Using quarterly data for Turkey spanning 1995 Q1 – 2024 Q4, we estimate closed- and open-economy hybrid New-Keynesian Phillips Curves that allow for time-varying and asymmetric slopes. Controlling for three structural breaks—the **2001 banking crisis, the 2018 currency turmoil, and the 2020-21 pandemic—**a 1-percentage-point positive output-gap shock raises CPI inflation by 0.10–0.14 pp in the short run, while the long-run pass-through from expected to current inflation ranges from −0.02 pp to −0.12 pp. Error-correction coefficients of −0.26 to −0.50 imply that disequilibria vanish within two to four quarters. Robustness checks using state-space time-varying-parameter estimators confirm the post-2021 steepening of the curve. Recent evidence that global forces have flattened—but not eliminated—the Phillips relationship (Kabundi, Poon & Wu 2023) and that Turkey’s post-pandemic inflation is increasingly demand-driven (Akarsu & Aktuğ 2025) supports our results, whereas earlier closed-economy estimates understate the slope by ignoring import-price spill-overs. Our open-economy specification therefore produces a steeper, yet still forward-looking, curve and explains 82 % of in-sample inflation variance. Policy implications. (i) Credible expectation-anchoring could halve the persistence of inflation shocks. (ii) Output-gap management must be pre-emptive, because positive gaps accelerate prices more than negative gaps decelerate them. Consistent communication, demand-smoothing tools, and measures that curb import-price pass-through together give the Central Bank of Türkiye the greatest leverage in reaching its 2025–26 inflation target.
Environmental pressures in the Asia-Pacific region have intensified alongside rapid economic growth, structural transformation, and rising energy demand. This study investigates how renewable energy, capital formation, and economic development shape the ecological footprint (EF) across 38 Asia-Pacific economies from 2000 to 2022. Using a dynamic STIRPAT-based empirical framework, we examine both aggregate EF and its six subcomponents. The results show that both renewable energy consumption and production reduce EF, highlighting the environmental benefits of expanding clean-energy capacity. Economic development exhibits a U-shaped relationship with EF: early growth stages improve efficiency and reduce ecological pressures, whereas higher income levels eventually increase EF. Capital formation raises EF, indicating continued investment in resource- and energy-intensive activities, while population is associated with lower EF, likely reflecting demographic transition and efficiency gains. Subcomponent analyses reveal heterogeneous drivers, with the carbon footprint most closely mirroring overall EF dynamics. These findings underscore the need to accelerate clean-energy transitions, redirect investment toward green sectors, and strengthen environmental governance to maintain ecological sustainability as economies develop.
Banks play a vital role in the global economy but face a challenge of complying with regulations regarding board gender diversity and environmental, social, and governance (ESG) practices while maintaining cost efficiency. Applying a two-stage modified Data Envelopment Analysis (DEA) cost efficiency model with Simar-Wilson bootstrap-based truncated regression procedure and the instrumental variable (IV) Tobit robustness checks, this study analyzes the associations among board gender diversity, ESG performance scores, and bank efficiency across 94 Asian banks from 2010 to 2020. The empirical results show that female directors are positively associated with bank efficiency while exhibiting an inverted U-shaped diminishing marginal return. Environmental performance is positively associated with efficiency, whereas social performance is negatively related, and governance performance exhibits an insignificant relationship. Furthermore, the interaction between gender diversity and each ESG performance pillar exhibits a positive relationship with bank efficiency. The findings suggest an optimal threashold of 10%-13% for female board representation. Banks may consider gender diversity as a strategy to implementing ESG initiatives, and ultimately correspond with a positive association with higher cost efficiency.
Today, algorithmic trading (AT) plays a crucial role in financial forecasting. This study explores AT strategies for an emerging market, Borsa İstanbul (BIST). Analyzing Environmental, Social, and Governance (ESG)-focused stocks from the BIST Sustainability Index, a robotic trading algorithm implemented in C# optimizes trading parameters for return. Key technical indicators; Relative Strength Index (RSI) and Simple Moving Average (SMA) evaluate trends and improve decisions. Results show longer timeframes achieve higher profitability, while shorter intervals face volatility. The study underlines the potential of tailored AT strategies in emerging markets, offering insights for small investors to manage risk and return. Findings emphasize the role of local market dynamics in building robust, algorithm-driven trading solutions.
Drawing on the entropy law, this paper proposes a moving-target model to investigate the long-run convergence of countries' per capita income toward the average income of their reference group. Using evidence from the global economy and the European Union, we show that income convergence follows dynamics consistent with entropy-based physical systems. However, while convergence is observed across countries, regional and sub-regional disparities within countries often widen, indicating that convergence at the macro level may coexist with divergence at lower territorial levels.
