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A1168
Title: Learning rough volatility Authors:  Blanka Horvath - Kings College London (United Kingdom) [presenting]
Abstract: Calibration time is the bottleneck for models with rough volatility. We present ways for substantial speed-ups, along every step of the calibration process: In a first step we describe a powerful numerical scheme (based on functional central limit theorems) for pricing a large family of rough volatility models. In a second step we discuss various machine learning methods that significantly reduce calibration time for these models. By simultaneously calibrating several (classical and rough) models to market data as a byproduct of our calibration results, we re-confirm that volatility is rough, calibration performance being best for very small Hurst parameters in a multitude of market scenarios.