Search Results (18)

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

This paper develops a Natural Language Processing (NLP) pipeline to quantify the hawkish–dovish stance in the Federal Reserve’s semiannual Monetary Policy Reports (MPRs). The goal is to transform long-form central-bank text into reproducible stance scores and interpretable policy signals for research and monitoring. The corpus comprises 26 MPRs (26 February 2013 to 20 June 2025). PDFs are parsed and segmented and chunks are embedded, indexed with FAISS, retrieved via LangChain, and scored by GPT-4o on a continuous scale from −2 (dovish) to +2 (hawkish). Reliability is assessed with a four-dimension validation suite: (i) semantic consistency using cosine-similarity separation, (ii) numerical consistency against theory-implied correlation ranges (e.g., Taylor-rule logic), (iii) bootstrap stability of reported metrics, and (iv) content-quality diagnostics. Results show a predominant Neutral distribution (50.0%), with Dovish (26.9%) and Hawkish (23.1%). The average stance is near zero (≈0.019) with volatility σ ≈ 0.866, and the latest window exhibits a hawkish drift of ~+0.8 points. The Numerical Consistency Score is 0.800, and the integrated validation score is 0.796, indicating publication-grade robustness. We conclude that an embedding-based, agentic RAG approach with GPT-4o yields a scalable, auditable measure of FED communication; limitations include biannual frequency and prompt/model sensitivity, but the framework is suitable for policy tracking and empirical applications. Full article

Using time-varying Granger causality, Neural Networks Nonlinear VAR, and Wavelet Coherence analysis, we evidence the unstable effect of the money market rate on industrial production and consumer price index in Tunisia. The effect is asymmetric and depends on geopolitical risk (low versus high). We show that global geopolitical risk has both detriments and benefits sides—it is a threat and an opportunity for monetary policy transmission mechanisms. Interacted local projections (LPs) reveal short–medium-term volatility or dampening effects, suggesting that geopolitical uncertainty might weaken the immediate impact of monetary policy on output and prices. In uncertain environments (e.g., high geopolitical risk), economic agents—households and businesses—may adopt a wait-and-see approach. They delay consumption and investment decisions, which could initially mute the impact of monetary policy. Agents may delay their responses until they gain more information about geopolitical developments. Once clarity emerges, they may adjust their behavior, aligning with the long-run effects observed in the Vector Error Correction Model (VECM). Furthermore, we identify an exacerbating investor sentiment following tightening monetary policy, during global and local geopolitical episodes. The impact is even more pronounced under conditions of high domestic weakness. Evidence is extracted through a novel composite index that we construct using Principal Component Analysis (PCA). Our results have implications for the Central Bank’s monetary policy conduct and communication practices. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

Target2 is the Eurozone’s cross-border payment system, which is mandatory for the settlement of euro transactions involving Eurozone central banks. It is being used to save the Eurozone from imploding. A key underlying problem is that the Eurozone does not satisfy the economic conditions for being an Optimal Currency Area, i.e., a geographical area over which a single currency and monetary policy can operate on a sustainable, long-term basis. The different business cycles in the Eurozone, combined with poor labour and capital market flexibility, mean that systematic trade surpluses and deficits will build up because inter-regional exchange rates can no longer be changed. Surplus regions need to recycle the surpluses back into deficit regions via transfers to keep the Eurozone economies in balance. But the largest surplus country—Germany—refuses to formally accept that the European Union is a ‘transfer union’. However, deficit countries, including the largest of these—Italy—are using Target2 for this purpose. Target2 has become a giant credit card for Eurozone members that import more than they export to other members, but with two differences compared with normal credit card debt: neither the debt nor the interest that accrues on the debt ever needs to be repaid. Furthermore, the size of the deficits being built up is causing citizens in deficit countries to lose confidence in their banking systems, leading them to transfer their funds to banks in surplus countries. Target2 is also being used to facilitate this capital flight. However, these are not viable long-term solutions to systemic Eurozone trade imbalances and weakening national banking systems. There are only two realistic outcomes. The first is a full fiscal and political union, with Brussels determining the levels of tax and public expenditure in each member state—which has long been the objective of Europe’s political establishment. The second outcome is that the Eurozone breaks up. Full article

