Establishing a clear link between prices of conventional and niche product versions can help address challenges associated with anticipating these prices and attenuate concerns related to thin market conditions. In that regard, we analyze the price dynamics, differentiating between agri-product...

Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112...

We adopted an empirical approach to capture the macroeconomic impact of tax changes for the examined period from 1974 to 2018. It is generally accepted that vector autoregression model (VAR) has proven useful for describing the dynamic interrelationships of multivariate series. Our empirical...

I use Bayesian VARs with stochastic volatility to forecast global temperatures and sea level and ice cover in the Northerin emisphere until 2010, by exploiting (i) their long-run equilibrium relationship with climate change drivers (CCDs) and (ii) the relationship between world GDP and...

This study examined how uncertainty and global risk affect financial markets in emerging economies, focusing on foreign investment, CDS spreads, exchange rates, and stock return volatility. Using over 8.6 million ticker transaction observations and structural vector autoregression (VAR) models,...

Globalization and liberalization have heightened the volatility and complexity of financial markets, prompting investors to diversify their portfolios across different asset classes. This study investigates the dynamic interrelationships among the Indian stock market benchmark index (Nifty 50),...