A group of agents with a common prior receive informative signals about an unknown state repeatedly over time. If these signals were public, agents' beliefs would be identical and commonly known. This suggests that if signals were private, then the more correlated these are, the greater the...

This study examines the evolution of public debt among the 27 EU member states using Graph Theory tools; the Threshold Weighted-Minimum Dominating Set (TW-MDS) and the k-core decomposition method, alongside a standard network quantitative metric, the density. By separating the data into three...

The incentives and subsidies needed to stimulate growth, resilience, and success in agripreneurial businesses will only be realized through numerous interventions as agri-preneurship contributes significantly to sustainable agricultural development in South Africa. This study provided a novel...

This study deals with the evolution of fuels' prices over time in the EU. The central research inquiry revolves around whether there exists any correlation among the trajectories followed by national prices in the gasoline and diesel markets. The EU, and more specifically the Euro-Area, by its...

A decision maker (DM) must choose between two projects or decide on no project. The expected benefits of these projects are correlated. The DM seeks advice from an agent with private information about the projects' benefits. However, the agent's divergent preferences for projects and lack of...

We empirically assess the interlinkages between sovereign risk, measured in terms of CDS spreads, and exchange rates for a sample of emerging markets. Our period of analysis includes periods of severe stress, such as the Global Financial Crisis, the COVID-19 pandemic and the Ukrainian War. Using...

Healthcare leaders are faced with many financial challenges in the contemporary environment, leading to financial distress and notable instances of bankruptcies in recent years. What is not well understood are the specific conditions that may lead to organizational economic failure. Though there...

We investigate the impact of structural shocks on the joint distribution of future real GDP growth and inflation in the euro area. We model the conditional mean of these variables, along with selected financial indicators, using a VAR and perform quantile regressions on the VAR residuals to...

This paper presents an intellectual exchange with ChatGPT, an artificial intelligence chatbot, about correlation pitfalls in risk management. The exchange takes place in the form of a conversation that provides ChatGPT with context. The purpose of this conversation is to evaluate ChatGPT's...

In Bayesian theory, the data together with the prior produce a posterior. We show that it is also possible to follow the opposite route, that is, to use data and posterior information (both of which are observable) to reveal the prior (which is not observable). We then apply the theory to...

Recently, scholarly interest in Socially Responsible Investment has increased significantly, and it is now regarded as one of the most important fields in business and strategy literature. The growth of socially conscious investors is a key component of the continuous quest for enhanced...

The objective of this study is to analyse the relationship between tourist attitudes towards waste generation in Slovakia and their corresponding behaviours. With an increasing emphasis on environmental responsibility, understanding the behaviours and beliefs of tourists concerning waste...

This study examines the evolution of public debt among the 27 EU member states using Graph Theory tools; the Threshold Weighted-Minimum Dominating Set (TW-MDS) and the k-core decomposition method, alongside a standard network quantitative metric, the density. By separating the data into three...

This study proposes a regression-based estimation method in difference-in-differences settings in the presence of time-varying covariates - a scenario commonly encountered in applications. We impose only a conditional parallel trends assumption with time-varying covariates and plausible...

There is a growing need to provide AI risk management models that can assess whether AI applications are safe and trustworthy, to make them responsible. To date, there are a few research papers on this topic. To fill the gap, in this paper we extend the recently proposed SAFE framework, a...

This study examines how oil supply, demand, and risk shocks, along with geopolitical risks, impact the performance of clean and fossil fuel stocks. Using daily data from March 2014 to January 2022, advanced methods like wavelet quantile correlation (WQC), cross-quantilogram (QC), and...

This paper shows that, in a setup 'a la Kyle (1985), correlated retail trading opens up new profit opportunities for professional investors at the expense of retail investors. Additionally, it demonstrates that market quality can benefit through higher market liquidity and higher price...

An intrusion detection system (IDS) is integral to a robust cybersecurity infrastructure. This study presents a comprehensive and advanced methodology for monitoring and detecting unwanted or malicious activities in network-oriented environments. The proposed IDS consists of three crucial...

Soybeans play a critical role in China's food system and hold significant importance in ensuring national food security. This study explores the correlation between soybean futures prices and international soybean import prices. It employs a time-series model to analyse the volatility of the...

The energy sector, as a major source of carbon emissions, has a significant impact on the operation of the carbon market and the management of carbon emissions. With the introduction of the "dual carbon" goals, the Chinese government has actively implemented measures to reduce carbon emissions,...

We examine technology ETF and uncertainty index (VIX, GVZ, and OVZ) spillover dynamics and quantile frequency interconnectedness across market states. This study is the first to use quantile-frequency spillover, quadruple wavelet coherence, and wavelet quantile correlation methodologies to...

We develop a data-driven procedure to identify which correlations in high-dimensional dynamic systems should be time-varying, constant, or zero. The method integrates a vine-based multivariate partial correlation model with sequential penalized estimation. Applied to 50 US equities and...

This paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable choice of the localizing kernel. The PDF...