Not so Particular about Calibration: Smile Problem Resolved

2019/09/29 by Aitor Muguruza, Muguruza, Aitor · 1 citation
Economics, Econometrics and Finance · #60F05 #60F17 #60G15 #60G22 #91B25 #91G20 #91G60 #Computational Finance (q-fin.CP) #FOS: Economics and business #Mathematical Finance (q-fin.MF) #msc:60F05 #msc:60F17 #msc:60G15 #msc:60G22 #msc:91B25 #msc:91G20 #msc:91G60 #q-fin.CP #q-fin.MF

paper · pdf · doi:10.48550/arxiv.1909.13366

15 pages, 5 figures

arxiv created 2019/09/29 · arxiv updated 2019/10/01

Abstract

We present a novel Monte Carlo based LSV calibration algorithm that applies to all stochastic volatility models, including the non-Markovian rough volatility family. Our framework overcomes the limitations of the particle method proposed by Guyon and Henry-Labordère (2012) and theoretically guarantees a variance reduction without additional computational complexity. Specifically, we obtain a closed-form and exact calibration method that allows us to remove the dependency on both the kernel function and bandwidth parameter. This makes the algorithm more robust and less prone to errors or instabilities in a production environment. We test the efficiency of our algorithm on various hybrid (rough) local stochastic volatility models.

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