标题： 图神经网络在具有溢出效应的多元实现波动率预测中的应用 显示英文标题

标题： Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects

摘要： 我们提出了一种新的方法，使用定制的图神经网络对多变量实现波动率进行建模和预测，以纳入股票之间的溢出效应。 该模型提供了结合多跳邻居的溢出效应、捕捉非线性关系以及使用不同损失函数进行灵活训练的优势。 我们的实证结果提供了有力的证据，表明仅结合多跳邻居的溢出效应在预测准确性方面并不表现出明显优势。 然而，对非线性溢出效应进行建模可以提高实现波动率的预测准确性，尤其是在最多一周的短期范围内。 此外，我们的结果一致表明，与常用的均方误差相比，使用准似然损失进行训练能显著提升模型性能。 在替代设置中进行的一系列全面实证评估确认了我们结果的稳健性。 显示英文摘要

摘要： We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. Our empirical findings provide compelling evidence that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. However, modeling nonlinear spillover effects enhances the forecasting accuracy of realized volatilities, particularly for short-term horizons of up to one week. Moreover, our results consistently indicate that training with the Quasi-likelihood loss leads to substantial improvements in model performance compared to the commonly-used mean squared error. A comprehensive series of empirical evaluations in alternative settings confirm the robustness of our results.