Seasonality and the valuation of commodity options

In this paper, we analyze the role of the jump size distribution in the US natural gas prices when valuing natural gas futures traded at New York Mercantile Exchange (NYMEX) and we observe that a jump-diffusion model always provides lower errors than a diffusion model. Moreover, we also show that although the Normal distribution offers lower errors for short maturities, the Exponential distribution is quite accurate for long maturities. We also price natural gas options and we see that, in general, the model with the Normal jump size distribution underprices these options with respect to the Exponential distribution. Finally, we obtain the futures risk premia in both cases and we observe that for long maturities the term structure of the risk premia is negative. Moreover, the Exponential distribution provides the highest premia in absolute value.

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“…Other authors have found seasonal patterns in commodity markets and this fact has been taken into account in their models; see [12][13][14][15].…”

Section: Abstract and Applied Analysismentioning

confidence: 99%

The Jump Size Distribution of the Commodity Spot Price and Its Effect on Futures and Option Prices

Gómez-Valle

Habibilashkary

Martínez-Rodríguez

2017

Abstract and Applied Analysis

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“…Convenience yield in a commodity is analogous to a dividend in an asset that provides a known income; and dividends would naturally lead to a corresponding adjustment in the price of the underlying asset of a futures contract. Whilst we did not explicitly model the dynamics of the convenience yield as it is not the intent of this paper to quantify it, we see that it is implicitly taken into account through the log-spot price ζ in the closed-form expression for the futures price in equation (2).…”

Section: Arbitrage-free Evolution Of Futures Pricesmentioning

confidence: 99%

“…Furthermore, at a fixed time t, F i (t) can be an increasing or a decreasing function of maturity T i , depending on the choice of parameters, which can easily be seen from equation (2). Futures prices decreasing (respectively, increasing) with maturity reflects backwardation (respectively, contango).…”

Section: Arbitrage-free Evolution Of Futures Pricesmentioning

confidence: 99%

“…A one-factor regime-switching model was developed in Chen and Forsyth [7] but the objective was to capture the risk-adjusted natural gas spot price dynamics; regression was used in model calibration using both market data on futures and options on futures. Back, et al [2] conducted an extensive analysis covering samples of soybean, corn, heating oil and natural gas options, and provided evidence that seasonality in volatility is an important aspect to consider when valuing futures contracts; an appropriate seasonality adjustment significantly reduces pricing errors in these markets and yields more improvement in valuation accuracy than increasing the number of stochastic factors.…”

Section: Introductionmentioning

confidence: 99%

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Filtering and forecasting commodity futures prices under an HMM framework

2013

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We propose a model for the evolution of arbitrage-free futures prices under a regimeswitching framework. The estimation of model parameters is carried out using the hidden Markov filtering algorithms. Comprehensive numerical experiments on real financial market data are provided to illustrate the effectiveness of our algorithm. In particular, the model is calibrated with data from heat oil futures and its forecasting performance as well as statistical validity is investigated. The proposed model is parsimonious, self-calibrating and can be very useful in predicting futures prices.

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“…(Brooks and Prokopczuk 2013;Mohanty et al 2013;Diewald et al 2015;Cartwright and Riabko 2015) These above styled-facts of price dynamics complicate the market participants' perspective on natural gas' pricing and hedging. In addition, Deng (1999), Casassus and Collin-Dufresne (2005), Suenaga et al (2008), Volmer (2011), Back et al (2013), and Arismendi et al (2016) document evidence of heteroscedasticity in commodity price returns.…”

Section: Introductionmentioning

confidence: 99%

The affine styled-facts price dynamics for the natural gas: evidence from daily returns and option prices

Hsu

Chen

Lin

et al. 2016

Rev Quant Finan Acc

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This study analyzes affine styled-facts price dynamics of Henry Hub natural gas price by incorporating the price features of jump risk, and seasonality within stochastic volatility framework. Affine styled-facts dynamics has the advantage of being able to incorporate mean reversion (MR), stochastic volatility (SV), seasonality trends (S), and jump diffusion (J) in a standardized inclusive framework. Our main finding is that models that incorporate jumps significantly improve overall out-of-sample option pricing performance. The combined MRSVJS model provides the best fit of both daily gas price returns and the related cross section of option prices. Incorporating seasonal effects tend to provide more stable pricing ability, especially for the long-term option contracts.

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Seasonality and the valuation of commodity options

Cited by 67 publications

References 40 publications

The Jump Size Distribution of the Commodity Spot Price and Its Effect on Futures and Option Prices

The Jump Size Distribution of the Commodity Spot Price and Its Effect on Futures and Option Prices

Filtering and forecasting commodity futures prices under an HMM framework

The affine styled-facts price dynamics for the natural gas: evidence from daily returns and option prices

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