Convenience yield in commodity price modeling: A regime switching approach

Almansour, Abdullah

doi:10.1016/j.eneco.2014.06.016

Cited by 15 publications

(21 citation statements)

References 26 publications

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“…This variable is defined as the difference between the second and the first nearby futures contracts and is adjusted for a risk‐free interest rate, as follows:

{italiciab}_{italictT} = \frac{F (t, 2) - F (t, 1) (1 + r (t, T))}{F (t, 1)},

where

{iab}_{tT}

is the 2‐month interest‐adjusted basis (adjusted basis henceforth); and F ( t , 1) and F ( t , 2) are the first and the second nearby futures prices. The reason for choosing the second nearby contract rather than the contracts with longer maturity dates is that closer delivery futures contracts are more liquid and are expected to be priced with minimal error (Almansour, 2016). The futures data that we use are the losing futures prices of West Texas Intermediate (WTI), traded on NYMEX, and they are measured in U.S. dollars per barrel.…”

Section: Methodsmentioning

confidence: 99%

The theory of storage in the crude oil futures market, the role of financial conditions

Ahmadi

Behmiri

Manera

2020

Journal of Futures Markets

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This study examines the impacts of inventory and financial instability on the basis of the crude oil market. The results show that, first, the basis rises with inventory, and this effect is higher during low inventory regimes. This validates the theory of storage in the crude oil market. Second, the basis rises with financial instability, and this effect is higher during turbulent regimes. These results warn the oil market participants that, to make decisions based on the basis variation, traditionally known as a signal of scarcity or abundance, the underlying cause of the variation has to be considered.

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“…This variable is defined as the difference between the second and the first nearby futures contracts and is adjusted for a risk‐free interest rate, as follows:

{italiciab}_{italictT} = \frac{F (t, 2) - F (t, 1) (1 + r (t, T))}{F (t, 1)},

where

{iab}_{tT}

Section: Methodsmentioning

confidence: 99%

The theory of storage in the crude oil futures market, the role of financial conditions

Ahmadi

Behmiri

Manera

2020

Journal of Futures Markets

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“…We also carry out an out-of-sample exercise by using the Kalman filter to obtain one-step ahead forecasts and compare the in-sample and outof-sample performance of the two versions of our model against the two-factor model in Schwartz and Smith (2000). 2 Both exercises confirm the superiority of the Fourier model to fit market data as well as to forecast futures prices. Finally, we analyse the economic factors driving the evolution of the long-term swing process.…”

Section: Introductionmentioning

confidence: 78%

“…Section 3 presents the empirical analysis, where subsections 3.3 and 3.4 present the in-sample and out-of-sample exercises, 1 We make no distinction between futures and forward agreements. 2 As described in the academic literature, most multi-factor models are hardly comparable to our model as they propose a stochastic convenience yield or a jump diffusion process. Schwartz and Smith (2000) is the most popular two factor model and it has been chosen as a natural benchmark in many papers, see for instance, Manoliu and Tompaidis respectively, and subsection 3.5 presents the economic determination of the long-term swing process.…”

Section: Introductionmentioning

confidence: 94%

“…Fouquau and Six (2015) prove theoretically that the two methods commonly used to obtain the convenience yield (filtered from derivative prices or using the interest-adjusted basis an observable proxy) do not provide the same results. Almansour (2016) extended the Gibson and Schwartz (1990) model to allow for regime switching and to account for shifts in the futures term structure. See Lai and Mellios (2016) and references 2 therein for different analysis of the convenience yield in commodity pricing.…”

Section: Introductionmentioning

confidence: 99%

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Long-term swings and seasonality in energy markets

Moreno

Novales

Platania

2019

European Journal of Operational Research

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This paper introduces a two-factor continuous-time model for commodity pricing under the assumption that prices revert to a stochastic mean level, which shows smooth, periodic fluctuations over long periods of time. We represent the mean reversion price by a Fourier series with a stochastic component. We also consider a seasonal component in the price level, an essential characteristic of many commodity prices, which we represent again by a Fourier series. We obtain analytical pricing expressions for futures contracts. Using futures price data on Natural Gas, we provide evidence on the presence of long-term fluctuations and show how to estimate the long-term component simultaneously with a seasonal component using the Kalman filter. We analyse the in-sample and out-of-sample empirical performance of our pricing model with and without a seasonal component and compare it with the Schwartz and Smith (2000) model. Our findings show the in-sample and out-of-sample superiority of our model with seasonal fluctuations, thereby providing a simple and powerful tool for portfolio management, risk management, and derivative pricing. AbstractThis paper introduces a two-factor continuous-time model for commodity pricing under the assumption that prices revert to a stochastic mean level, which shows smooth, periodic fluctuations over long periods of time. We represent the mean reversion price by a Fourier series with a stochastic component. We also consider a seasonal component in the price level, an essential characteristic of many commodity prices, which we represent again by a Fourier series. We obtain analytical pricing expressions for futures contracts. Using futures price data on Natural Gas, we provide evidence on the presence of long-term fluctuations and show how to estimate the long-term component simultaneously with a seasonal component using the Kalman filter. We analyse the in-sample and out-of-sample empirical performance of our pricing model with and without a seasonal component and compare it with the Schwartz and Smith (2000) model. Our findings show the in-sample and out-of-sample superiority of our model with seasonal fluctuations, thereby providing a simple and powerful tool for portfolio management, risk management, and derivative pricing.

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“…They found that output shocks shifted to the low volatility regime around 1985 and inflation shocks shifted to the low volatility regime around 1990. Focusing on the convenience yield and employing a regime switching framework, Almansour (2016) modeled the crude oil and natural gas futures term structures. Specifically, this study extended the model of Gibson and Schwartz (1990) to allow for regime switching behavior in the convenience yield.…”

Section: Recent Literature Reviewmentioning

confidence: 99%

Corporate Investment and Portfolio Returns in Japan: A Markov Switching Approach

Tsuji¹

2018

JMS

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This paper explores the profitability of four Japanese higher return equity portfolios and their linkages between corporate investment factor return, the so-called conservative-minus-aggressive (CMA), suggested by Fama and French (2015). Our empirical examinations derive the following evidence. First, in the four Japanese equity portfolios, the smallest and the highest operating profitability portfolio presents the highest return. Second, the smallest and the highest book-to-market (B/M) portfolio, the smallest and moderate investment portfolio, and the smallest and the second strongest momentum portfolio also record higher excess returns than the overall equity market in Japan. Moreover, our analyses via two-regime Markov switching models evidence that for all the four Japanese equity portfolios, there are clearly two regimes: one is positively related to CMA and the other is little or negatively related to CMA. Furthermore, our analyses also reveal that recently, all the four Japanese equity portfolios yield higher returns than CMA with showing weaker linkages between CMA.

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Convenience yield in commodity price modeling: A regime switching approach

Cited by 15 publications

References 26 publications

The theory of storage in the crude oil futures market, the role of financial conditions

The theory of storage in the crude oil futures market, the role of financial conditions

Long-term swings and seasonality in energy markets

Corporate Investment and Portfolio Returns in Japan: A Markov Switching Approach

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