Forecasting VIX using interpretable Kolmogorov-Arnold networks
This provides an interpretable method for financial time-series forecasting, addressing the black-box nature of neural networks in finance, though it is incremental as it applies a known interpretable technique to a specific domain.
The paper tackled forecasting the CBOE Volatility Index (VIX) using Kolmogorov-Arnold Networks (KANs), achieving competitive performance with significantly fewer parameters than traditional MLP-based models.
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.