Outlier-Driven Network Inference of Financial Time Series

Outliers in financial time series can reveal latent inter-asset relationships that are often missed by traditional dependence measures and average dynamic models. To address this gap, we propose Outlier-Driven Network Inference (ODNI), a framework for reconstructing directed lagged Tail Outlier-Triggering Networks from financial time series. ODNI first converts multivariate return series into upper and lower tail outlier indicators using empirical quantiles, then applies a bivariate EM-based attribution model to infer lagged triggering relationships across tail channels, and finally constructs a directed weighted network by combining baseline-corrected excess activation with EM attribution weights. For controlled evaluation, we simulate multivariate time series with volatility clustering and cross-variable spillovers from a known directed interaction template using a cross-GARCH(1,1) model. Across extensive experiments, ODNI achieves the best reconstruction performance among CoVaR, a Clayton copula tail-dependent network, and DCC-GARCH, with especially strong precision. Robustness tests show stable behavior across regimes, with systematic improvement as sample length increases and true coupling becomes more identifiable. Applications to major foreign exchange rates and global stock indices further reveal clear regional structure and asymmetric sender–receiver roles across tail-triggering channels. ODNI provides a practical tool for uncovering latent risk transmission pathways driven by tail outliers.