Risk valuation of quanto derivatives on temperature and electricity

Aurélien Alfonsi () and Nerea Vadillo
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Aurélien Alfonsi: MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École nationale des ponts et chaussées - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique, CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École nationale des ponts et chaussées
Nerea Vadillo: CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École nationale des ponts et chaussées

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Abstract: This paper develops a coupled model for day-ahead electricity prices and average daily temperature which allows to model quanto weather and energy derivatives. These products have gained on popularity as they enable to hedge against both volumetric and price risks. Electricity day-ahead prices and average daily temperatures are modelled through non homogeneous Ornstein-Uhlenbeck processes driven by a Brownian motion and a Normal Inverse Gaussian Lévy process, which allows to include dependence between them. A Conditional Least Square method is developed to estimate the different parameters of the model and used on real data. Then, explicit and semi-explicit formulas are obtained for derivatives including quanto options and compared with Monte Carlo simulations. Last, we develop explicit formulas to hedge statically single and double sided quanto options by a portfolio of electricity options and temperature options (CDD or HDD).

Keywords: Pricing of Securities (q-fin.PR); Portfolio Management (q-fin.PM); Risk Management (q-fin.RM); FOS: Economics and business; Energy quanto options; Weather derivatives; Joint Temperature-Electricity model; Risk hedging (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ene, nep-env and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-04358505v1
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Published in Applied Mathematical Finance, 2023, 30 (6), pp.275-312. ⟨10.48550/arXiv.2310.07692⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04358505

DOI: 10.48550/arXiv.2310.07692

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