Sofie Reyners
Katholieke Universiteit Leuven
6 Papers
1 Citations
Sofie Reyners is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Mathematical finance & Exotic option. The author has an hindex of 3, co-authored 6 publications.
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Papers
Machine learning for quantitative finance: fast derivative pricing, hedging and fitting
TL;DR: It is illustrated that for many classical problems, the price of extra speed is some loss of accuracy, but this reduced accuracy is often well within reasonable limits and hence very acceptable from a practical point of view.
ESG: a new dimension in portfolio allocation
TL;DR: In this article, the authors examine the impact of including environmental, social and governance (ESG) criteria in the allocation of equity portfolios and focus on the risk and return characteristics of the portfolio.
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Gradient boosting for quantitative finance
TL;DR: This paper discusses how tree-based machine learning techniques can be used in the context of derivatives pricing, and illustrates this methodology by reducing computation times for pricing exotic derivative products and American options.
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Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedging and Fitting
TL;DR: In this paper, the authors show how to deploy machine learning techniques in the context of traditional quant problems, and illustrate that for many classical problems, they can arrive to speed-ups of several orders of magnitude by deploying machine learning technique based on Gaussian process regression.
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MaMaMoMaMa: BTC Options
TL;DR: In this article, the authors investigate the behavior of the bitcoin price through the vanilla options available on the market and calibrate a series of Markov models on the option surface, including the Black-Scholes model, Laplace model, five Variance Gamma related models and the Heston model.