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Analytic formulas for futures and options for a linear quadratic jump diffusion model with seasonal stochastic volatility and convenience yield: Do fish jump?

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  • Ewald, Christian
  • Zou, Yihan

Abstract

In this article we derive tractable analytic solutions for futures and options prices for a linear-quadratic jump-diffusion model with seasonal adjustments in stochastic volatility and convenience yield. We then calibrate our model to data from the fish pool futures market, using the extended Kalman filter and a quasi-maximum likelihood estimator and alternatively using an implied-state quasi-maximum likelihood estimator. We find no statistical evidence of jumps. However, we do find evidence for positive correlation between salmon spot prices and volatility, seasonality in volatility and convenience yield. In addition we observe a positive relationship between seasonal risk premium and uncertainty of EU salmon demand. We further show that our model produces option prices that are conform with the observation of implied volatility smiles and skews. Our work connects to a number of results that have recently appeared in the Operations Research literature.

Suggested Citation

  • Ewald, Christian & Zou, Yihan, 2021. "Analytic formulas for futures and options for a linear quadratic jump diffusion model with seasonal stochastic volatility and convenience yield: Do fish jump?," European Journal of Operational Research, Elsevier, vol. 294(2), pages 801-815.
  • Handle: RePEc:eee:ejores:v:294:y:2021:i:2:p:801-815
    DOI: 10.1016/j.ejor.2021.02.004
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    Cited by:

    1. Jilong Chen & Christian Ewald & Ruolan Ouyang & Sjur Westgaard & Xiaoxia Xiao, 2022. "Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil," Annals of Operations Research, Springer, vol. 313(1), pages 29-46, June.
    2. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    3. Feng, Ling & Wang, Jieyu, 2023. "Random sources correlations and carbon futures pricing," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. Juan D. Borrero & Jesus Mariscal, 2022. "Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
    5. Størdal, Ståle & Ewald, Christian-Oliver & Lien, Gudbrand & Haugom, Erik, 2023. "Trading time seasonality in electricity futures," Journal of Commodity Markets, Elsevier, vol. 31(C).
    6. Ewald, Christian-Oliver & Haugom, Erik & Lien, Gudbrand & Størdal, Ståle & Wu, Yuexiang, 2022. "Trading time seasonality in commodity futures: An opportunity for arbitrage in the natural gas and crude oil markets?," Energy Economics, Elsevier, vol. 115(C).

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