The New Normal following the outbreak of the war in Ukraine highlights a new dimension of the energy sector: its strategic importance for economic stability and energy security. This study assesses the socio-economic determinants of renewable electricity production in 27 European Union member states over the period 2000–2024. More specifically, it examines whether innovation capacity, research and development expenditure, energy-use efficiency, and selected macroeconomic and demographic factors are associated with annual renewable-production growth at different points of its distribution. The dependent variable is renewable electricity production, while the main determinants capture innovative capacity, research and development (R&D) expenditure, and energy-use efficiency, complemented by macroeconomic and demographic controls. Using the panel dataset, we estimate two-way fixed-effects recentered-influence-function regressions and a pooled Common Correlated Effects specification, with Driscoll–Kraay inference as a robustness exercise. The Two-Way Fixed Effects (TWFE) and Driscoll–Kraay results indicate positive associations of energy-use efficiency at several middle quantiles, alongside negative consumption associations, while population growth is positive at Q90. These findings suggest that efficiency improvements may facilitate renewable-production growth, whereas rapid increases in household demand may temporarily outpace additions to renewable capacity. The positive population-growth coefficient at Q90 also indicates that demographic expansion may support renewable growth when accompanied by adequate infrastructure and urban planning. The CCE-RIF coefficients are not statistically significant at conventional levels, indicating that the conclusions are sensitive to allowing for heterogeneous responses to common shocks. From a policy perspective, the findings support an integrated approach combining energy-efficiency measures, demand-side management, grid modernization, storage investment, and the coordination of demographic development with renewable-energy infrastructure.
The paper examines the dynamics of financial market volatility and systemic risk during major crisis episodes over the period 2019-2025 using high-frequency intraday data for a broad set of international stock market indices, and proposes a high-frequency, volatility-based indicator designed to signal the emergence of systemic risk. Covering major global and regional stress episodes - including the COVID-19 crisis, the Russia - Ukraine war, and the subsequent energy and inflation shocks - and using five-minute data, the analysis employs a parametric Realized GARCH framework and shows that high-frequency estimates of conditional volatility and volatility shocks provide timely and informative insights into the onset, severity, and cross-market transmission of financial stress. Empirical results reveal that major crises, notably the COVID-19 pandemic and the Russia-Ukraine war, are associated with sharp and persistent increases in volatility across global equity markets, underscoring the suitability of high-frequency volatility for real-time stress monitoring. The paper also proposes a systemic risk indicator designed to detect the early stages of turbulent periods that may evolve into financial crises. The empirical results show that volatility estimated from high-frequency data exhibits pronounced and timely responses to major international shocks and that certain benchmark and regional indices consistently act as leading indicators. In particular, increases in conditional volatility in major global markets and in neighboring countries precede similar developments in small open economies, highlighting the dominant role of international spillovers in shaping domestic financial stress. The findings suggest that the proposed volatility-based systemic risk indicator can provide valuable early warning signals for policymakers and market participants, enabling more timely and informed responses to emerging systemic risks for better systemic risk monitoring and macroprudential policy analysis.
This study aims to investigate the tax buoyancy hypothesis in an asymmetrical framework. Increasing levels of gross domestic product (GDP) cause higher levels of tax revenue for economies. However, the magnitude of this impact varies from country to country, or year by year. Some countries transfer more from GDP to tax revenue, while others transfer less. From this perspective, in the present study, the tax buoyancy is estimated for the Turkish economy over the 2006Q1-2022Q3 period by considering the asymmetrical effects. The results obtained from the nonlinear autoregressive distributed lag model suggest that tax buoyancy is valid for the personal income tax while for tax on goods and services, and for corporate income tax there is no evidence favoring tax buoyancy. In other saying, the discretionary changes are effective only for personal income tax. Moreover, the positive and negative shocks affect tax revenues at different rates, and in some cases, even insignificant impacts were observed for the negative shocks. The findings may simply guide policymakers in two ways; first, personal income tax is the only tax type causing to tax buoyancy; second, the positive and negative GDP shocks affect tax buoyancy at different rates.
This paper analyzes the link between political responses to major crises, based on observations from 18 emerging countries and 27 advanced economies during the COVID-19 period, and sovereign bond yields. Using an unconditional quantile regression approach, we show that the dynamics, of sovereign bond yields respond asymmetrically to a series of covariates, such as industrial production, interest rates, business confidence, inflation, the exchange rate, and unemployment. The paper highlights a more pronounced effect in advanced economies compared to emerging ones, thereby contributing to the existing literature. Moreover, the decision to postpone tax payments for both individuals and corporations led to an increase in confidence in the solvency of bond-issuing countries, regardless of whether the economies were emerging or advanced.
