使用可解释的Kolmogorov-Arnold网络预测VIX
Forecasting VIX using interpretable Kolmogorov-Arnold networks
AI总结：
本文提出使用可解释的Kolmogorov-Arnold Networks (KANs)预测VIX，通过可学习样条激活函数和符号化提供闭式解与关键特征见解，在多数据集上以更少参数实现具竞争力的性能。
摘要：
本文提出了使用Kolmogorov-Arnold Networks (KANs) 来预测CBOE Volatility Index (VIX)。与常因黑盒特性受批评的传统基于MLP的神经网络不同，KAN通过可学习的样条激活函数和符号化提供了一种可解释的方法。基于具有符号函数的简约架构，KAN将VIX的预测表示为解释变量的闭式解，并为VIX的关键特征（包括均值回归和杠杆效应）提供了可解释的见解。通过对多个数据集和时期的深入实证分析，我们表明，与基于MLP的神经网络模型相比，KAN在实现具有竞争力的预测性能的同时，所需的参数显著更少。我们的发现证明了KAN作为一种可解释的金融时间序列预测方法的能力和潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
