Abstract Models characterized by autoregressive structure and random coefficients are
powerful tools for the analysis of high-frequency, high-dimensional and volatile time series …

Supply chain disruptions have emerged as a critical factor influencing global economic
conditions. This research examines the implications of global supply chain disruptions via …

In this paper, we analyze the potential impact of geopolitical risks on various natural
resources such as oil, coal, copper, and zinc, from January 2005 to September 2022. Firstly …

Vector autoregression (VAR) models are widely used for multivariate time series analysis in
macroeconomics, finance, and related fields. Bayesian methods are often employed to deal …

This paper proposes a joint model by combining the time-varying coefficient susceptible-
infected-removal model with the hierarchical Bayesian vector autoregression model. This …

The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often
hailed as the “currency of the future”. Simulating the BTC market in the price discovery …

This research examines the global implications of agricultural production and price
fluctuations via Global Bayesian Vector Autoregression (GBVAR) model. We develop a …

This study proposed an optimal model to examine the relationship between the Bitcoin price
and six macroeconomic variables–the Bitcoin price, Standard and Poor's 500 volatility index …

In this paper, we investigate non-linear linkages between Bitcoin and the unconventional
monetary policies of the European Central Bank (ECB). In particular, we examine whether a …

Investigating the asymmetric effect of income inequality on financial fragility in South Africa and
selected emerging markets: a Bayesian approach with hierarchical priors | Emerald Insight Books …