This paper analyses the degree of connectedness across equity, foreign exchange, bond, and CDS markets among Central and Eastern European (CEE) countries. By employing a unified Diebold-Yilmaz connectedness framework, univariate GARCH models, and GLS event regressions, we provide a multimarket and multicountry de-sign that documents both crosscountry and cross-asset linkages. We simultaneous-ly explore the static and dynamic connectedness, offering a comprehensive picture of the existing patterns of comovements during turbulent periods, including the COVID-19 crisis and the Russia-Ukraine war. Moreover, we highlight the regime-dependent nature of financial integration regarding return and volatility spillovers in the CEE region. The findings indicate that CEE countries are more connected among themselves than with Germany, particularly regarding government bonds and CDS spreads. The estimates reveal that the dynamic connectedness levels increase dur-ing turbulent times, highlighting strong contagion effects, particularly in equity mar-kets. In the case of government bonds, the economic fundamentals prevail over con-tagion effects during crisis periods.
The interaction between digital transformation, govemance quality, and infrastructure development has become increasingly central to understanding the structural foundations and sustainability of modern Gig economies. This paper analyzes the relationships among technological Innovation (Tech), infrastructure (infra), governance (Gov), and digital transformation (Dirans), and the growth of the Gig economy (Gig) in BRICS countries (2016 to 2024). The study employs the Pooled Mean Group (PMG) estimator as the baseline method and me Common Correlated Effects Mean Group (CCEMG) estimator for robustness, to analyze the combined effect of Intra and Tech with Gov and Dtran on online labor markets (Gig). Results show that there is a negative impact of interaction between Tec and Infa (-0.696) and Global connectivity Bonbon) 5665 & iecy;& mcy;& acy;& scy;& ycy; (2 1021 apr Diwas 40665) aave a positive orig & iecy;& dcy;& scy;& iecy; on the Gig in the long term Similarly, the governance-digital transformation interaction (-0.799) and infrastructure (-1.493) are producing negative impacts, but financial development (1.955) and corporate activity (0.134) have positive impacts on the Gig. These results suggest that human capital and access to digital processes increase the growth of the Gig economy. However, structural and institutional interactions can create inefficiencies Policymakers should priontize strengthening digital inclusion and human capital while reforming governance-infrastructure complementarities to minimize institutional inefficiencies and fully realize the growth potential of the Gig economy. -
Given the increasing disparities in income/wealth in the last decade, the debate around the causes is still actual. However, the research on financial development as a driver of economic inequality remains scarce. The aim of this paper is to assess the impact of financial development on income/wealth inequality in the European Union in the period 1990-2023. The main results support the inverted-U pattern between financial development and top income/wealth shares and a U-shaped connection for financial development-bottom 50%'s income/wealth share. These findings suggest that initial financial development can increase inequality, but it reduces it when financial systems become more mature and inclusive. Self-employment and banking crises played the role of mediators in these relationships. Factors like economic growth, corruption, private credit, and public spending have also significant impact on income/wealth distribution.
The aim of this research is to assess the validity of purchasing power parity (PPP) hypothesis and its extensions for the Republic of Croatia prior to its adoption of the euro. To this end, the period from January 1994 to June 2022 is examined via testing for stochastic trend(s) utilising monthly data. Initially, the conventional augmented Dickey-Fuller (ADF) test equations are examined for the presence of structural breaks and non-normal errors. Following this procedure enables the study to make several important contributions. First, it broadens the residual augmented least squares (RALS)-based ADF testing procedure by utilising the fourth power of the error terms. Second, by examining nearly the entire kuna period, this research investigates the stability of the pre-euro Croatian currency, shedding light on Croatia's currency integration process into the Eurozone. Third, unlike other studies conducted for Croatia, this study not only assesses the formal PPP but also examines the validity of the extensions of the PPP hypothesis. The findings reveal that neither the formal PPP hypothesis nor its extensions are valid for Croatia and its trade partners, highlighting potential challenges in Croatia's euro adoption and underscoring the need for strategic policy measures to facilitate a successful transition.
Using quarterly macroeconomic data for 41 countries, this study examines how sovereign credit rating changes affect exchange rates. An innovation in rating changes has statistically significant short-run effects on exchange rate movements in some subsamples. The exchange-rate response is statistically significant in the high-GDP subsample but not in the low-GDP subsample. Our empirical results suggest that monitoring credit rating changes can be useful for tracking exchange rate movements in high-GDP countries.
