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
On the Moments of the
Stochastic Discount Factor (with Ian Martin)
Mean–variance analysis—one of
the dominant frameworks of financial economics—relies on an assumption
that the stochastic discount factor (SDF) has finite variance. We
present evidence that calls this assumption into question: the \(\theta\)th moment of the SDF appears to
diverge before \(\theta\) reaches two.
But we also show that variance bounds are inherently unstable:
regardless of sample size, they have poor statistical properties even in
population. The question of whether the SDF has finite variance may
therefore be unanswerable. In contrast, bounds on the \(\theta\)th moment for \(\theta \in (0,1)\), and on entropy
measures, are theoretically well-behaved, empirically stable, and supply
measures of the attractiveness of investment opportunities and of market
risk aversion. Our framework exploits a comparison between the true
return distribution, which we infer from realized returns, and the
risk-neutral return distribution, which we observe via option
prices.
Forecasting Crashes with a
Smile (with Ian Martin)
Jack Treynor Prize
We derive option-implied bounds on
the probability of a crash in an individual stock, and argue a priori
that the lower bound should be close to the truth. The lower bound
successfully forecasts crashes both in and out of sample. Crucially, our
theory-based approach avoids the “crying wolf” problem faced by
risk-neutral crash probabilities, which severely overstate crash risk
during crisis periods. Despite having no free parameters, the lower
bound outperforms elastic net, ridge, and Lasso models that flexibly but
atheoretically combine stock characteristics, risk-neutral probabilities
and the bound itself, because such models overfit during crisis periods.
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.
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
Model
Uncertainty in the Cross Section of Stock Returns (with Jiantao Huang)
Journal of Econometrics, accepted
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
Teaching
FNCE 3030 - Investment and Portfolio Management (Spring 2023, Fall 2023, 2024) FNCE 7020 Research Topics (Empirical Asset Pricing) (Spring 2024)