2024/01/27 by Roberto Baviera, Baviera, Roberto, Pietro Manzoni +1
Economics, Econometrics and Finance · Engineering · #Advanced Control Systems Optimization #Computational Finance (q-fin.CP) #FOS: Economics and business #Mathematical Finance (q-fin.MF) #Nuclear reactor physics and engineering #Pricing of Securities (q-fin.PR) #Stochastic processes and financial applications
paper · doi:10.48550/arxiv.2401.15483
openalex publication_date 2024/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Lévy-driven Ornstein-Uhlenbeck (OU) processes represent an intriguing class of stochastic processes that have garnered interest in the energy sector for their ability to capture typical features of market dynamics. However, in the current state of play, Monte Carlo simulations of these processes are not straightforward for two main reasons: i) algorithms are available only for some specific processes within this class; ii) they are often computationally expensive. In this paper, we introduce a new simulation technique designed to address both challenges. It relies on the numerical inversion of the characteristic function, offering a general methodology applicable to all Lévy-driven OU processes. Moreover, leveraging FFT, the proposed methodology ensures fast and accurate simulations, providing a solid basis for the widespread adoption of these processes in the energy sector. Lastly, the algorithm allows explicit control of the numerical error. We apply the technique to the pricing of energy derivatives, comparing the results with the existing benchmarks. Our findings indicate that the proposed methodology is at least one order of magnitude faster than the existing algorithms, while maintaining an equivalent level of accuracy.