2017/08/31 by Ryan McCrickerd, Mikko S. Pakkanen · 50 citations
Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Financial Risk and Volatility Modeling #Implied volatility #Local volatility #Markov chain Monte Carlo #Monte Carlo method #Skew #Stochastic processes and financial applications #Stochastic volatility #Valuation of options #Variance reduction #Volatility (finance) #msc:91G20 #msc:91G60 #q-fin.CP #q-fin.PR
paper · pdf · doi:10.1080/14697688.2018.1459812
published in Quantitative Finance 18(11), 1877-1886 (Taylor & Francis) · 16 pages, 10 figures, v3: minor amendments and reformatted
openalex created_date 2017/08/17 · arxiv created 2018/03/16 · openalex publication_date 2018/04/30 · arxiv updated 2021/01/06 · openalex updated_date 2026/08/05
The rough Bergomi model, introduced by Bayer et al. [Quant. Finance, 2016, 16(6), 887–904], is one of the recent rough volatility models that are consistent with the stylised fact of implied volatility surfaces being essentially time-invariant, and are able to capture the term structure of skew observed in equity markets. In the absence of analytical European option pricing methods for the model, we focus on reducing the runtime-adjusted variance of Monte Carlo implied volatilities, thereby contributing to the model’s calibration by simulation. We employ a novel composition of variance reduction methods, immediately applicable to any conditionally log-normal stochastic volatility model. Assuming one targets implied volatility estimates with a given degree of confidence, thus calibration RMSE, the results we demonstrate equate to significant runtime reductions—roughly 20 times on average, across different correlation regimes.