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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.
Topic: Deep Learning Volatility
Aitor Muguruza is the Head of Quantitative Modelling and Data Analytics at Kaiju Capital Management. He has previously served as a Quantitative Research Analyst at Natixis, where he worked in the equities division. He has been awarded the 2020 rising star award in quantitative finance by Risk Magazine due to the seminal paper “Deep Learning Volatility”.His research interests include stochastic volatility modelling, machine learning and AI in finance. Aitor is an expert in Monte-Carlo simulation methods and proficient in C#,C++ and Python.