Commodity Strategies Based on Momentum, Term Structure, and Idiosyncratic Volatility

Fuertes, Ana-Marı́a; Miffre, Joëlle; Fernández-Pérez, Adrián

doi:10.1002/fut.21656

Cited by 60 publications

(32 citation statements)

References 67 publications

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“…However, strategies with J(1) = 1, 3 and 6, although profitable and statistically significant, do not outperform the respective single-sort momentum benchmarks. 22 Fuertes et al (2010) and Fuertes et al (2015), where the second (third) sorts involve variables that are exogenous to past returns (i.e. term structure and idiosyncratic volatility), our proposed double-sort strategy exploits information only based on past returns.…”

Section: Strategy Performancementioning

confidence: 99%

“…position is rolled over to the next contract in order to maintain a continuous exposure in the underlying commodity (Miffre and Rallis, 2007;Shen et al, 2007;Fuertes et al, 2010Fuertes et al, , 2015Moskowitz et al, 2012;Asness et al, 2013). This study employs continuous commodity futures price series which are pre-constructed by the respective data provider.…”

Section: Datamentioning

confidence: 99%

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Combining momentum with reversal in commodity futures

Bianchi

Drew

Fan

2015

Journal of Banking & Finance

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a b s t r a c tThis paper examines profitable trading strategies that jointly exploit momentum and reversal signals in commodity futures. While the single-sort momentum strategies returns 11.14% per annum, on average, a consistent reversal pattern of momentum profits is pronounced from 12 to 30 months after portfolio formation. Combining the observed reversal pattern with the momentum signal, our double-sort strategy returns 20.24% per annum, which significantly outperforms single-sort strategies. The proposed strategy is robust to seasonality effects and sample adjustments in commodity futures. The profitability of the double-sort strategy cannot be explained by standard risk factors, term structure, market volatility, investor sentiment, data-mining or transaction costs, but appears to be related to global funding liquidity. As a consequence, the double-sort strategy in commodity futures may be employed as a portfolio diversification tool.

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Section: Strategy Performancementioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Combining momentum with reversal in commodity futures

Bianchi

Drew

Fan

2015

Journal of Banking & Finance

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“…For monthly hedging horizon this figure changes to less than 0.38% (0.06%) per month for the NG (NBP), with the potential for this figure to go as high as 3.11% (0.25%). In commodity markets Locke and Venkatesh (1997) estimate that futures trading costs range between 0.0004% and 0.033% of notional value, while Fuertes, Miffre, and Fernandez-Perez (2015) take a more conservative view, imposing TCs of 0.033-0.066% per trade. Therefore, in addition to the economic value associated with higher HE, combination strategies yield, on average, positive fees (gains in terms of utility improvement), concluding that composite hedge ratios work well versus both individual models and the 1/N scheme.…”

