Banking, Risk Management, Financial Regulation
“Banks' Next Top Model” (Job Market Paper)
I study the design of regulation using banks’ internal risk models. Specifically, I explore the optimal combination of capital requirements and penalties to ensure truthful reporting. I first characterize optimal risk-sensitive capital and penalties when banks have private information about their risk. I find that the Basel framework can be rationalized provided sufficient variation in banks' preferences. I then use hand-collected data on risk model revisions to show that current penalties provide only weak incentives for model improvements and in fact induce misreporting. My model suggests that recent changes in Basel regulation may further impair truthful disclosure.
This paper investigates the strategic use of banks' internal models for market risk. We study hand-collected data on modelling and disclosure choices and examine how they relate to the level of Value-at-Risk (VaR) and the number of VaR violations. We find that more elaborate modelling like using more historical data and Monte Carlo (MC) simulation can correspond to more conservative VaR, but seems to be more punitive in terms of capital requirements. Presumably more sophisticated, but also more opaque internally computed 10-day VaRs, instead, seem not to capture tail risk well, precisely when in-house information should be particularly valuable. We conclude that capital requirements for market risk are compromised by strategic modelling, but that the degree of the implied distortion depends on the specific model choice.
“Implied Banks’ Risk Exposure: A Model-Free and Forward-Looking Approach” (with
This paper examines how option data can be used to determine the risk level of banks. Such an estimate is called an implied estimate. Option prices convey aggregate views of the market participants on the future stock price level, i.e., not just on its expected value, but rather on the distribution around that expectation. Therefore, an option-implied estimate is automatically forward-looking and fast-moving. We propose a novel model-free approach based on the binomial tree option pricing. We show that our implied estimates serve better as an indicator of individual and system-wide bank distress than their stock-based counterparts.
Supervisor of 24 projects (Fall 2017-2021)
University of Illinois at Urbana-Champaign
Risk Management Practices and Regulation
Invited Lecturer (Fall 2019)
Invited Lecturer (Spring 2020)
Illinois Risk Lab
Graduate Supervisor (Spring 2020, Fall 2020)
IRisk Lab Award for the project in Fall 2020 as one of three exceptional IRisk Lab projects in Fall 2019, Spring 2020, Summer 2020, and Fall 2020
Honorable Mention Award for the project in Spring 2020 at the UIUC 2020 Undergraduate Research Symposium
Higher School of Economics
Teaching Assistant (Fall 2014)