{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/turbocharging-monte-carlo-pricing-for-the","title":"Turbocharging Monte Carlo pricing for the rough Bergomi model","arxiv_id":"1708.02563","date":"2018-03-16","proceeding":null,"authors":[],"abstract":"The rough Bergomi model, introduced by Bayer, Friz and Gatheral [Quant.\nFinance 16(6), 887-904, 2016], is one of the recent rough volatility models\nthat are consistent with the stylised fact of implied volatility surfaces being\nessentially time-invariant, and are able to capture the term structure of skew\nobserved in equity markets. In the absence of analytical European option\npricing methods for the model, we focus on reducing the runtime-adjusted\nvariance of Monte Carlo implied volatilities, thereby contributing to the\nmodel's calibration by simulation. We employ a novel composition of variance\nreduction methods, immediately applicable to any conditionally log-normal\nstochastic volatility model. Assuming one targets implied volatility estimates\nwith a given degree of confidence, thus calibration RMSE, the results we\ndemonstrate equate to significant runtime reductions - roughly 20 times on\naverage, across different correlation regimes.","url_abs":"http://arxiv.org/abs/1708.02563v3","url_pdf":"http://arxiv.org/pdf/1708.02563v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"turbocharging-monte-carlo-pricing-for-the","repo_url":"https://github.com/ryanmccrickerd/rough_bergomi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}