**Publications**

l _..Timescale
Betas and the Cross Section of Equity Returns: Framework, Application, and
Implications for Interpreting the Fama-French Factors (with Francis In and Tong Suk Kim), 2017, ** Journal
of Empirical Finance** 42, 15-39.

o
We show that standard beta pricing models quantify an
asset's systematic risk as a weighted combination of a number of different
timescale betas. Given this, we develop a wavelet-based framework that examines
the cross-sectional pricing implications of isolating these timescale betas. An
empirical application to the Fama-French model reveals that the model's well-known
empirical success is largely due to the beta components associated with a
timescale just short of a business cycle (i.e., wavelet scale 3). This implies
that any viable explanation for the success of the Fama-French model that has
been applied to the Fama-French factors should apply particularly to the scale
3 components of the factors. We find that a risk-based explanation conforms
closely to this implication.

l Prime Broker-Level Comovement in Hedge Fund Returns:
Information or Contagion? (with Ji-Woong
Chung), 2016, ** Review of Financial Studies **29, 3321-3353.

o
.We document
strong comovement in the returns of hedge funds sharing the same prime broker.
This comovement is driven neither by funds in the same family nor in the same
style, and it is distinct from market-wide and local comovement. The common
information hypothesis attributes this phenomenon to the prime broker providing
valuable information to its hedge fund clients. The prime broker-level
contagion hypothesis attributes the comovement to the prime broker spreading
funding liquidity shocks across its hedge fund clients. We find strong evidence
supporting the common information hypothesis, but limited evidence in favor of
the prime broker-level contagion hypothesis.

l _..A
Longer Look at the Asymmetric Dependence between Hedge Funds and the Equity
Markets (with Francis In, Gunky Kim,
and Tong Suk Kim), 2010, **Journal of Financial and Quantitative
Analysis*** *45,
763-789.

o
.This paper
reexamines, at a range of investment horizons, the asymmetric dependence
between hedge fund returns and market returns. Given the current availability
of hedge fund data, the joint distribution of longer-horizon returns is
extracted from the dynamics of monthly returns using the filtered historical
simulation; we then apply the method based on copula theory to uncover the
dependence structure therein. While the direction of asymmetry remains
unchanged, the magnitude of asymmetry is attenuated considerably as the
investment horizon increases. Similar horizon effects also occur on the tail
dependence. Our findings suggest that nonlinearity in hedge fund exposure to
market risk is more short term in nature, and that hedge funds provide higher
benefits of diversification, the longer the horizon.

**Working Papers**

l _..Asymmetric Dependence, Tail Dependence, and the Time
Interval over which the Variables Are Measured (with Gunky Kim), 2014, presented at 2013 SFM, 2014
SoFiE/INET, JBF conference on financial econometrics

o
.The effect
of time interval on the linear correlation coefficient between random variables
is well documented in the literature. In this paper, we investigate the time
interval effect on asymmetric dependence and tail dependence between random
variables. We prove that when two random variables are characterized by
asymmetric dependence (in any direction), the magnitude of asymmetry in their
dependence structure decreases monotonically as the time interval increases,
approaching zero (i.e., symmetry) in the limit. Also, when two random variables
exhibit tail dependence, their tail dependence decreases monotonically as the
time interval increases, approaching zero (i.e., tail independence) in the
limit. Our results hold regardless of whether the variables are both additive,
both multiplicative, or one is additive and the other is multiplicative.

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