Signal from noise: a neural network-based denoising approach for measuring global financial spillovers

Abstract Filtering signal from noise is fundamental to accurately assessing spillover effects in financial markets. This study investigates denoised return and volatility spillovers across a diversified set of markets, spanning developed and developing economies as well as key asset classes, using a neural network-based denoising architecture. By applying denoising to the covariance matrices prior to spillover estimation, we disentangle signal from noise. Our analysis covers the period from late 2014 to mid-2025 and adopts both static and time-varying frameworks. The results reveal that developed markets predominantly serve as net transmitters of volatility spillovers under normal conditions, but often transition into net receivers during episodes of systemic stress, such as the Covid-19 pandemic. In contrast, developing markets display heightened instability in their spillover roles, frequently oscillating between transmitter and receiver positions. Denoising not only clarifies the dynamic and heterogeneous nature of spillover channels, but also sharpens the alignment between observed spillover patterns and known financial events. These findings highlight the necessity of denoising in spillover analysis for effective monitoring of systemic risk and market interconnectedness.