analysis of the predictor of a volatility surface by machine learning

Analyse de la prédiction d'une nappe de volatilité par Machine Learning

Valentin Lourme ()
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Valentin Lourme: Arts et Métiers ParisTech, Natixis

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Abstract: The purpose of this study is to compare two approaches to assessing the points of a volatility layer. The first approach used is cubic spline interpolation, while the second approach is a machine learning algorithm, the XGBoost. The purpose of this comparison is to define the use case where the XGBoost Learning machine algorithm is more suitable compared to the cubic spline. The comparison between the two approaches is measured with the error between the measured volatility and the interpolated or predicted volatility. Cubic spline interpolation requires volatility data on the day of the study for interpolation to occur. The XGBoost Machine Learning algorithm will train on historical data to predict the volatility value on the day of the study.

Date: 2023-07-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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Published in Procedia Materials Science (Elsevier), 2023

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04151604

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