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
Convexity Bounds for the Stochastic Discount Factor: Is
the Singularity Near? (with Ian Martin)
We derive new families of bounds on the moments of the stochastic
discount factor (SDF). Our results generalize existing bounds on the
variability of the SDF which exploit risk-adjusted measures of
investment opportunities—such as Sharpe ratios or expected log
returns—that are maximized in the cross-section, across assets. By
contrast, we can fix a single asset and optimally exploit information in
its true and risk-neutral return distributions. We apply the framework
to the S&P 500 index, which has an a priori motivation as an asset
of interest and hence avoids concerns about in-sample data snooping. Our
theory indicates that the second moment—and all higher moments—of the
SDF may not exist, and the empirical evidence based on the S&P 500
index supports these conjectures. These patterns can emerge in
equilibrium models with parameter learning or heterogeneous beliefs.
Conditional
Asset Pricing with Text-Managed Portfolios (with Jian Feng, Jiantao Huang and
Shiyang Huang)
We construct managed portfolios that exploit information extracted
from firms’ earnings call transcripts and examine their asset pricing
implications. Returns on these text-managed portfolios correlate with
investor sentiment and predict macroeconomic outcomes. Individual
stocks’ exposures to the text-managed portfolios explain as much return
variation as those to the characteristics-sorted portfolios. Adding
earnings call information to firm characteristics increases
mean-variance efficiency, though it does not improve stock-level return
predictability. Consistent with the insights from Kozak and Nagel (2024)
on mean-variance spanning, our results suggest that earnings calls
provide information about return covariances beyond what is captured by
firm characteristics alone.
Forecasting Crashes with a
Smile (with Ian Martin)
Jack Treynor Prize
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, we find
that the lower bound is a highly successful predictor of crashes, both
in and out of sample; 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.
Model
Uncertainty in the Cross Section of Stock Returns (with Jiantao Huang)
We develop a
transparent Bayesian framework to measure uncertainty in asset pricing
models. By assigning a modified class of \(g\)-priors to the risk prices of asset
pricing factors, our method quantifies the trade-off between
mean-variance efficiency and parsimony for asset pricing models to
achieve high posterior probabilities. Model uncertainty is defined as
the entropy of these model probabilities. We prove the model selection
consistency property of our procedure, which is missing from the classic
\(g\)-priors. Acknowledging the
possibility of omitting true asset pricing factors in real applications,
we also characterize the maximum degree of contamination that the
omitted factors can introduce to our model uncertainty measure.
Empirically, we find that model uncertainty escalates during major
market events and carries 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.
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 the 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, 2024) FNCE 7020 Research Topics (Empirical Asset Pricing) (Spring 2024)