In the scientific literature, there is a lack of a systematic approach to credit risk factors. In addition, insufficient attention is still paid to analysing the macroeconomic factors of consumer loan credit risk. Thus, this research aims to evaluate the macroeconomic factors of consumer loan credit risk in Central and Eastern European countries’ banking systems. The findings of the study can be formulated as follows. After analysing scientific literature on credit risk factors, an improved and detailed (at five different levels) classification of factors influencing banking credit risk is proposed. This classification can be beneficial for more enhanced analysis of the factors influencing banking credit risk for the whole loan portfolio as well as for different types of loans, e.g., consumer loans. For quantitative evaluation of the impact of macroeconomic factors on consumer loan credit risk, the methods of panel data analysis and bivariate and multiple regressions are employed. Eleven CEE countries in the period from 2008 to 2020 are analysed. The results revealed that the aggregate of general macroeconomic condition factors is negatively related to consumer loan NPLs. Moreover, the economic growth, stock market, foreign exchange market, and institutional environment factors proved to be risk-decreasing, while credit market and bond market factors had a risk-increasing impact. The results of this research might help financial institutions manage credit risk more efficiently and also might be relevant to governments and central banks when selecting and applying fiscal and monetary policy measures. This study also makes policy recommendations. Full article

Conventional explanations of monetary policy decisions in the United States assume that the longer-run Federal funds rate is determined by a representative central banker (i.e., the Fed) using longer-term forecasts of economic activity and unemployment. This assumption is inconsistent with the federalist structure of the Federal Reserve in which the Federal funds rate is determined by a committee made up of the Federal Reserve Board and the Federal Reserve Banks. This inconsistency would be irrelevant if differences in the Fed participants’ longer-run projections were small or constant, but they are not: disparities in these longer-run projections are large and volatile. This finding raises several questions: Are FOMC participants relying on the same forecasting framework (i.e., model or rules of thumb) but using different values for the forecast drivers? Or are these participants using the same forecast drivers but relying on different frameworks? Full article

Suggested only a few years ago, green central banking has received a new impetus with the central bank interventions implemented in the wake of the COVID-19 pandemic. Several central banks, with the European Central Bank (ECB) and the Bank of England (BoE) being prominent examples, have stepped up their public communication on this issue in an effort to explain and justify their planned or ongoing policy actions. Carefully recorded and easy to find, these public communication messages are a rich source of insight into the process of monetary policy formation. In this article, we analyze the messages from two central banks, with the primary objective of identifying the narratives they use (if any) and describing the key features of these narratives, thus shedding new light on an ongoing process of policy change. A secondary objective of the article is to contribute to the growing literature related to the use of narratives in public policy by studying narratives in monetary policy through qualitative means, an approach that, to date, has received relatively little attention from scholars. To this end, we discuss two expectations related to the use of policy narratives derived from the literature. Thus, we hope to show how the two central banks devise and deploy narratives to help implement an unprecedented turnaround in monetary policy. Full article

Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squares (OLS) regression as a base-learner, and we re-update the parameters of our base-learner by using neural networks, which is a meta-learner in our proposed model. The meta-learner updates the regression coefficients using the confidence interval formula. We extensively compared our proposed model to other benchmark approaches on public datasets for regression task. The results showed that our proposed neural network-based interpretable model showed outperformed results compared to the benchmark models. We also applied our proposed model to the synthetic data to measure model interpretability, and we showed that our proposed model can explain the correlation between input and output variables by approximating the local linear function for each point. In addition, we trained our model on the economic data to discover the correlation between the central bank policy rate and inflation over time. As a result, it is drawn that the effect of central bank policy rates on inflation tends to strengthen during a recession and weaken during an expansion. We also performed the analysis on CO₂ emission data, and our model discovered some interesting explanations between input and target variables, such as a parabolic relationship between CO₂ emissions and gross national product (GNP). Finally, these experiments showed that our proposed neural network-based interpretable model could be applicable for many real-world applications where data type is tabular and explainable models are required. Full article

