Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach

Ying Chen, Maria Grith and Hannah L. H. Lai

MPRA Paper from University Library of Munich, Germany

Abstract: Implied volatility (IV) forecasting is inherently challenging due to its high dimensionality across various moneyness and maturity, and nonlinearity in both spatial and temporal aspects. We utilize implied volatility surfaces (IVS) to represent comprehensive spatial dependence and model the nonlinear temporal dependencies within a series of IVS. Leveraging advanced kernel-based machine learning techniques, we introduce the functional Neural Tangent Kernel (fNTK) estimator within the Nonlinear Functional Autoregression framework, specifically tailored to capture intricate relationships within implied volatilities. We establish the connection between fNTK and kernel regression, emphasizing its role in contemporary nonparametric statistical modeling. Empirically, we analyze S&P 500 Index options from January 2009 to December 2021, encompassing more than 6 million European calls and puts, thereby showcasing the superior forecast accuracy of fNTK.We demonstrate the significant economic value of having an accurate implied volatility forecaster within trading strategies. Notably, short delta-neutral straddle trading, supported by fNTK, achieves a Sharpe ratio ranging from 1.45 to 2.02, resulting in a relative enhancement in trading outcomes ranging from 77% to 583%.

Keywords: Implied Volatility Surfaces; Neural Networks; Neural Tangent Kernel; Implied Volatility Forecasting; Nonlinear Functional Autoregression; Option Trading Strategies (search for similar items in EconPapers)
JEL-codes: C14 C45 C58 G11 G13 G17 (search for similar items in EconPapers)
Date: 2023-10-24
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:119022

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