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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Deep SPX & VIX Smile Calibration under Rough Volatility |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 81 |
| Date | 2023 |
| Abstract Text | Inspired by the celebrated Joint SPX & VIX Calibration problem, this study delves into the technical derivation of two prominent rough stochastic volatility models from the ground up, with the aim of consolidating and extending previous empirical findings as well as shining a new light on critical details that might have gone unnoticed. A systematic analysis of the latest Deep Learning interpretations of the rough Bergomi and rough Heston models reveals a series of eloquent properties with regards to stylised facts observed in SPX and VIX volatility time series. |
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