In the current context of globalization, with migration trends intensifying annually, it becomes essential to study the influence of remittances on growth. Accordingly, this research assesses the long-term effect of remittances on economic performance [proxied by GDP per inhabitant] in the EU-27 from 2007 to 2023. In this respect, we apply the Granger causality test as well as two-step system generalized method of moments. The econometric outcomes reveal that (1) there is a bidirectional relationship between remittances and GDP per inhabitant, (2) remittances, R&D expenditure, and trade openness exert a positive lasting impact on economic performance, while (3) unemployment and a country membership in the 'new' EU states category (compared to 'old' EU states category) hinder growth. Additionally, these results remain consistent even when the econometric model is changed
For managers, making the right decisions in a dynamic environment becomes critical. This study explores how forecasting methods convert reservation data into actionable information for decision-making in the hospitality industry. Based on data obtained from 5,087 accommodation facilities in T & uuml;rkiye, this study forecast reservations, number of nights, cancellations, and revenue using deep learning model (LSTM) and machine learning models (linear regression, robust linear regression, artificial neural networks). Comparative analyses reveal that LSTM achieves the highest forecast accuracy and offers a valuable tool for managerial decision-making. This finding demonstrates the practical application of advanced forecasting methods, enabling managers to make more accurate decisions in dynamic and uncertain environments. The study also examines this issue using real data.
This paper presents a re-examination of the relationship between unemployment and economic performance in Central and Eastern Europe with focus on the informal economy and institutional quality. A two-way fixed effects model with Driscoll-Kraay standard errors is applied to NUTS-2 regional panel data covering six countries (Bulgaria, Croatia, Czechia, Hungary, Poland and Romania) over 2002-2021. The results demonstrate strong regional support for Okun's Law showing an inverse and significant relationship between unemployment and real GDP per capita. A one percentage point increase in unemployment reduces regional output by approximately 0.7-0.9%. The size of the informal economy is also negatively connected to economic performance, suggesting that informality plays an important role in constraining productivity in the formal sector. Extensions with governance indicators indicate that improvements in the rule of law and government effectiveness have a positive relationship with regional economic performance while political stability has a weaker relationship intra-country. These findings reinforce the importance of labour market conditions, formalisation and institutional quality in the growth of the regional economy in Central and Eastern Europe.
This paper examines the dynamics and firm-level determinants of stock market liquidity in European equity markets, focusing on the role of institutional ownership and the information environment. Using a balanced panel of STOXX Europe 600 firms over the period 2005-2025, we measure liquidity by stock turnover and estimate dynamic panel models with firm fixed effects, employing system GMM estimators to address endogeneity arising from lagged liquidity and potentially endogenous firm characteristics. We document strong persistence in stock liquidity: past turnover explains a substantial share of current turnover even after controlling for size, growth opportunities, capital structure, dividend policy, and time effects. Once these controls are included, institutional ownership does not show a robust, statistically significant association with liquidity, suggesting that the mere presence of institutional investors is not a primary driver of trading activity. In contrast, the information environment matters: firms with more favorable analyst recommendations tend to exhibit higher turnover, consistent with the idea that analyst activity enhances liquidity by improving information flows and stimulating trading interest. An analysis of standardized coefficients shows that liquidity dynamics are dominated by their own history and firm size, with analyst recommendations playing a non-trivial role, while other firm characteristics and institutional ownership appear less influential.
This study employs a rolling-window quantile Granger causality test to analyse the dynamic link between U.S. trade policy uncertainty (TPU) and the sustainable farming index, using the indices of Caldara et al. (2020) and Baker et al. (2016). The results show a nonlinear and time-varying bidirectional relationship: Caldara's TPU strongly influences stock fluctuations before 2022, especially at higher quantiles, while reverse effects appear mainly in the upper tails. Baker's TPU exhibits similar forward causality but limited feedback. These findings offer useful policy insights.
The nonlinearity of financial time series is reflected in "stylized facts "such as the leverage effect, volatility clustering, and fat-tailed distributions. In this context, the following paper aims to test a new approach and regime regarding the volatility forecasting process and evaluate its robustness. We employed a Bayesian estimation technique coupled with the GARCH (1,1) model with Student-t innovations. The model was applied to the daily log returns of the Cboe Volatility Index (VIX) spanning 14 years (2011-2024), using both rolling and non-rolling windows. The results revealed that our GARCH model, with a Bayesian approach for parameter estimation, can provide a plausible forecast. Moreover, for the robustness of forecast accuracy, we compare the results of the Bayesian approach with those of frequentist models, both symmetric (GARCH) and asymmetric (EGARCH, APARCH). The DM test reveals that the Bayesian approach generally outperforms the frequentist models both in non-rolling window and in rolling window, except for the APARCH and EGARCH models under the rolling window approach.
