Search Results (55)

The triple shocks of the financial crisis, sovereign debt crisis, and COVID-19 pandemic have exerted significant impact on the financial markets in the Eurozone. Since the 2008 recession, the European Central Bank (ECB) has implemented an array of unconventional monetary policies (UMPs). These policies aim to address issues such as financing constraints and low inflation rates that the traditional monetary policy framework could not handle. The data indicated that when the ECB implemented its quantitative easing (QE) programs (e.g., the pandemic emergency purchase program), inflation in the Eurozone bounced back. It went up from −0.3% in August 2020 to 5% by December 2021. These measures prevented the pandemic from pushing the economy into a long-lasting deflation pressure. As the world’s second-largest economy, China’s monetary policy decisions play a crucial role in maintaining economic stability and fostering sustainable growth. This study examines ECB’S major unconventional monetary policy measures, evaluates their effects, and explores how these align with China’s monetary policy formulation and reforms. This research can provide useful insights for shaping monetary policy in the Eurozone and emerging economies such as China, especially during times of economic uncertainty. Full article

Purpose: This study investigates how South Africa’s sectoral stock market performance responds to monetary and fiscal policy shifts across two macroeconomic regimes: the pre-inflation targeting (Pre-IT) and the inflation targeting (IT) periods. Design/methodology/approach: Employing a Markov Switching Dynamic Regression (MS-DR) model, the paper explores non-linear and state-dependent relationships between policy instruments (interest rate, money supply, government expenditure, tax revenue, exchange rate, and inflation) and the performance of the industrial, financial, and resource sectors. Findings: The results reveal regime- and sector-specific heterogeneities. In the Pre-IT era, monetary policy exhibited stronger contractionary effects, while fiscal policy had mixed impacts. Under IT, sectoral responses were moderated, with inflation stability supporting industrial and financial sectors during expansions but dampening resource sector performance in recessions. Practical implications: The findings highlight the need for sector-specific and state-contingent policy designs to enhance macroeconomic stability and inclusive growth. Industrial and resource sectors, being more labour-intensive, require tailored support during downturns. Originality/value: This paper contributes to the literature by providing novel evidence on how structural changes in policy regimes affect the transmission of macroeconomic policies to different stock market sectors in South Africa. Full article

Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels—including sentence length, punctuation density, word length, and type–token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT’s communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation—though often subtle—may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability—key pillars of sustainable development. Full article

Using panel data on 99 Italian provinces in the period between 2005 and 2020, the research investigates the effects of fundamental economic factors on the home sales at the provincial level, in order to build a forecasting model using a non-linear artificial intelligence approach (MLP-Multiple Linear Perceptron neural network). There are multiple objectives to this: (a) to test the hypothesis that national, regional and local fundamentals such as interest rates, income, inflation rate, unemployment and demography affect the activity’s degree of the housing market; (b) to verify the effectiveness of a neural network in describing the dynamics of the real estate market; (c) to build a simulation model capable of predicting the effect of changes in fundamentals, also due to economic policy measures, on the market. Empirical results show that neural networks offer better capabilities than linear models in representing the complex relationships between the economic situation and the real estate market. The study provides useful information for regulators to improve the effectiveness of monetary policy to stabilize real estate markets as well as for stakeholders to draw up scenarios of market development. Full article

As governments increasingly explore digital financial instruments to diversify funding channels and expand citizen participation, Thailand’s G-Token represents an early attempt to integrate blockchain technology into sovereign debt issuance. This study examines its potential implications through a multi-dimensional risk and governance framework, situating the analysis within both domestic regulatory structures and international benchmarks. The evaluation considers macroeconomic effects—such as potential shifts in monetary policy transmission, bank disintermediation risks, and systemic liquidity impacts—alongside micro-level concerns involving investor protection, market integrity, and financial literacy. Using comparative analysis with the European Union, Singapore, and United States regulatory approaches, the paper identifies critical gaps in legal classification, oversight maturity, and structural safeguards. Findings indicate that while Thailand’s design—particularly its separation from payment systems—supports monetary coherence, its ad hoc legal integration, reliance on administrative investor protections, and early-stage market infrastructure pose vulnerabilities if adoption scales. The study concludes that achieving long-term viability will require explicit statutory authorization, enhanced disclosure and governance standards, strengthened interagency oversight, and inclusive market access strategies. These insights provide a structured basis for emerging economies seeking to adopt government-backed tokenized instruments without undermining financial stability or public trust. Full article

