An integrated optimisation model for pricing and hedging oil derivatives

Teemu Pennanen & LUCIANE SBARAINI BONATTO

Review of Derivatives Research2026https://doi.org/10.1007/s11147-026-09229-8article
AJG 2ABDC B
Weight
0.50

What the paper says

Abstract This paper develops an integrated optimisation model for pricing and hedging oil derivatives in incomplete markets where available market quotes and the trader’s views, inventory and risk aversion may affect the pricing. The model is well suited for practical applications such as the design of optimal cross-hedging strategies and the market-maker problem of pricing derivatives while managing inventory risk in illiquid market conditions. We use numerical experiments to illustrate the model features. First, by computing optimal hedge ratios, we show that the hedge effectiveness of using all available market quotes is significantly higher than that of conventional strategies using only one hedging instrument. Second, we find that the indifference prices of spread derivative contracts are often more competitive than the available market quotes. Third, we study the sensitivities of indifference prices with respect to a market-maker’s risk aversion, views and inventory.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1007/s11147-026-09229-8

Or copy a formatted citation

@article{teemu2026,
  title        = {{An integrated optimisation model for pricing and hedging oil derivatives}},
  author       = {Teemu Pennanen & LUCIANE SBARAINI BONATTO},
  journal      = {Review of Derivatives Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s11147-026-09229-8},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

An integrated optimisation model for pricing and hedging oil derivatives

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.