Pricing options in energy markets is particularly challenging because of sharp price swings, nonlinear dynamics, and heavy-tailed distributions observed in commodity returns. This study develops a numerical framework for valuing crude oil options by applying the stochastic volatility with correlated jumps model, which captures both volatility clustering and sudden price shocks. The associated partial integro-differential equation is reformulated into a system of forward–backward stochastic differential equations (FBSDEs), which we solve using a deep learning–based algorithm. We benchmark the proposed method against classical numerical techniques, including Monte Carlo simulation. Using real market data for West Texas Intermediate crude oil prices and options, we demonstrate that the deep learning–FBSDE approach provides greater accuracy and stability while reducing computational inefficiency. Our findings highlight the promise of this approach for advancing option valuation, hedging, and risk management in energy and commodity finance.
Option pricing can be viewed as a trade-off among pricing accuracy, model stability, interpretability, arbitrage consistency, computational latency, and data requirements.
Shu-Ya Lu· Proceedings of the 1st Inter...· 0 citations
With the increase in uncertainty of the modern financial market, so too has the problem of derivative pricing risen. Traditionally, the Black-Scholes model has been a solid theoretical model. However, it relies on idealized assumptions such as constant volatility and continuous asset price movements. I In reality, fina...
Ke Mo· Advances in Economics, Manag...· 0 citations
We develop a PDE-constrained optimization framework for calibrating a regime-switching Heston–Merton model to S&P 500 index option prices. The model features two latent Markov regimes modulating stochastic volatility parameters and compound Poisson jumps, capturing the stylized fact that market volatility clusters diff...
This paper studies the valuation of continuously monitored geometric Asian options under a two-factor stochastic volatility model with stochastic long-term variance levels. We derive the joint characteristic function required for continuous geometric averaging and obtain analytical pricing formulas for fixed-strike cal...
Accurate estimation of asset value changes in capital markets is fundamental to informed
investment decision-making, particularly in highly volatile sectors. While stochastic models such
as Geometric Brownian Motion (GBM), Jump Diffusion, and stochastic volatility models have
been widely applied to capture price dyn...
P. Nwagor· INTERNATIONAL JOURNAL OF APP...· 0 citations
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