Ran Shi

Assistant Professor of Finance, University of Colorado Boulder

Email: ran.shi@colorado.edu

Research Interest: Asset Pricing, International Finance, Financial Economics

Working Papers

**Forecasting Crashes with a
Smile** (with Ian Martin)

We introduce a framework that
uses option prices to deliver upper and lower bounds on the probability
of crash in an individual stock, and argue based on a priori
considerations that the lower bound should be close to the true crash
probability. Empirical tests support this prediction in and out of
sample. We horse-race the lower bound against a range of characteristics
proposed by the prior literature. The lower bound is highly
statistically significant, with a *t*-statistic above five, and
is an order of magnitude more economically significant than any of the
characteristics, in the sense that a one standard deviation increase in
the lower bound raises the predicted probability of a crash by 3
percentage points, whereas a one standard deviation change in the next
most important predictor (a measure of short interest) moves the
predicted probability of a crash by only 0.3 percentage points.

**Model Uncertainty in the Cross
Section** (with Jiantao
Huang)

We develop a transparent
Bayesian framework to quantify uncertainty in linear stochastic discount
factor (SDF) models. In our framework, model probabilities increase with
in-sample Sharpe ratios and decrease with model dimensions. The entropy
of model probabilities represents model uncertainty. We provide
theoretical guarantees to ensure consistent interpretation of our model
uncertainty measure, even for misspecified models with omitted factors.
Empirically, surging model uncertainty coincides with major market
events. Combining SDF models improves mean-variance efficiency only
during periods of high model uncertainty. Positive shocks to model
uncertainty predict persistent outflows from US equity funds and inflows
to treasury funds.

**A Quantitative Model of
Limited Arbitrage in Currency Markets: Theory and
Estimation**

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.

Publication

**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.

Teaching

FNCE 3030 - Investment
and Portfolio Management (Spring 2023, Fall 2023)