This study investigates the short-run and long-run dynamics of the financial cycle in South Africa, focusing on its macroeconomic drivers and their asymmetric effects across different phases. It addresses the persistent challenge in emerging market economies of balancing financial development and stability amidst volatile conditions. Using monthly data from 2000 to 2024, the research employs a quantile autoregressive distributed lag (QARDL) model to capture the heterogeneity and persistence of macro-financial linkages across the financial cycle’s distribution. The use of the QARDL model in this study allows for capturing asymmetric and quantile-specific relationships that traditional linear models might overlook. Findings reveal that monetary policy, and the housing sector are key drivers of long-term financial development in South Africa, showing positive effects. Conversely, exchange rate movements, inflation, money supply, and macroprudential policy dampen financial development. Short-term financial booms are associated with GDP growth, credit, share, and housing prices. Money supply and inflation are more closely linked to burst phases. These results underscore the importance of policy coordination, particularly between monetary and macroprudential authorities, to balance promoting financial development and ensuring stability in emerging markets. This study contributes to the empirical literature and offers practical insights for policymakers. Full article

The effectiveness of fiscal and monetary policy in sustaining growth and facilitating recovery from economic crises is increasingly considered to be significantly influenced by the quality of a country’s institutions. Strong institutions may determine how well macroeconomic policies perform under both stable and turbulent circumstances. This study examines how institutional quality (IQ) moderates the effects of fiscal and monetary policies on economic growth in Thailand from Q1:2003 to Q4:2023. Using a combination of BART and BASAD models, we find that voice and accountability and control of corruption are key institutional factors. Among macroeconomic indicators, exports, household debt, gold prices, and electricity generation emerge as the most important drivers of growth during the study period. The findings showed that IQ stabilizes and enhances the impact of policy interest rates and export growth while mitigating negative shocks from household debt and energy infrastructure challenges. Monetary policy effectiveness varies and depends on governmental institutions. Fiscal policy remains mostly neutral but shifts with institutional conditions. These results highlight that strong institutions improve the efficacy of macroeconomic policies and support sustainable growth. This study empirically examines the moderating role of IQ in economic resilience and policy design in an emerging economy using microdata from Thailand as a focus and the Time-varying Seemingly Unrelated Regression Equation (tvSURE) model. Full article

This paper considers the impact of Central Bank Digital Currencies (CBDCs) on the world’s financial systems with a special emphasis on G20 economies. Using quarterly macro-financial data for the period of 2000 to 2024, collected from the IMF, BIS, World Bank, and Atlantic Council, a Global Vector Autoregression (GVAR) model is applied to 20 G20 countries. The results reveal significant heterogeneity across economies: CBDC shocks intensify emerging market financial instability (e.g., India, Brazil), while more digitally advanced countries (e.g., UK, Japan) experience stabilization. Retail CBDCs increase disintermediation risks in more fragile banking systems, while wholesale CBDCs improve cross-border liquidity. This article contributes to the literature by providing the first GVAR-based estimation of CBDC spillovers globally. Full article

Housing prices have recently risen sharply in many countries, primarily linked to the global credit cycle. Although various factors play a role, the ability of developing countries to navigate this cycle and maintain autonomous monetary policies is crucial. This paper introduces a dynamic macroeconomic model featuring a housing production sector within an imperfect banking framework. It captures key housing and economic dynamics in advanced and emerging economies. The analysis shows domestic liquidity policies, such as bank capital requirements, reserve ratios, and currency devaluation, can stabilize investment and production. However, their effectiveness depends on foreign interest rates and liquidity. Stabilizing housing prices and risk-free bonds is more effective in high-interest environments, while foreign liquidity shocks have asymmetric impacts. They can boost or lower the effectiveness of domestic policy, depending on the country’s level of financial development. These findings have several policy implications. For example, foreign capital controls would be adequate in the short term but not in the long term. Instead, governments would try to promote the development of local financial markets. Controlling debt should be a target for macroprudential policy as well as promoting saving instruments other than real estate, especially during low interest rates. Full article

This study employs a dynamic stochastic general equilibrium model with Bayesian estimation to rigorously evaluate China’s macroeconomic responses to cost-push, monetary policy, and foreign income shocks. This analysis leverages quarterly data from 2000 to 2024, focusing on critical variables such as the output gap, inflation, interest rates, exchange rates, consumption, investment, and employment. The results demonstrate significant social welfare losses primarily arising from persistent inflation and output volatility due to domestic structural rigidities and global market dependencies. Monetary policy interventions effectively moderate short-term volatility but induce welfare costs if overly restrictive. The findings underscore the necessity of targeted structural reforms to enhance economic flexibility, balanced monetary policy to mitigate aggressive interventions, and diversified economic strategies to reduce external vulnerability. These insights contribute novel policy perspectives for enhancing China’s macroeconomic stability and resilience. Full article

This study investigates cyclical asymmetries in money demand models considering the moderating effect of financial development. Prior research has overlooked this issue in the money demand literature within the Fijian context, where research is outdated. Using annual data from 1983 to 2023, we find that income elasticity is about positive unity, irrespective of recessions or expansions. In expansions, an increase in interest rates reduces money demand. An increase in interest rates reduces money demand nine times more strongly in recessions. These effects are accentuated with financial development. Declining interest rates do not impact money demand. The findings suggest that stable money demand could be achievable, but only once the impact of structural breaks is accounted for. Under ideal conditions—without such breaks—money demand exhibits stability, and its connection to income and interest rates appears predictable. However, in reality, structural disruptions complicate this relationship, making money demand less consistent with its key drivers and undermining the reliability of money supply as a monetary policy instrument. The findings align with the pulling on a string hypothesis that monetary contractions control inflation, but expansions may not impact output. Full article

