Multi-asset financial markets: mathematical modelling and data-driven approaches

Thesis

This thesis develops statistical models and data-driven algorithms for modelling, simulation, and forecasting asset price dynamics in financial markets with many instruments and risk factors, focusing on equity and option markets. In such multi-asset settings, modelling of co-movements is crucial in order to implement correct hedging strategies, and generate realistic portfolio dynamics and loss distributions. Models also need to respect arbitrage relations linking prices of various instrumen...

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Authors: Vuletic, M

• DOI: 10.5287/ora-bp2ymzjen
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: GenAI; machine learning; simulation; factor models; financial markets; data-driven; implied volatility; options; volatility; scenario simulation; generative AI; ensemble learning; time series forecasting; generative models; hedging; transfer learning; arbitrage; risk management
• Subjects: Financial engineering; Generative artificial intelligence; Deep learning (Machine learning); Mathematical models; Machine learning; Risk management