Graph Neural Networks for Forecasting Multivariate Realized Volatility
  with Spillover Effects
Chao Zhang Xingyue Pu Mihai Cucuringu Xiaowen Dong
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.