Hedge Fund Strategies, PerformanceDiversification: A Portfolio Theory & Stochastic Discount Factor Approach

2021 ◽  
pp. 101000
Author(s):  
David Newton ◽  
Emmanouil Platanakis ◽  
Dimitrios Stafylas ◽  
Charles Sutcliffe ◽  
Xiaoxia Ye
2019 ◽  
Author(s):  
David Newton ◽  
Emmanouil Platanakis ◽  
Dimitrios Stafylas ◽  
Charles M. Sutcliffe ◽  
Xiaoxia Ye

2016 ◽  
Vol 51 (1) ◽  
pp. 231-257 ◽  
Author(s):  
Haitao Li ◽  
Yuewu Xu ◽  
Xiaoyan Zhang

AbstractWe study hedge fund performance evaluation under the stochastic discount factor framework of Farnsworth, Ferson, Jackson, and Todd (FFJT). To accommodate dynamic trading strategies and derivatives used by hedge funds, we extend FFJT’s approach by considering models with option and time-averaged risk factors and incorporating option returns in model estimation. A wide range of models yield similar conclusions on the performance of simulated long/short equity hedge funds. We apply these models to 2,315 actual long/short equity funds from the Lipper TASS database and find that a small portion of these funds can outperform the market.


2002 ◽  
Vol 6 (2) ◽  
pp. 242-265 ◽  
Author(s):  
Martin Lettau ◽  
Harald Uhlig

We use a log-normal framework to examine the effect of preferences on the market price for risk, that is, the Sharpe ratio. In our framework, the Sharpe ratio can be calculated directly from the elasticity of the stochastic discount factor with respect to consumption innovations as well as the volatility of consumption innovations. This can be understood as an analytical shortcut to the calculation of the Hansen–Jagannathan volatility bounds, and therefore provides a convenient tool for theorists searching for models capable of explaining asset-pricing facts. To illustrate the usefulness of our approach, we examine several popular preference specifications, such as CRRA, various types of habit formation, and the recursive preferences of Epstein–Zin–Weil. Furthermore, we show how the models with idiosyncratic consumption shocks can be studied.


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