Measuring and Quantifying Uncertainty in Volatility Spillovers: A Bayesian Approach. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Shapovalova, Yuliya; Eichler, Michael
• Is Part Of: Data science in science. Volume 2:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Volatility spillovers -- uncertainty quantification -- stochastic volatility -- particle Markov chain Monte Carlo; Big data -- Periodicals; Big data -- Data processing -- Periodicals; Data mining -- Periodicals; 006.312
• DOI: 10.1080/26941899.2023.2176379
• Languages: English
• ISSNs: 2694-1899

Abstract

Abstract: Volatility spillover measures are crucial for studying connectivity of financial time series. Understanding how financial time series are interconnected can help, for example, portfolio managers and policymakers in their decision process. Besides estimating the spillover effects themselves, it is important to estimate the corresponding uncertainty which current approaches lack. We propose a fully Bayesian approach based on a multivariate stochastic volatility model, which allows us to estimate the distribution of the volatility spillovers and naturally leads to uncertainty quantification.