Efficient simulation schemes for pricing options under the Ornstein–Uhlenbeck driven stochastic volatility model

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Highly relevant for quantitative trading desks that price and hedge exotic derivatives under stochastic volatility models. The OU-driven SV model captures the leverage effect and volatility clustering observed in real markets. Efficient simulation is critical for real-time pricing of path-dependent options (Asian, barrier, corridor swaps) which are common in structured products. The accuracy-speed tradeoff analysis directly informs production system design. However, the paper is primarily academic and does not address live trading, execution, or market microstructure considerations.

The operator splitting scheme itself is relatively simple to implement (only requires sampling 2 normal variates per time step and evaluating elementary functions). However, understanding the full paper requires knowledge of SDEs, operator splitting theory, characteristic functions, Laplace inversion, KL expansions, and Monte Carlo variance reduction techniques. The comparison with three other schemes adds complexity. The variance reduction techniques (conditioning, martingale-preserving control variates) require careful implementation per option type. Overall moderate complexity for a quantitative finance professional.

The paper provides detailed algorithmic steps, explicit formulas for all sub-steps, model parameter values (S0=100, K=100, T=1, sigma0=0.2, theta=0.2, kappa=4, xi=0.1, rho=-0.7, r=0.09531), reference true values for all four option types, and clear descriptions of all four simulation schemes. However, no code or GitHub repository is provided. The numerical tests use standard parameters from Li and Wu (2019) and Choi (2025), aiding reproducibility. The Hilbert interpolation table construction details (2048 Fourier terms, frequency spacing 0.1) are specified.