Forecasting VIX using interpretable Kolmogorov-Arnold networks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910810405601280 |
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| author | Cho, So-Yoon Lee, Sungchul Kim, Hyun-Gyoon |
| author_facet | Cho, So-Yoon Lee, Sungchul Kim, Hyun-Gyoon |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_00980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting VIX using interpretable Kolmogorov-Arnold networks Cho, So-Yoon Lee, Sungchul Kim, Hyun-Gyoon Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science 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. |
| title | Forecasting VIX using interpretable Kolmogorov-Arnold networks |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2502.00980 |