Assistant Professor of Finance, University of Colorado Boulder
Research Interest: Asset Pricing, International Finance, Financial Economics
We develop a transparent Bayesian framework to measure uncertainty in asset pricing models. Our framework quantifies the tradeoff between mean-variance efficiency and parsimony for models to attain high posterior probabilities. Model uncertainty is defined as the entropy of these posterior probabilities, which is consistently interpretable even under misspecification due to omitted factors. Empirically, model uncertainty accumulates during major market events, carrying a significantly negative risk premium of approximately half the magnitude of the market. Positive shocks to model uncertainty predict persistent outflows from US equity funds and inflows to Treasury funds.
We use option prices to derive bounds on the probability of a crash in an individual stock, and argue that the lower bound should be close to the truth. Empirically, the lower bound is highly statistically and economically significant; on its own, it outperforms 15 stock characteristics proposed by the prior literature combined. In a multivariate regression, a one standard deviation increase in the bound raises the predicted crash probability by 3 percentage points, whereas a one standard deviation increase in the next most important predictor (a measure of short interest) raises the predicted probability by only 0.3 percentage points.
I develop and estimate a limits-to-arbitrage model to quantify the effects of financial constraints, arbitrage capital, and hedging demands on asset prices and their deviations from frictionless benchmarks. Using foreign exchange derivatives price and quantity data, I find that varying financial constraints and hedging demands contribute to 46 and 35 percent variation in the deviations from covered interest parity of one-year maturities. While arbitrage capital fluctuation explains the remaining 19 percent of variation on average, it periodically stabilizes prices when the other two forces exert disproportionately large impacts. The model features a general form of financial constraints and produces a nonparametric arbitrage profit function. I unveil shapes and dynamics of financial constraints from estimates of this function.
The Spread of COVID-19 in London: Network Effects and Optimal Lockdowns (with Christian Julliard and Kathy Yuan) Journal of Econometrics (2023), 235:2:2125-2154
We generalise a stochastic version of the workhorse SIR (Susceptible-Infectious-Removed) epidemiological model to account for spatial dynamics generated by network interactions. Using the London metropolitan area as a salient case study, we show that commuter network externalities account for about 42% of the propagation of COVID-19. We find that the UK lockdown measure reduced total propagation by 44%, with more than one third of the effect coming from the reduction in network externalities. Counterfactual analyses suggest that: the lockdown was somehow late, but further delay would have had more extreme consequences; a targeted lockdown of a small number of highly connected geographic regions would have been equally effective, arguably with significantly lower economic costs; targeted lockdowns based on threshold number of cases are not effective, since they fail to account for network externalities.
FNCE 3030 - Investment and Portfolio Management (Spring 2023, Fall 2023)