The Federal Reserve has played an arguably important role in financial crises in the United States since its creation in 1913 through monetary policy tools. Thus, this paper aims to analyze the impact of monetary policy on the United States’ economic growth in the short and long run, measured by Gross Domestic Product (GDP). The Vector Autoregressive (VAR) method explores the relationship among the variables, and the Granger causality test assesses the predictability of the variables. Moreover, the Impulse Response Function (IRF) examines the behavior of one variable after a change in another, utilizing the time-series dataset from the first quarter of 1959 to the second quarter of 2022. This work demonstrates that expansionary monetary policy does have a positive impact on economic growth in the short term though it does not last long. However, in the long term, inflation, measured by the Consumer Price Index (CPI), is affected by expansionary monetary policy. Therefore, if the Federal Reserve wants to cease the expansionary monetary policy in the short run, this should be done appropriately, with the fiscal surplus, to preserve its credibility and trust in the US dollar as a global store of value asset. Also, the paper’s findings suggest that continuous expansion of the Money Supply will lead to a long-term inflationary problem. The purpose of this research is to bring the spotlight to the side effects of expansionary monetary policy on the US economy, but also allow other researchers to test this model in different economies with different dynamics. Full article

Although empirical studies show that different types of loans have different risks (moreover, consumer credit risk is higher compared to other types of loans), it is common to study the credit risk of the banking sector as a whole, or of an individual bank’s whole loan portfolio, and the macro-economic factors affecting it (without grouping them by type of loan). Thus, an analysis of the credit risk of the whole loan portfolio (measured by all non-performing loans) is insufficient. Therefore, the aim of this research is to identify the macroeconomic determinants of the consumer loan credit risk and quantitatively assess their impact in Central and Eastern European countries. After the analysis of scientific literature in the field of credit risk determinants, a detailed classification of factors influencing banking credit risk is proposed. The distinguishing feature of the classification is that the factors influencing credit risk are classified at five different levels; twelve groups of general macroeconomic conditions variables were selected as the potential factors of NPLs. This classification can be useful to better understand and investigate the factors influencing banking credit risk for the whole loan portfolio (in the same way as the factors that affect the credit risk of different types of loans, e.g., consumer loans). Using the methods of constant, fixed and random-effects panel analysis, simple OLS, least squares with breakpoints regression analysis and Markov regime-switching models, the impact of the macroeconomic variables from twelve separate groups is evaluated. The data from 11 CEE countries are used, and the period from 2008 to 2020 is covered. The results of this assessment reveal that in the group of CEE countries, such variables as GDP and labour market variables appeared to have contributed to the increase in the share of non-performing consumer loans, while inflation and real estate market variables were related to the decrease in consumer NPLs; at the same time, the impact of variables form other groups appeared to be mixed-nature or insignificant. The results of this research are useful in that they allow the identification of the most important determinants of consumer loan credit risk and thus allow making assumptions about NPL changes due to the changing macroeconomic situation. In the case of Lithuania, this kind of study (assessment of macroeconomic determinants of consumer loan credit risk) was conducted for the first time. Consumer loan credit risk assessment is especially relevant in an increasing interest rate environment, and deeper analysis can help banks and other financial institutions to manage credit risk. On the other hand, a better understanding of the main influencing factors of the macroeconomic environment can help central banks and other official institutions take appropriate monetary and fiscal policy decisions to ensure a good credit transmission channel for sustainable economic growth. Full article

This paper discusses critical considerations in the design of central bank digital currency (CBDC) in West Africa through a comparative case study of Ghana’s (eCedi) and Nigeria’s (eNaira) design frameworks. This paper analyses CBDC design options framed through context (digital payment landscape and CBDC objectives), technical aspects (design principles, architecture, risks), use cases, and deployment plans. This study conducted a thematic analysis of official CBDC design documents to identify similarities, differences, and patterns. The results indicate more similarities between the eCedi and eNaira designs than differences. Differences were observed in the CBDC deployment context, risk profiles, and plans. Surprisingly, neither country has articulated the detailed legal and regulatory environments for CBDC. This paper highlights the use of CBDC designs to promote citizens’ welfare by using financially inclusive policy goals within central banking’s welfare functions, thereby extending their traditional role. Policymakers should focus on adaptive legal and policy design outlooks to address uncertainties associated with CBDC. This paper is important because it is one of the first to contribute to a detailed comparison of Ghana and Nigeria’s CBDC design frameworks. Full article

