id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-222868-k3k0iqds	Goswami, Anindya	Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning	2020-08-02		.txt	text/plain	9749	520	60	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. While the former used only the moneyness parameter (ratio of spot and strike values) and time-to-maturity as inputs to their learning model, the latter also used historical volatility, interest rate, and lagged prices of the underlying asset and option contract. Model evaluation metrics for models trained and tested on BANKNIFTY options contract price data From the results shown in Table 5 and Table 4 , it is evident that Approach III ANN models perform significantly better than all other proposed models. Table 11 presents the values of the performance metrics, for when the pre-trained Approach III models (constructed in sections 5.2 and 5.4) are tested on 2019 − 2020 data for the NIFTY50 Index.	./cache/cord-222868-k3k0iqds.txt	./txt/cord-222868-k3k0iqds.txt