Tail risk in the hedge fund industry

The dissertation goal is to quantify the tail risk premium embedded into hedge funds' returns. Tail risk is the probability of extreme large losses. Although it is a rare event, asset pricing theory suggests that investors demand compensation for holding assets sensitive to extreme market downturns. By de nition, such events have a small likelihood to be represented in the sample, what poses a challenge to estimate the e ects of tail risk by means of traditional approaches such as VaR. The results show that it is not su cient to account for the tail risk stemming from equities markets. Active portfolio management employed by hedge funds demand a speci c measure to estimate and control tail risk. Our proposed factor lls that void inasmuch it presents explanatory power both over the time series as well as the cross-section of funds' returns.

Precious metals, a shiny hedge for investors?

Recorrendo a duas abordagens diferentes, regressão e correlação, e cobrindo os últimos vinte anos de dados diários para sete países, esta tese investiga as propriedades “safe haven” e “hedge” dos metais preciosos, em comparação com acções internacionais para um dado estado da economia. Adicionalmente, esta tese avalia o desempenho de diferentes portfolios, dentro e fora da amostra, com o objectivo de verificar se o investimento em metais preciosos poderá ajudar a atenuar a gestao do risco por parte do investidor. Os principais resultados são os que se seguem: (i) O ouro é o melhor metal precioso para um “hedging” internacional em oposição às acções (ii) O ouro permite obter valiosos benefícios de gestão de risco do portfolio (iii) 60/40 dos portofios atribuidos com ouro permitem ao investidor obter bons resultados.

An SDF approach to hedge funds’ tail risk: evidence from Brazilian funds

The main purpose of this paper is to propose a methodology to obtain a hedge fund tail risk measure. Our measure builds on the methodologies proposed by Almeida and Garcia (2015) and Almeida, Ardison, Garcia, and Vicente (2016), which rely in solving dual minimization problems of Cressie Read discrepancy functions in spaces of probability measures. Due to the recently documented robustness of the Hellinger estimator (Kitamura et al., 2013), we adopt within the Cressie Read family, this specific discrepancy as loss function. From this choice, we derive a minimum Hellinger risk-neutral measure that correctly prices an observed panel of hedge fund returns. The estimated risk-neutral measure is used to construct our tail risk measure by pricing synthetic out-of-the-money put options on hedge fund returns of ten specific categories. We provide a detailed description of our methodology, extract the aggregate Tail risk hedge fund factor for Brazilian funds, and as a by product, a set of individual Tail risk factors for each specific hedge fund category.