Bayesian Spillover Graphs for Dynamic Networks
We present Bayesian Spillover Graphs (BSG), a novel method for learning temporal relationships, identifying critical nodes, and quantifying uncertainty for multi-horizon spillover effects in a dynamic system. BSG leverages both an interpretable framework via forecast error variance decompositions (FEVD) and comprehensive uncertainty quantification via Bayesian time series models to contextualize temporal relationships in terms of systemic risk and prediction variability. Forecast horizon hyperparameter $h$ allows for learning both short-term and equilibrium state network behaviors. Experiments for identifying source and sink nodes under various graph and error specifications show significant performance gains against state-of-the-art Bayesian Networks and deep-learning baselines. Applications to real-world systems also showcase BSG as an exploratory analysis tool for uncovering indirect spillovers and quantifying systemic risk.
Code (1)
Tasks
Time SeriesTime Series AnalysisUncertainty QuantificationSimilar Papers 제목 키워드 기반
Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Days
This study introduces a global stock market volatility forecasting model that enhances forecasting accuracy and practical utility in real-world financial decision-making by integrating dynamic graph structures and encomp…
AllDecision MakingGraph Neural NetworkIdentification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split
The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when there are few treated units. SCM assumes the stable unit treatment value assumption (SUTVA), which posits that pot…
Bayesian InferenceCausal InferenceDynamic Treatment on Networks
In networks, effective dynamic treatment allocation requires deciding both whom to treat and also when, so as to amplify policy impact through spillovers. An early intervention at a well-connected node can trigger cascad…
Reinforcement LearningDynamic graph neural networks for enhanced volatility prediction in financial markets
Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility c…
Decision MakingGraph AttentionUnderstanding Volatility Spillover Relationship Among G7 Nations And India During Covid-19
Purpose: In the context of a COVID pandemic in 2020-21, this paper attempts to capture the interconnectedness and volatility transmission dynamics. The nature of change in volatility spillover effects and time-varying co…