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We test the new Fama and French five-factor model on a sample of hedge fund strategies. This model embeds the q-factor asset pricing model which lies on the CMA and RMW factors. We find that the HML factor is not redundant for many strategies, as conjectured by Fama and French in their setting. HML seems to embed risk dimensions which are not included in the Fama and French new factors. In contrast to Fama and French, the α puzzle is robust to the addition of CMA and RMW. Furthermore, hedge funds seem to prefer, on the one hand, firms which invest a lot to firms which invest less, and, on the other hand, weak firms over robust ones. Finally, our results are not sensitive to the addition of the Fung and Hsieh seven-factor model. However, the explanatory power of the eleven-factor model is much higher for some hedge fund strategies involved in arbitrage.

This article uses a parsimonious and robust instrumental variables technique to minimize the specification errors in the Pastor-Stambaugh (PS) empirical model. In particular, we use an improvement of Hansen’s generalized method of moments (GMM) that uses higher moments that are robust instruments. We also propose a robustness test for these instruments. Results with these instruments indicate that the liquidity measure used in the PS empirical model is improperly measured and/or is ill-conceived. Although this article applies a GMM framework to a financial application, this technique is applicable to estimation problems in the presence of specification errors in all areas of quantitative finance.

Most of traded double barrier options are monitored in discrete time, their pricing being more challenging than in continuous time. A few solutions are analytical with a correction for continuity; the remaining solutions are numerical with lattices, grids or Monte Carlo (MC) simulation. We apply an original variance reduction technique to the pricing of European double barrier options monitored in discrete time. This technique speeds up significantly the MC simulation. The computational algorithm repeats each experiment with an increasing number of trials at a logarithmic rate and calculates a weighted average of the options values. We test the accuracy of the Login (Logarithmic Increment) model with an analytical solution adjusted for discretization and a naive numerical model using Monte Carlo simulation. We show that the Login model accurately prices double barrier options. Market participants in need of selecting a reliable numerical method for pricing double barrier options monitored in discrete time will find our article appealing. Moreover, the idea behind the method is simple and can be applied to the pricing of plain-vanilla or more complex derivatives, easing and speeding the valuation step significantly.

Estimation of financial models of hedge fund returns often gives rise to abnormally high alphas. This phenomenon may be due to mis-specified models, but recently the introduction of volatility factors in hedge fund returns models (Kuenzi and Xu, 2007) has been viewed as a solution to solve the alpha puzzle. This paper shows that modelling the volatility of the innovation term of hedge fund return models might be another way to explain the alpha puzzle. The model proposed in this paper is a factor model that incorporates an 'alternative' factor, which is the return of a short put on the Standard & Poor's 500 whose volatility is the VIX. This paper takes an overall view to analyse the problem of specification errors in financial models. To account for specification errors, it proposes a new estimator based on the generalised method of moments (GMM) whose instruments are the higher moments of returns, the GMM-hm. Some transformations of the basic factor model, which are recommended in the financial literature to improve the estimation of the alpha, are considered the n-CAPM and the conditional model. Some GARCH and EGARCH specifications of our basic model of returns are also estimated. Our results show that modelling the volatility of the innovation is the best way to lower the alpha. The n-CAPM has also had some success in reducing the alpha. The alpha is not the only garbage bag of a returns model (Jaeger and Wagner, 2005), but the alpha and the innovation both constitute the garbage bag.