To stabilize economies, central banks implemented unconventional monetary policies like quantitative easing following the global financial crisis. Although much research has been done on how quantitative easing affects financial markets, the influence of central bank transparency on bank profitability under such policies is still underexplored. This paper looks at how central bank transparency affects bank profitability in advanced countries under unconventional monetary policy. Using a panel dataset of commercial banks from 25 advanced economies (2013–2019), we apply a two-step Generalized Method of Moments (GMM) estimator to handle any endogeneity. Focusing on central bank transparency as a main transmission route, the model accounts for macroeconomic factors and bank-specific characteristics. The results show that central bank transparency greatly improves bank profitability together with quantitative easing. Although other elements, macroeconomic conditions and bank-specific characteristics, support transparency as a vital channel via which monetary policy influences the operation of the banking sector. This paper provides recommendations for legislators trying to enhance the effectiveness of unconventional policies in various institutional contexts by highlighting the need for central bank transparency as a channel for monetary policy efficacy. Full article

The process of institutional accreditation establishes crucial mechanisms that lead to better quality childcare in orphanages through the development of organizational stability and trained staff, in addition to healthcare improvements. The assessment of accreditation effects on children’s health draws from learning organizations and quality assurance frameworks in this systematic review. A systematic database review yielded 35 peer-reviewed publications that followed PRISMA analysis procedures. Research evidence shows that accredited orphanages attain better results when it comes to hygiene practices, as well as nutrition standards, healthcare access, mental healthcare support. Accreditation enables institutions to learn continuously because the process promotes service delivery improvements. The advantages of accreditation in orphanages are clear, but accreditation faces the barriers of monetary constraints, employee reluctance towards external inspections, and erratic policy execution, which reduce its widespread adoption. Accreditation efforts in orphanages require purposeful funding alongside built-up staff competencies and stronger regulatory policies to achieve their maximum potential benefit. Full article

This study investigated the influence of stochastic fluctuations on financial system stability by analyzing both ordinary and fractional-order financial models under noise. The ordinary financial system experiences perturbations due to bounded random disturbances, whereas the fractional-order counterpart models memory-dependent behaviors by incorporating fractional Gaussian noise (FGN) characterized by a Hurst parameter that governs long-term correlations. This study used data generated through MATLAB simulations based on standard financial models from the literature. Numerical simulations compared system behavior in deterministic and noisy environments. The results reveal that ordinary systems experience transient fluctuations, quickly returning to a stable state, whereas fractional systems exhibit persistent deviations due to historical dependencies. This highlights the fundamental difference between integer-order and fractional-order derivatives in financial modeling. Our key findings indicate that noise significantly impacts interest rates, investment needs, price indices, and profit margins, with the fractional system displaying higher sensitivity to external shocks. These insights emphasize the necessity of incorporating memory effects in financial modeling to improve accuracy in predicting market behavior. The study further underscores the importance of adaptive monetary policies and risk management strategies to mitigate financial instability. Future research should explore hybrid models combining short-term stability with long-term memory effects for enhanced financial forecasting and stability analysis. Full article

Addressing unemployment is essential for formulating effective public policies. In particular, socioeconomic and monetary variables serve as essential indicators for anticipating labor market trends, given their strong influence on employment dynamics and economic stability. However, effective unemployment rate prediction requires addressing the non-stationary and non-linear characteristics of labor data. Equally important is the preservation of interpretability in both samples and features to ensure that forecasts can meaningfully inform public decision-making. Here, we provide an explainable framework integrating unsupervised and supervised machine learning to enhance unemployment rate prediction and interpretability. Our approach is threefold: (i) we gather a dataset for Colombian unemployment rate prediction including monetary and socioeconomic variables. (ii) Then, we used a Local Biplot technique from the widely recognized Uniform Manifold Approximation and Projection (UMAP) method along with local affine transformations as an unsupervised representation of non-stationary and non-linear data patterns in a simplified and comprehensible manner. (iii) A Gaussian Processes regressor with kernel-based feature relevance analysis is coupled as a supervised counterpart for both unemployment rate prediction and input feature importance analysis. We demonstrated the effectiveness of our proposed approach through a series of experiments conducted on our customized database focused on unemployment indicators in Colombia. Furthermore, we carried out a comparative analysis between traditional statistical techniques and modern machine learning methods. The results revealed that our framework significantly enhances both clustering and predictive performance, while also emphasizing the importance of input samples and feature selection in driving accurate outcomes. Full article