Search Results (132)

This study examines the asymmetric responses of stock market indices in Türkiye and Kazakhstan to oil price shocks during the 2010–2025 period. Using the Nonlinear Autoregressive Distributed Lag (NARDL) model, the study decomposes the nonlinear effects of oil price fluctuations on financial markets. Empirical findings reveal that in Türkiye, a net oil importer, the stock market exhibits a dual-sensitivity: while exchange rate dynamics (2.34) remain the dominant driver, oil price increases (−0.12) exert a direct and statistically significant negative pressure. In contrast, Kazakhstan, a net oil exporter, shows a high vulnerability to oil price decreases (−1.05) at the 1% significance level, confirming a strong asymmetric structure (p = 0.0122). Furthermore, the error correction speed is significantly higher in Türkiye (28%) than in Kazakhstan (4%), indicating divergent market efficiency and recovery mechanisms. These results demonstrate that financial market reactions to external shocks differ fundamentally based on energy trade structures. The findings suggest that oil-importing countries must prioritize exchange rate stability, while oil-exporting nations must develop specific policy buffers against the persistent downside risks of global energy cycles. Full article

This study investigates whether fluctuations in the real exchange rate give rise to symmetric or asymmetric J-curve effects in manufacturing trade between Türkiye and Germany, thereby positioning the analysis within and contributing to the broader scholarly discourse on exchange rate–trade balance dynamics. Using monthly data for the period 2013M01–2025M07, the paper first estimates a linear Autoregressive Distributed Lag (ARDL) model for the bilateral manufacturing trade balance and subsequently extends the framework to a nonlinear ARDL (NARDL) specification, which explicitly incorporates symmetry and asymmetry by decomposing real exchange rate changes into positive (depreciation) and negative (appreciation) partial sums. The linear ARDL results provide no evidence of a conventional J-curve and suggest that the aggregate impact of the real exchange rate is weak and often statistically insignificant. In contrast, the NARDL estimates uncover pronounced long-run and cumulative short-run asymmetries: real depreciations of the Turkish lira are associated with a persistent improvement in the bilateral manufacturing trade balance, whereas appreciations exert weak and statistically insignificant effects, a finding that remains robust when a real effective exchange rate measure is employed. Overall, the evidence indicates that Türkiye–Germany manufacturing trade does not conform to the standard J-curve pattern. These findings suggest that trade policy should adopt an asymmetric stance toward exchange rate movements: since depreciations yield persistent trade balance improvements while appreciations produce negligible effects, policies designed to support export competitiveness should prioritize the management of depreciation episodes rather than assuming symmetric adjustment dynamics. Full article

We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on ${(0,1)}^2$. To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. Full article

Given the context of the COVID-19 pandemic disrupting global logistics, coupled with the Russia–Ukraine war causing global energy price changes, examining both the linear and nonlinear associations between shipping cost and oil price is crucial in a global context. This study empirically exhibits the association among Global Commodity Prices Index (GPI), Oil Price (OP), Gold Future Price (GFP), and Baltic Dry Index (BDI) by employing Linear Autoregressive Distributive Lag (ARDL) as well as Nonlinear Autoregressive Distributive Lag (Nonlinear ARDL) from January 2003 to January 2023. The findings indicate that the influence of OP on BDI has a negative impact in the long run and a positive impact in the short run. Furthermore, the OP has an asymmetric effect on BDI in both the long and short terms. Finally, the predictive performance of the NARDL model outperforms the ARDL model in forecasting OP and BDI. The empirical findings derived from the ARDL and NARDL algorithms offer valuable insights for policymakers in designing public policies and for investors in portfolio construction. Full article

This study applies a multiple threshold nonlinear autoregressive distributed lag (MTNARDL) model to examine the asymmetric impact of real exchange rate volatility on Vietnam’s exports and imports with its three leading trading partners: China, the United States, and South Korea. By allowing trade responses to vary across different volatility regimes, the MTNARDL framework provides a flexible approach to capturing potential nonlinear adjustment dynamics that cannot be addressed by single-threshold models. Moreover, using bilateral import and export data helps reduce aggregation bias. The results indicate the presence of asymmetric long-run adjustment dynamics in the relationship between real exchange rate volatility and bilateral trade flows, while short-run effects are generally weak and less consistent across trading partners. These findings provide valuable insights into the complex effects of exchange rate volatility, enabling policymakers to more effectively design and manage policies to mitigate its impact. Full article

This research examines how financial development, energy efficiency and energy inefficiency, green technology, and research and development (R&D) affect environmental conditions and ecological footprint in the Group of Seven (G7) countries. The study combines two theoretical models, including the Environmental Kuznets Curve (EKC) and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT), within a unified framework. This study addresses a key gap in the literature by examining whether financial development follows a distinct nonlinear environmental pathway beyond the traditional income-based EKC framework. Using annual data from 1994 to 2023, the analysis applies the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) approach to account for cross-sectional dependence and long-run heterogeneity among G7 economies. The findings indicate that the G7 does not support the traditional EKC hypothesis, although a U-shaped relationship between environmental degradation and financial development exists. Energy-related factors play a central role in shaping environmental outcomes, with energy efficiency and green technology significantly alleviating environmental pressure, and green technology proving to be the most influential among them. While energy inefficiency is associated with greater environmental stress, its effect remains statistically insignificant, suggesting that efficiency improvements are more decisive than reductions in inefficiency. Beyond energy dynamics, financial development and R&D investment also contribute significantly to environmental improvement, highlighting the complementary role of technological progress and financial systems in promoting environmental sustainability. The robustness results confirm the main findings. Overall, the study underscores the critical role of technological innovation and energy efficiency in promoting sustainability and challenges the applicability of the traditional EKC framework in advanced economies. The findings contribute to a deeper understanding of the multifaceted relationships between financial development, energy efficiency and inefficiency, green technology, and R&D, and their implications for sustainable development. Full article

