Abstract:We survey articles covering how hedge funds returns are explained, using largely nonlinear multifactor models that examine the non-linear pay-offs and exposures of hedge funds. We provide an integrated view of the implicit factor and statistical factor models that are largely able to explain the hedge fund return-generating process. We present their evolution through time by discussing pioneering studies that made a significant contribution to knowledge, and also recent innovative studies that examine hedge fu… Show more
“…With increased forecast accuracy, their models could select portfolios that significantly outperform the benchmark. Stafylas et al, provided an integrated view of the implicit and statistical factor models [7]. They found out a few exposures that were valid for nearly every hedge fund strategy, such as macroeconomic risk.…”
After the 2008 financial crisis, hedge funds regained their popularity. Investors naturally wonder whether it is possible to predict and explain hedge fund returns just as its constituents. To answer this question, we examined hedge fund performance of 14 strategies from 2000 to 2017 by separating them into 3 groups. After deriving a statistical model, we applied it to the period of 2017-2022 and examined the errors. We observed that most strategies have a positive risk-adjusted rate of return and the current periods returns have a positive relationship with the previous periods. We concluded that monthly return has too much randomness while 3 strategies yearly returns in the middle quantile could be predicted. More historical return data can improve the accuracy of the model.
“…With increased forecast accuracy, their models could select portfolios that significantly outperform the benchmark. Stafylas et al, provided an integrated view of the implicit and statistical factor models [7]. They found out a few exposures that were valid for nearly every hedge fund strategy, such as macroeconomic risk.…”
After the 2008 financial crisis, hedge funds regained their popularity. Investors naturally wonder whether it is possible to predict and explain hedge fund returns just as its constituents. To answer this question, we examined hedge fund performance of 14 strategies from 2000 to 2017 by separating them into 3 groups. After deriving a statistical model, we applied it to the period of 2017-2022 and examined the errors. We observed that most strategies have a positive risk-adjusted rate of return and the current periods returns have a positive relationship with the previous periods. We concluded that monthly return has too much randomness while 3 strategies yearly returns in the middle quantile could be predicted. More historical return data can improve the accuracy of the model.
“…Jackwerth PAGE 4 OF 24 and Slavutskaya (2016) assess the addition of alternative assets to pension fund portfolios in terms of the total benefit derived from diversification, addition of positive skewness, and the elimination of left tails of returns. Stafylas et al (2017) provide an integrated view of the implicit factors and statistical factor models that are largely able to explain the hedge fund return-generating process. Joenväärä et al (2019) re-examine the fundamental questions regarding hedge fund performance and find a significant association between fund-characteristics related to share restrictions as well as compensation structure and risk-adjusted returns.…”
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<p>This paper presents a model to use market indices to predict the daily net asset value of hedge funds that are not market observable. The model allows financial market participants to produce a daily mark-to-model for their hedge fund positions, and eventually options derived thereon. We use the historical data of indices to generate a robust estimate of the required index weighting parameters. Empirical study shows that the model makes reasonable predictions when appropriate index choices have been made and produces diagnostic information that can indicate the relative reliability of predicted daily hedge fund returns.</p>
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