards with higher credit limits, lower interest rates, or bundled with other products. Motivated by these facts, we develop a model of non-exclusive lending in which lenders compete for repayment priority through contract terms and cross- selling. We show that there can be over- or underprovision of relationship benefits and that equal-recovery bankruptcy policies may reduce welfare. Our back-of-the-envelope calculation implies that the credit limit one year after origination are lower by by 7% relative to the first best. Imposing equal recovery would lower credit limits by 2.5%.

We study how consumers’ tendency to hold multiple financial products from the same bank shapes competition and policy in U.S. retail banking. We document pervasive crossholding across deposits, credit cards, auto loans, mortgages, and brokerage services — a pattern that spans all consumer segments. Combining an original consumer survey on search and switching with an equilibrium model of bank choice under incomplete consideration, we identify three forces behind cross-holding: correlated preferences across banking products, consideration spillovers from banks’ cross-selling efforts, and utility complementarities between same-bank products. Consideration spillovers and utility complementarities together account for a substantial share of bank franchise value, with reward credit cards functioning as a loss leader whose relationship value far exceeds its standalone margin. Accounting for these synergies qualitatively changes standard policy conclusions. A narrow-bank entrant into the deposit market competes at a steep disadvantage, as incumbents are shielded by their credit card customer bases. Interchange-fee regulation that depresses credit card demand spills over into deposit competition. And a horizontal merger that appears anticompetitive on a deposit-HHI screen becomes pro-competitive once the merged bank deploys its credit card as a loss leader for a larger depositor base.

This paper studies how banks compete through a novel lens: the content of advertising videos. Using video embeddings, we identify three competitive dimensions: pricing, service quality, and trust-building emotional appeals. We build a framework connecting advertising content to franchise value and monetary policy transmission. Empirical evidence is consistent with model predictions. Banks with high local market shares compete on service and trust while downplaying pricing. New entrants compete primarily on pricing. Banks lacking pricing or service advantages lean on emotional appeals. Non-price advertising strengthens the deposit franchise, enabling banks to maintain wider spreads during monetary tightening and shaping monetary transmission.

This paper investigates the effect of deposit insurance (DI) on household portfolio allocation between bank deposits and risky assets. Theoretically, limited DI creates a kink in the capital allocation line, causing depositor bunching at the DI threshold and increased equity holdings. Using a natural experiment in India and data on individual holdings of stocks, deposits, and mutual funds, we confirm depositor bunching at the DI threshold. Leveraging a bunching-in-differences design, we show that DI expansion shifts portfolios from equities and mutual funds to deposits, driven by unmet demand for safe assets. Bunchers increase their deposits between 3.6% and 5.1% by liquidating their stock holdings, which were more exposed to safer state-owned enterprises, transiently affecting the asset prices of these stocks. We show that the share of bunchers is a sufficient statistic to measure the depositor-implied bank failure risk. Our estimates of the welfare effect of changes in DI show that depositors gain at least 0.04% as DI increases, even after accounting for the resulting moral hazard by banks.