About: Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning
Creator	Goswami, Anindya Rajani, Sharan Tanksale, Atharva
source	ArXiv
abstract	We propose three different data driven approaches for pricing European style call options using supervised machine-learning algorithms. The proposed approaches are tested on two stock market indices, NIFTY50 and BANKNIFTY from the Indian equity market. Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black Scholes formula for all the experiments. Our choice of scale free I/O allows us to train models using combined data of multiple different assets from a financial market. This not only allows the models to achieve far better generalization and predictive capability, but also solves the problem of paucity of data, the primary limitation of using machine learning techniques. We also illustrate the performance of the trained models in the period leading up to the 2020 Stock Market Crash, Jan 2019 to April 2020.
has issue date	2020-08-02(xsd:dateTime)
has license	arxiv
sha1sum (hex)	6edec9177333536bb1dfc83363cefba4bf0dc66b
resource representing a document's title	Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning
resource representing a document's body	covid:6edec9177333536bb1dfc83363cefba4bf0dc66b#body_text
is schema:about of	named entity 'experiments' named entity 'tested' named entity 'CRASH' named entity 'FREE' named entity 'MULTIPLE' »more»

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