Volatility Dynamics and Seasonality in Energy Prices: Implications for Crack-Spread Price Risk

Suenaga, Hiroaki; Smith, Aaron

doi:10.5547/issn0195-6574-ej-vol32-no3-2

Cited by 21 publications

(10 citation statements)

References 20 publications

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“…He also proposed a long-short trading strategy that was able to outperform the buy & hold strategy. Suenaga and Smith (2011) investigated the oil, gasoline and heating oil seasonality from the demand and storage cycle point of view. A study from Arendas (2017a) that investigated the presence of the Halloween effect in various segments of the financial markets discovered that although the oil market is not affected by the Halloween effect, the Brent, WTI, as well as Fateh oil prices tend to deliver notably better returns during the summer (May -October) than during the winter (November -April) half of the year.…”

Section: Literature Reviewmentioning

confidence: 99%

Seasonal patterns in oil prices and their implications for investors

Árendáš

Tkacova

Bukoven

2018

Journal of International Studies

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This paper focuses on the investigation of oil price seasonal patterns and their exploitability in the investment process. As oil is one of the most important and most traded commodities, a successful investment strategy exploiting seasonal patterns in oil price behaviour may be useful for many retail as well as institutional investors. The results show that during the 1983-2017 period, the Brent and WTI oil prices tended to record abnormally positive returns during the months of March, April and August and abnormally negative returns -during the months of October and November. The analysis also shows that simple investment strategies based on switching between the oil market and money market investments, that are utilizing the oil price seasonal patterns, can outperform in the long term.

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Section: Literature Reviewmentioning

confidence: 99%

Seasonal patterns in oil prices and their implications for investors

Árendáš

Tkacova

Bukoven

2018

Journal of International Studies

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“…In this model, daily return of a futures contract is decomposed into the common latent factors and an idiosyncratic term. The model, as applied to the NYMEX energy futures contracts in Suenaga and Smith (2011), is expressed in the following form,…”

Section: Models Of Price Return (Pots Model)mentioning

confidence: 99%

“…This study examines the conventional term-structure models of commodity prices through comparing them with a model of daily futures returns. In this alternative model, I follow the same approach as Smith (2005) and Suenaga and Smith (2011) and specify factor loadings directly by flexible, non-parametric functions, rather than determining them by a small number of parameters characterizing the temporal dynamics of the underlying stochastic factors. These flexible functions allow the model to replicate highly non-linear price dynamics of commodities with significant storage costs and strong seasonality in demand and/or supply.…”

Section: Introductionmentioning

confidence: 99%

Misspecification in term structure models of commodity prices: Implications for hedging price risk

Suenaga¹

2011

Chan, F., Marinova, D. And Anderssen, R.S. (Eds) MODSIM2011, 19th International Congress on Modelling and Simulation.

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Stochastic dynamics of commodity prices and valuation of derivative contracts have long been studied in the field of financial economics. In the literature a common approach is to specify a stochastic dynamics of the underlying assets and derive from the suggested model valuation formulas of various derivative contracts whose payoff depends on the realization of the underlying asset value. Recently, models with additional latent factors and more flexible stochastic process of each factor have been suggested. Although these complex models tend to fit better to the observed data, it is often understated that this modeling approach only approximates the true stochastic process of the underlying asset values. This approximation bias can be substantial in magnitude for a storable commodity with significant demand and/or supply seasonality, for which equilibrium path of spot and futures prices cannot be expressed in reduced form, as shown by the theory of storage.This study examines conventional term-structure models of commodity prices. In particular, this study quantifies the approximation bias of these conventional models through comparing them with an alternative approach of modeling the variance of daily futures returns directly by flexible non-parametric functions so as to allow the model to replicate highly non-linear price dynamics of storable commodities. Empirical applications of the models reveal that, for all four commodities (gold, crude oil, natural gas, and corn) examined in this study, the volatility of daily futures prices is more complex than the pattern as implied by the dynamics stipulated in the conventional term-structure models. In particular, all four commodities exhibit a strong time-to-maturity effect as well as a significant seasonal pattern in both levels and compositions (among two factors and idiosyncratic errors) of volatility of daily futures returns. Conventional termstructure models draw incorrect portraits of volatility dynamics as well as incorrect correlation among concurrently traded contracts with different maturity dates, which lead these models to suggest hedging strategies that are considerably less effective than the strategy based on the model of futures returns.

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“…An alternative approach to modeling a term structure of commodity prices, as recently introduced by Smith (2005) and later extended by Suenaga and Smith (2011), is to model directly the dynamics of futures curve. In this model, daily futures returns is decomposed into a set of common stochastic factors affecting all futures returns and an idiosyncratic term.…”

Section: Introductionmentioning

confidence: 99%

A flexible model of term-structure dynamics of commodity prices: a comparative analysis with a two-factor Gaussian model

Suenaga

2013

Quantitative Finance

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This study compares two approaches to modeling a term structure of commodity prices. The first approach specifies the stochastic process of the underlying spot price and derives from the stipulated spot price dynamics valuation formulas of futures and other derivative contracts through no arbitrage. The second approach, as introduced by Smith (2005), is to model the dynamics of the entire futures curve directly by a set of common stochastic factors and to specify factor loadings by flexible functions of time-to-maturity and contract delivery month. Empirical applications of the models to four commodities (gold, crude oil, natural gas, and corn) reveal that the volatility of futures prices exhibits more complex dynamics than the pattern implied by the model stipulating a two-factor Gaussian process of the underlying spot price. Specifically, the flexible model of futures returns depicts the maturity effect and, particularly for the three consumption commodities, strong seasonal and cross-sectional variations in variance and covariances of concurrently traded contracts. Incorporating the depicted variance and covariance dynamics leads the flexible model of futures returns to suggest hedging strategies that are more effective than the strategies based on the conventional two-factor term-structure model.

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Volatility Dynamics and Seasonality in Energy Prices: Implications for Crack-Spread Price Risk

Cited by 21 publications

References 20 publications

Seasonal patterns in oil prices and their implications for investors

Seasonal patterns in oil prices and their implications for investors

Misspecification in term structure models of commodity prices: Implications for hedging price risk

A flexible model of term-structure dynamics of commodity prices: a comparative analysis with a two-factor Gaussian model

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