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  5. Quantitative Advances in Asset Pricing
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Quantitative Advances in Asset Pricing

Paper Session

Sunday, Jan. 4, 2026 10:15 AM - 12:15 PM (EST)

Philadelphia Marriott Downtown, Room 310
Hosted By: Econometric Society
  • Chair: Sean Myers, University of Pennsylvania

Credit Lines and Bank Risk

Jason Hukjae Choi
,
University of Toronto

Abstract

This paper studies how much providing credit lines to firms contributes to bank risk and its welfare implications. I develop a quantitative model in which banks lend to heterogeneous firms both through term loans and credit lines. Credit lines give firms liquidity insurance against crisis times and help alleviate financing frictions. At the same time, credit lines also introduce a new channel for banks to be exposed to excessive risk. I estimate the model to match both aggregate and distributional moments. I find that 20% of bank losses in crisis times can be attributed to credit lines, and that credit lines help stabilize banks during crises, but only to moderate shocks. My model suggests banks are overlending in both contracts compared to a planner, but the relative shares of contracts are close optimal. I find bank capital ratios should be 3% higher and show how to implement optimal policy. Additionally, I show how a model with only term loans would underpredict optimal capital ratios.

The Subjective Belief Factor

Tingyue Cui
,
University of Pennsylvania
Ricardo Delao
,
University of Southern California
Sean Myers
,
University of Pennsylvania

Abstract

Subjective expectations and asset prices both revolve around distorted probabilities. Subjective expectations are expectations under biased probabilities, and asset prices are expectations under risk-neutral probabilities. Given this link, asset pricing techniques designed to estimate a Stochastic Discount Factor (SDF) can be used to estimate a Subjective Belief Factor (SBF) – a distortion that characterizes many subjective expectations, even for non-financial variables. Further, the Subjective Belief Factor can be used to characterize asset prices, by separating the roles of beliefs and preferences/risk. Using the Survey of Professional Forecasters and Blue Chip, we find that differences between subjective expectations and statistical expectations for 24 macroeconomic variables can be summarized (average R-squared of 50%) by a single SBF related to real GDP growth and the T-bill rate. This SBF accurately replicates differences across the 24 variables in the serial correlation of forecast errors and under/overreaction. Applying this SBF to cross-sectional stock returns, we find it accounts for the majority of excess returns for the Fama-French factors and explains about two thirds of the variation in returns across 176 anomalies, while the remaining third is attributed to preferences/risk. Our results support models like diagnostic expectations and robust control in which agents' beliefs across different variables are characterized by a single probability distortion.

Transaction Costs and Slow-Moving Capital in Risk-On and Risk-Off Arbitrage Regimes

Won-Chang Choi
,
Yonsei University

Abstract

I investigate asset price dynamics and market efficiency in a competitive economy where risk-averse arbitrageurs face price impact costs and gradually share hedgers' liquidity risks in segmented markets. The asset spread varies with the level of hedgers' liquidity risks and aggregate arbitrage capital. While the spread increases with hedgers' liquidity risks, its relationship with aggregate arbitrage inventory depends on the relative holding-to-trading costs of arbitrageurs and hedgers. This spread-inventory relationship identifies two distinct arbitrage regimes: "Risk-On"" and ""Risk-Off."" In the Risk-On regime

Market Dynamics of Risk-On and Risk-Off Incentives and their Effect on Asset Prices

Idan Hodor
,
Monash University
Fernando Zapatero
,
Boston University

Abstract

Mutual fund asset managers initially strive to outperform the index, but as performance improves, their focus shifts to maintaining performance, resembling an S-shaped objective akin to prospect theory, with an inflection point equaling the benchmark rather than wealth. This paper integrates this objective into a dynamic equilibrium asset pricing model. The equilibrium, expressed in closed form, introduces two predictability channels: (i) changes in incentives and (ii) changes in performance uncertainty. These channels lead to momentum in outperformance and reversal in underperformance, both in the time-series and cross-section. Furthermore, the active share exhibits a U-shape and tracking error an inverted U-shape.
JEL Classifications
  • G12 - Asset Pricing; Trading Volume; Bond Interest Rates