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.
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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