Working Papers

Fund Convexity and Tail Risk-Taking [PDF] (Job Market Paper)

This paper studies how a fund manager takes skewed bets in two dimensions. First, the fund manager
constantly reexamines fund performance relative to her peers and takes a position with respect to
skewness risk. I show that when a fund manager underperforms her peers, she will gamble on trades
with lottery-like returns. On the other hand, when a fund outperforms peer funds, the fund manager will
take negatively skewed trades to improve performance at the expense of significant downside risk. The
results are robust to different econometric specifications. Second, I examine how convexity in
incentives impacts tail risks across and within different types of investment funds. The literature has
documented different forms of convexity that a fund manager faces: discounts in closed-end
funds, tournaments and fund flow-performance relation in open-ended funds, and high water mark
provisions in hedge funds. Sorting funds by the degree of convexity and comparing skewness between
the group with the most convexity and the group with the least convexity, I conclude that convexity
impacts fund tail risks. This result suggests that both implicit and explicit convexity provide incentives
for fund managers to take systematic and idiosyncratic bets with tail risks.

Alpha and Performance Measurement: The Effect of Investor Heterogeneity, with Wayne Ferson                   (Revise and Resubmit, Journal of Finance)

Studies of investment performance routinely use various measures of alpha, yet the literature has not
established that a positive (negative) alpha, as traditionally measured, means that an investor would want
to buy (sell) a fund. However, under general conditions, when alpha is defined using the client's
marginal utility function, a client faced with a positive alpha would want to buy the fund. Thus,
performance measurement is inherently investor specific, and investors will disagree about the
attractiveness of a given fund. We provide empirical evidence that bounds the effects of investor
heterogeneity on performance measures, and study the cross sectional effects of disagreement on
investors’ flow response to past fund performance. The effects of investor heterogeneity are
economically and statistically significant.

Tail Risks across Investment Funds [SSRN] [PDF]

Managed portfolios are subject to tail risks, which can be either index level (systematic) or fundspecific.
Examples of fund-specific extreme events include those due to big bets or fraud. This paper
studies the two components in relation to compensation structure in managed portfolios. A simple
model generates fund-specific tail risk and its asymmetric dependence on the market, and makes
predictions for where such risks should be concentrated. The model predicts that systematic tail risks
increase with an increased weight on systematic returns in compensation and idiosyncratic tail risks
increase with the degree of convexity in contracts. The model predictions are supported with empirical
results. Hedge funds are subject to higher idiosyncratic tail risks and Exchange Traded Funds exhibit
higher systematic tail risks. In skewness and kurtosis decompositions, I find that coskewness is an
important source for fund skewness, but fund kurtosis is driven by cokurtosis, as well as volatility
comovement and residual kurtosis, with the importance of these components varying across fund types.
Investors are subject to different sources of skewness and fat tail risks through delegated investments.
Volatility based tail risk hedging is not effective for all fund styles and types.

Do Fund Managers Manage Tail Risks?, with Yun Ling

The literature has introduced various higher moment factors and their relative importance in asset
pricing. Given that fund returns exhibit tail risk exposures and beta-timing and stock selection can
induce different types of covariance risks of higher moments, we examine how fund managers manage
tail risks in view of investors. We first include the higher moment factors on top of three baseline
models - the CAPM, the Carhart four factor model, the CAPM plus the coskewness factor, and the
Carhart four factor model plus the coskewness factor. We show that introducing these higher moments
can improve R squares and shift the distribution of alphas. This confirms the importance of higher
moment factors in performance measures. We further show that higher moment policies that fund
managers adopt persist over time and differ across fund types. This implies that fund managers do
manage tail risks over time and fund characteristics may induce different managerial behavior on tail


Work in Progress

Tail Risk-Taking with Non-linearity and Higher Moments

Flow-induced Tail Risk, with Haitao Mo