Section: Bestfcmentioning

confidence: 99%

A novel risk management framework for natural gas markets

Pouliasis

Visvikis

Papapostolou

et al. 2019

Journal of Futures Markets

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This paper examines dynamic hedges in the natural gas futures markets for different horizons and explores the gains from devising risk management strategies. Despite the substantial progress made in developing hedging models, forecast combinations have not been explored. We fill this gap by proposing a framework for combining hedge-ratio predictions. Composite hedge ratios lead to significant reduction in portfolio risk, whether spot prices are partially predictable or not. We offer insights on hedging effectiveness across seasons, backwardation-contango conditions and the asymmetric profiles of long-short hedgers. We conclude that forecast combinations better reconcile realized performance with the hedging process, mitigating model instability. K E Y W O R D S dynamic futures hedging, forecast combination, natural gas J E L C L A S S I F I C A T I O N C32; C53; G11; G32; L95 3Gagnon and Lypny (1995) provide evidence in support of GARCH models. In contrast, Lien and Tse (2002) support the traditional regression approach. GARCH models exhibit few limitations. For example, the observed nonnormalities in return distributions are more pronounced than those implied by GARCH; the model fails to reproduce time variability in higher moments unless explicitly modeled, and a strong degree of persistence is imputed to volatility which may be due to structural breaks (Lamoureux and Lastrapes, 1990). POULIASIS ET AL.| 431 differ for different hedging horizons (weekly and monthly), between long and short hedges, during seasons (winter and summer) and across market conditions (backwardation and contango).Finally, we also assess hedging performance under the supposition of partially predictable spot prices. Ederington and Salas (2008) postulate that, when spot prices are partially predictable, traditional regression estimates the OHR inefficiently, leading to upward-biased riskiness levels for both hedged and unhedged positions and downward-biased risk reduction levels. Martínez and Torró (2015) consider seasonality when OHRs are computed in European gas markets. Their results indicate that hedging effectiveness is much higher when the seasonal pattern in spot price changes is approximated with the basis (futures-spot spread). Fama and French (1987) and Viswanath (1993) also find that the basis is a useful predictor of future changes in spot commodity prices.The structure of this paper is as follows. Section 2 presents the OHR methodology and demonstrates the forecast combination procedure. In Section 3, the data and their properties are described. Section 4 discusses the empirical results and evaluates the hedging effectiveness of the proposed strategies. The reality check for superior hedging ability is also discussed, while this section presents information on weight and hedge ratio stability, transaction costs, performance fees, downside risk and segmentation to market conditions. Last section concludes.How to cite this article: Pouliasis PK, Visvikis ID, Papapostolou NC, Kryukov AA. A novel risk management framework for nat...

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“…The first one is a momentum investing strategy, according to which commodities with the highest historical returns outperform the market in the future, while futures with the worst returns underperform the market (Miffre & Rallis, 2007;Gorton, Hayashi, & Rouwenhorst, 2013;Fuertes, Miffre & Rallis, 2010;Fuertes, Miffre & Fernández-Pérez, 2014). Commodity investors can profit from the momentum effect, for instance, by going long the top performers and shorting the market laggards.…”

Section: Introductionmentioning

confidence: 99%

“…The momentum effect was documented in stocks (Jegadeesh & Titman, 1993;Liew & Vassalou, 2000;Griffin, Ji, & Martin, 2003;Chui, Wei, & Titman, 2010;Fama & French, 2012;), bonds (Ansess, Moskowitz & Pedersen, 2013), currencies Kho, 1996;LeBaron, 1999) and even domestic equity markets (Asness, Liew, & Stevens, 1997;Bhojraj & Swaminathan, 2006, Zaremba & Konieczka, 2014. The profitability of momentum investing strategy in commodity markets is proved in numerous studies (Erb & Harvey, 2006, Gorton, Hayashi, & Rouwenhorst, 2013, Miffre & Rallis, 2007Gorton, Hayashi, & Rouwenhorst, 2013;Fuertes, Miffre & Rallis, 2010;Fuertes, Miffre & Fernández-Pérez, 2014). Although it is currently one of the most researched phenomenon in the field of finances, no consensus has been reached on the sources of its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Strategies Based on Momentum and Term Structure in Financialized Commodity Markets

Zaremba¹

2016

berj

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The aim of this paper is to investigate the impact of the financialization of commodity markets on the profitability of strategies based on momentum and term structure. The performance of an array of portfolios from double-sorts on non-commercial traders' participation, historical returns and term spreads are tested against a risk model. The analysis covers the listing of 26 commodities in the period 1986-2013. First and foremost, the paper provides a fresh evidence for the validity of strategies based on momentum and term structure investing in commodity markets. Secondly, it proves that term structure strategies generate significantly higher performance results in non-financialized markets. Moreover, it supports the thesis that market financialization adversely affects momentum profits. The results are important in terms of tactical and strategic asset allocation in commodity markets. They imply that investors who implement momentum or term structure based strategies should also consider the composition of market participants.

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Commodity Strategies Based on Momentum, Term Structure, and Idiosyncratic Volatility

Cited by 60 publications

References 67 publications

Combining momentum with reversal in commodity futures

Combining momentum with reversal in commodity futures

A novel risk management framework for natural gas markets

Strategies Based on Momentum and Term Structure in Financialized Commodity Markets

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