A multiplicative seasonal component in commodity derivative pricing

In this paper, we focus on a seasonal jump–diffusion model to price commodity derivatives. We propose a novel approach to estimate the functions of the risk-neutral processes directly from data in the market, even when a closed-form solution for the model is not known. Then, this new approach is app...

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Bibliographic Details
Published in:Journal of computational and applied mathematics 2018-03, Vol.330, p.835-847
Main Authors: Gómez-Valle, L., Habibilashkary, Z., Martínez-Rodríguez, J.
Format: Article
Language:English
Subjects:
Jump–diffusion stochastic processes
Nonparametric estimation
Numerical differentiation
Risk premium
Risk-neutral measure
Seasonality
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Summary:In this paper, we focus on a seasonal jump–diffusion model to price commodity derivatives. We propose a novel approach to estimate the functions of the risk-neutral processes directly from data in the market, even when a closed-form solution for the model is not known. Then, this new approach is applied to price some natural gas derivative contracts traded at New York Mercantile Exchange (NYMEX). Moreover, we use nonparametric estimation techniques in order to avoid arbitrary restrictions on the model. After applying this approach, we find that a jump–diffusion model allowing for seasonality outperforms a standard jump–diffusion model to price natural gas futures. Furthermore, we also show that there are considerable differences in the option prices and the risk premium when we consider seasonality or not. These results have important implications for practitioners in the market.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2017.05.014