Rough volatility is a new approach to volatility modelling that uses stochastic processes rougher than Brownian motion to drive volatility, with the aim of accurately capturing the term structure of implied volatility without time-varying parameters. The rough Bergomi model (Bayer, Friz and Gatheral, 2016) is one of the first full-fledged rough volatility models. Currently, in the absence of any analytical pricing formulae, experiments with the rough Bergomi model require Monte Carlo pricing even for vanilla instruments. In my talk, I discuss some ideas that promise to offer a significant speed-up of Monte Carlo under this model. Joint work with Ryan McCrickerd.
Cass Business School, 106 Bunhill Row
106 Bunhill Row, London EC1Y 8TZ, UK
Imperial College London
Mikko Pakkanen is a Lecturer in Mathematical Finance and Statistics and Co-Director of MSc in Mathematics and Finance at Imperial College London. He is also the leader and founder of the Imperial Network of Excellence in Probabilistic Methods and Modelling and an International Fellow of CREATES at Aarhus University. Mikko’s current research interests include statistical modelling of high-frequency financial data and market microstructure, stochastic volatility models and applications of deep learning to computational finance.
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