Forecasting VIX using interpretable Kolmogorov-Arnold networks

• URL: http://arxiv.org/abs/2502.00980v1
• Date: Mon, 03 Feb 2025 01:24:02 GMT
• Title: Forecasting VIX using interpretable Kolmogorov-Arnold networks
• Authors: So-Yoon Cho, Sungchul Lee, Hyun-Gyoon Kim,
• Abstract summary: This paper presents the use of Kolmogorov-Arnold Networks (KANs) for forecasting the CBOE Volatility Index (VIX) Unlike traditional-based neural networks that are often criticized for their black-box nature, KAN offers an interpretable approach via learnable spline-based activation functions and symbolification.
• Score: 2.145761568085819
• License:
• Abstract: This paper presents the use of Kolmogorov-Arnold Networks (KANs) for forecasting the CBOE Volatility Index (VIX). Unlike traditional MLP-based neural networks that are often criticized for their black-box nature, KAN offers an interpretable approach via learnable spline-based activation functions and symbolification. Based on a parsimonious architecture with symbolic functions, KAN expresses a forecast of the VIX as a closed-form in terms of explanatory variables, and provide interpretable insights into key characteristics of the VIX, including mean reversion and the leverage effect. Through in-depth empirical analysis across multiple datasets and periods, we show that KANs achieve competitive forecasting performance while requiring significantly fewer parameters compared to MLP-based neural network models. Our findings demonstrate the capacity and potential of KAN as an interpretable financial time-series forecasting method.

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