Identifying at-risk populations is essential for designing effective energy poverty interventions. Using data from the HILDA Survey, a longitudinal dataset representative of the Australian population, and a multidimensional index of energy poverty, we develop a machine learning model combined...

Economically, all countries seek sustained growth driven by domestic demand, investment, and exports; however, COVID-19 revealed the vulnerability of interconnected economic systems and a sharp contraction in global trade. The objective of this research is to analyze through an econometric model...

This paper examines the dynamic relationship between economic indicators, law enforcement mechanisms, and property-related crimes in Mongolia using a time-series econometric approach. Relying on the theoretical frameworks of Becker’s economic model of crime and Cantor and Land’s...

As countries grapple with the aftermath of climate-related disasters, the disruptions they inflict on domestic consumption ripple through the fabric of income and price level shocks. The income shock emanates from the adverse effects of such disasters on economic agents, leading to both wage and...

The increasing interaction between the equity market and cryptocurrencies has raised concerns about volatility spillovers; however, empirical evidence about sectoral-specific spillover effects in emerging markets is scarce and hard to find. Existing research mainly concentrates on developed...

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital...

This study aims to identify the primary factors influencing the demand for money in Indonesia and to provide forecasts through 2033. The research employs two methodologies: time series econometrics and machine learning, utilizing data spanning from the first quarter of 2010 (2010Q1) to the...

Purpose - The purpose of this research is to develop a predictive model that can estimate the volume of remittances channeled toward Yemen's economic reconstruction efforts. Design/methodology/approach - This study utilized a time-series dataset encompassing remittance inflows into Yemen's...

Accurately detecting anomalous points in time-series data is critical, as false positives can mislead business stakeholders, waste valuable resources, and diminish the overall impact of the detection system. While various statistical and machine learning techniques are employed to flag potential...

Understanding the developments of atmospheric ethane is essential for better identifying the anthropogenic sources of methane, a major greenhouse gas with high global warming potential. While previous studies have focused on analyzing past trends in ethane and modeling the inter-annual...

This study uncovers a powerful, yet overlooked geographic dimension to terrorism’s macroeconomic impact. Our findings reveal that terror's harm to FDI is profoundly shaped by attack location. Using Pakistan as a case study we show that assaults on economic and political centers cause far...

Identifying at-risk populations is essential for designing effective energy poverty interventions. Using data from the HILDA Survey, a longitudinal dataset representative of the Australian population, and a multidimensional index of energy poverty, we develop a machine learning model combined...

Demographic aging results in a growing number of older people in need of care in many regions all over the world. Germany has witnessed steady population aging for decades, prompting policymakers and other stakeholders to discuss how to fulfill the rapidly growing demand for care workers and...

We study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate state-of-the-art machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the...

This study examines the dynamics of economic freedom in nine advanced democracies in comparison to China over the 1970-2022 period. Using data from the Fraser Institute and the Manifesto Project Database, we apply a Bayesian time-series methodology to identify three key patterns. First, economic...

This paper examines long-run linkages and possible instabilities in the gold–silver price relationship using daily futures prices over the period from 4 January 2010 to 28 November 2025. The empirical analysis includes unit-root and cointegration tests as well as endogenous structural break...

This study investigates the long-memory properties of the prices of three Kenyan commodities (tea, coffee and horticultural products) by applying fractional integration methods to monthly data spanning the period from August 1998 to December 2024. The empirical results provide evidence of...

This paper examines the impact on crude oil prices of the trade tariffs announced by the Trump administration on 2 April 2025 ("Liberation Day"). More specifically, it uses fractional integration methods to analyse daily data on WTI, Brent and Murban oil prices spanning the period from 3 June...

Building on a frequency-domain identification within a nonlinear Structural Dynamic Factor Model, we study the nonlinear transmission of demand and supply shocks, the two shocks accounting for the bulk of fluctuations in U.S. macroeconomic variables. Supply shocks propagate symmetrically and are...

This paper uses fractional integration methods to analyse the long-memory and persistence properties of inflation in the Shanghai Cooperation Organization (SCO) countries over the period from 1 January 1997 to 8 January 2025. This approach is more general than standard models based on the I(0)...

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to...

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently,...

We examine whether aggregate "music mood" derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors...

The COVID-19 pandemic constitutes one of the most significant exogenous shocks to global financial markets in recent history, raising questions about the robustness of market efficiency under extreme uncertainty. This study examines whether the pandemic affected the weak-form efficiency of the...

This paper compares the daily return volatility by four leading international indices: JSE Top 40, FTSE 100, Nikkei 225 and S&P/ASX 200. The return series are modelled in ARMA process, where ARMA(1,3) values are taken for JSE Top 40 and S&P/ASX 200, ARMA(0,0) for FTSE 100, and ARMA(1,2) for...

The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study...

We assess asymmetries, nonlinearities and state dependencies in dynamic responses of the euro area to monetary policy shocks. The dataset includes macroeconomic, financial, and survey-based variables measuring credit conditions and bank lending transmission channels. These data are observed at...