Spillover-Based Portfolio Management: Bayesian Diebold & Yilmaz Spillover Applications in Cryptocurrency Market

Abstract

This study evaluates the performance of various Bayesian priors in modeling and assessing financial and economic Diebold and Yilmaz spillover networks through simulations using the posterior distribution of spillover effects across multiple priors. A key contribution of this research is the introduction and validation of graph similarity analysis within spillover networks, demonstrating that even when the overall fit of the spillover is suboptimal, the interconnections between variables of interest are accurately captured in terms of directionality, albeit with slight discrepancies in magnitude. Building on this insight, we apply the Diebold and Yilmaz spillover approach to develop a novel portfolio optimization strategy that integrates Hierarchical Risk Parity (HRP) with the Louvain method, utilizing the optimized spillover values. This innovative method outperforms traditional HRP techniques when applied to both synthetic data and real cryptocurrency market data, providing a robust and efficient framework for managing interconnected financial assets.