In this paper, we show that in order to obtain a sound identification of Euro Area monetary policy shocks, one needs to deal with the interaction of the European Central Bank and the US Federal Reserve. In other words, a proper identification of monetary policy shocks for an open economy like the Euro Area requires consideration of the US policy rate. Indeed, when we exclude the Federal Funds Rate from an estimated VAR model including a set of Euro Area variables, i.e., Eonia, inflation and unemployment, we detect a wrong sign in the response of inflation to contractionary monetary policy shocks. Moreover, even adding the world price of oil does not help to overcome the problem. Instead, for a sample covering the period 1999–2019, when the Federal Funds Rate and the Euro–Dollar exchange rate are added to the VAR model inflation shows statistically non-significant effects for two years and thereafter decreases. Under this specification of the model, a clear and significant unemployment inflation trade-off emerges. These conclusions are confirmed by using industrial production instead of the unemployment rate in the VAR model. Full article

The purpose of this paper is to conduct a comparative analysis of monetization as a priority of the new monetary growth of the economies using the example of the Russian economy, identifying new trends in global practices of monetary factor management, as well as the search for ways to stimulate economic growth using the best international experience. Our paper tackles the novel research question of whether changing the priorities of monetary policy from targeting (and curbing) inflation to stimulating economic growth might yield more favorable economic results and what best world practices should be appropriately introduced in Russia to improve the effectiveness of monetary policy. The key results of the paper are focused on a comparative analysis of the economies’ development under the influence of monetary factors in comparison with the most progressive economies, the study of the best practices for increasing the monetization of national economies, and the identification of recommendations for determining the most optimal way to increase economic growth through the monetization of the economy. Monetarist views on the decisive role of fiat money in the development of the real sector of the economy, capital markets, payment and settlement systems, the standard of living of the population, and other important aspects of macro- and microeconomics have become the mainstream of government regulation. It seemed that by finding the right indicators of the relationship between interest rates, GDP, and inflation, all problems of economic growth could be solved. By increasing the amount of money faster than the achieved economic growth, it was believed that it was possible to stimulate GDP growth through monetary investments and credit, i.e., more money was issued than the value produced represented by the goods and services. Accordingly, new money that had no value had to create new value. We argue that monetization can be seen as the main factor in providing such incentives. Our results can be useful for central bankers, policymakers, and stakeholders in the banking and financial sector. The conclusions and recommendations of the authors are based on studies conducted using such research methods as content analysis, logical analysis, and statistical analysis. Full article

The aim of the article is to identify a degree of inclusive growth and to examine the influence of determinants of inclusive growth in the European Union (EU-27) countries, with particular emphasis on factors related to the influence of governments and central banks. The study took advantage of the weight correlation method, which was used to build an inclusive growth measure for the EU-27 for the years 2000, 2008, and 2020. For the construction of the inclusive growth rate, 42 factors were selected that affect inclusive growth in the economic, financial, and non-wage area. These determinants are found in the area of the influence of economic authorities, and mainly in the area of authorities responsible for conducting monetary and fiscal policy and general governance. On the basis of the built-up indicator of inclusive growth, it was noticed that among the 27 EU countries in the studied three years, only four countries distinguished themselves with the highest inclusive growth over the last 21 years, these are: Denmark, Luxembourg, Sweden, and Finland. On the other hand, invariably, three countries recorded the lowest inclusive growth, i.e., Bulgaria, Croatia, and Romania. The added value of the structure of the inclusive growth indicator was a possibility to observe which of the three areas: economic, financial, or non-wage, had a significant impact on the position of a given country in the compiled inclusive growth ranking. Full article