Hedge fund return higher moments over the business cycle

Racicot, François-Éric; Théoret, Raymond

doi:10.1016/j.econmod.2018.08.016

Cited by 23 publications

(11 citation statements)

References 58 publications

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“…Many studies find that stock return predictability is sensitive to business cycles, as the variation in economic conditions will influence investor behavior and will eventually drive stock returns (Andrei et al , 2019; Racicot and Théoret, 2019; Wen and Li, 2020). Moreover, the relationship between higher moments contained in hedge funds and future returns has an asymmetric effect over the business cycles (Gregoriou et al , 2020; Racicot and Théoret, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…By providing a useful return predictor in the form of realized skewness, we significantly extend our understanding of industry return distributions and industry investment. [1] Second, recent studies find that many return predictors entail asymmetric effects over business cycle recessions and expansions (Andrei et al, 2019;Gregoriou et al, 2020;Racicot and Théoret, 2019). Although Gregoriou et al (2020) and Racicot and Théoret (2019) consider the relationship between systematic higher moment risks contained in hedge funds and economic conditions, to our best knowledge none has considered the predictive role of industry higher moments over business cycles.…”

Section: Introductionmentioning

confidence: 99%

“…Second, recent studies find that many return predictors entail asymmetric effects over business cycle recessions and expansions (Andrei et al , 2019; Gregoriou et al , 2020; Racicot and Théoret, 2019). Although Gregoriou et al (2020) and Racicot and Théoret (2019) consider the relationship between systematic higher moment risks contained in hedge funds and economic conditions, to our best knowledge, none has considered the predictive role of industry higher moments over business cycles. Motivated by these results, we test the relationships between industry higher moments and returns over business cycles.…”

Section: Introductionmentioning

confidence: 99%

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Higher moments and US industry returns: realized skewness and kurtosis

Chen

Worthington

2021

RAF

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Purpose The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies. Design/methodology/approach Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships. Findings Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns. Research limitations/implications The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor. Practical implications Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking. Originality/value While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Higher moments and US industry returns: realized skewness and kurtosis

Chen

Worthington

2021

RAF

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“…Specifically, both gold and crude oil prices are inversely related with clean energy stock indices [14]. This suggests a negative association between gold (crude oil) prices and clean energy stock indices and potentially a positive association or cointegration between gold and crude oil markets (among other authors, Narayan et al [45] find evidence of cointegration between the gold and oil markets). It follows that increases in gold prices are positively associated with the hedge portfolio returns.…”

Section: Drivers Of the Hedge Portfolio Returnsmentioning

confidence: 99%

Hedging Strategies of Green Assets against Dirty Energy Assets

2020

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Previous studies argue that the relationships between clean/green and dirty energy assets are time-varying, but there is a lack of evidence on the hedging ability of clean energy stocks and green bonds for dirty assets, such as crude oil and an energy stock index exchange traded fund (ETF), and the portfolio implications. Furthermore, potential drivers of the dynamics of the hedge portfolio returns are still unknown. To address these research gaps, the authors provide an extensive analysis of the hedging ability of clean/green assets against two dirty energy assets (crude oil prices and energy ETF) using daily data from 3 January 2012 to 29 November 2019. Using corrected dynamic conditional correlation models, the authors model correlation and then compute hedge ratios and hedging effectiveness, which all seem to vary with time. The results from hedging effectiveness indicate that investors should follow a dynamic hedging strategy and that clean energy stocks are more effective hedge than green bonds, especially for crude oil. The application of regression analyses shows that the implied volatilities of US equities and crude oil as well as US dollar index have a negative impact on the hedge portfolio returns, whereas gold prices and inflation have a positive impact.

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“…They model volatility regime changes in the US market index by using a Markov switching model for the 1994-2009 period and find that conditional exposure to traditional location factors tends to decrease in Standard & Poor's (S&P) 500 down states relative to tranquil market states. By also relying on a Markov regime-switching process, Racicot and Théoret (2019) shows that higher moment risk is conditional on VIX. Hasanhodzic and Lo (2007), Kuenzi and Shi (2007), and McGuire et al (2005) use rollingwindow regression-based analyses to estimate fund managers' current market exposures.…”

Section: Dynamic Pricing Modelsmentioning

confidence: 99%

The macroeconomic drivers in hedge fund beta management

Lambert

Platania

2020

Economic Modelling

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We investigate how macroeconomic indicators alter the dynamic risk exposure of different hedge fund style strategies. We implement a multifactor model to estimate the unobservable timevarying risk exposure conditional to macroeconomic information and a VAR to measure the impact of macroeconomic predictors on different time horizons. Using monthly returns on a cross-section of 10 different style indices from February 1997 to August 2019, we find that, on average, macroeconomic indicators explain approximately 30%, 55%, and 75% variability of betas at 1-, 6-, and 36-months horizons, respectively. Although macroeconomic predictors play a critical role at every horizon, at 1-month the dominating effect comes from idiosyncratic shocks, which indicates that in the short run hedge fund managers mostly rely on their own reallocation signals.Moreover, consistent with the fundamental drivers of the smart beta factors, we find that interest rate level and GDP growth similarly impact hedge fund exposures across styles.

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Hedge fund return higher moments over the business cycle

Cited by 23 publications

References 58 publications

Higher moments and US industry returns: realized skewness and kurtosis

Higher moments and US industry returns: realized skewness and kurtosis

Hedging Strategies of Green Assets against Dirty Energy Assets

The macroeconomic drivers in hedge fund beta management

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