Climate change has emerged as one of the defining risks in recent years. These risks are associated with economic losses and, ultimately, the stability of the financial system. This study examines the impact of climate change on financial stability in Namibia using quarterly data spanning from the period 2009 to 2023. The Nonlinear Autoregressive Distributed Lag (NARDL) approach is employed to assess how climate change asymmetrically affects the stability of Namibia’s financial system. The findings reveal that both increases and decreases in rainfall, as well as higher temperatures, exert negative long-term asymmetric effects on financial stability, while rises in CO₂ emissions appear to enhance it. Accordingly, this study recommends the integration of climate-related risks into financial institutions’ risk assessment frameworks, together with the adoption of long-term monitoring and mitigation strategies. Finally, regulators are also encouraged to conduct climate stress tests to assess the resilience of the financial system under varying climate scenarios. Full article

This study examines the determinants of U.S. CO₂ emissions and provides evidence to inform more effective carbon-reduction policies. Using Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) models, the analysis covers January 1997 to February 2022 across four end-use sectors: Residential, Commercial, Industrial, and Transportation. The models capture both long-run equilibria and short-run adjustments between emissions and key drivers, including industrial production, interest rates, climate policy uncertainty (CPU), and energy prices. Results indicate a long-run asymmetric relationship in which economic growth and interest rates differentially affect total emissions, while CPU exerts a significant negative influence only in the transportation sector. Methodologically, the combined ARDL–NARDL approach offers robust evidence of nonlinear and asymmetric effects of macroeconomic and policy variables on emissions. These findings underscore the need to integrate economic and financial conditions into climate policy design and suggest that sector-specific measures—particularly targeting transportation—may substantially improve the effectiveness of carbon-mitigation strategies. Full article

This study examines the determinants of inflation volatility in Tunisia, focusing on central bank independence (CBI), economic transparency, and macroeconomic fundamentals. Although CBI is widely regarded as essential for monetary credibility, its effectiveness depends on its institutional framework. Our contribution is twofold. First, we develop a theoretical framework based on game theory to illustrate how the effectiveness of economic transparency and CBI shapes the welfare of both the central bank and the private sector in the presence (or not) of fiscal policy. Second, we use a binary threshold nonlinear autoregressive distributed lag (NARDL) model to capture long-run relationships and a Markov-switching GARCH (MS-GARCH) framework to model volatility dynamics. As a continuous measure, CBI has no significant impact on volatility. Paradoxically, high de jure independence in a binary regime is associated with a slight increase in inflation fluctuations. This indicates that legal independence alone is insufficient without fiscal discipline or effective coordination between the monetary and fiscal authorities. Notably, under fiscal pressure, greater CBI substantially reduces inflation volatility, highlighting the need for a coherent macroeconomic framework. Economic transparency generally increases short-term volatility but stabilizes inflation when supported by credible fiscal signals. Among the macroeconomic fundamentals, volatility in broad money is strongly destabilizing, whereas fluctuations in industrial production and the real exchange rate are largely insignificant. Government spending and exposure to external shocks, including import prices and geopolitical risks, further amplify this volatility. The observed negative trend over time reflects gradual improvements owing to policy reforms. Policy recommendations emphasize the establishment of genuinely independent and credible monetary institutions, enhancing coordination with fiscal policy, improving communication strategies, and strengthening risk management. Full article

This study examines the determinants of food price inflation in Morocco using a comprehensive econometric framework based on an Autoregressive Distributed Lag (ARDL) model. Relying on monthly data and controlling for major structural shocks, the analysis captures both the short-run dynamics and long-run equilibrium relationships between food prices and key macroeconomic, external, and climatic variables. The estimation results reveal strong inflation inertia, indicating that past food prices are the most significant driver of current price changes. External cost variables, including the nominal effective exchange rate, world oil prices, and international cereal prices, are mostly insignificant in the short run, suggesting a muted and delayed pass-through. Import volumes exert a marginal but lagged effect, while rainfall emerges as a consistent determinant, highlighting Morocco’s structural vulnerability to climatic variability. The error-correction term is negative and significant, confirming the existence of a stable long-run relationship. Long-run estimates show that oil prices and precipitation remain relevant drivers of food price dynamics, whereas the exchange rate appears largely neutral, reflecting the impact of subsidies, managed exchange rate arrangements, and domestic supply-chain characteristics. Nonlinear NARDL estimations provide no evidence of asymmetric exchange rate pass-through. The findings underscore some policy recommendations to enhance agricultural resilience, strengthen climate adaptation, and improve supply-chain efficiency for food price stability. Full article

This paper examines the impact of natural resource rents on the economic growth of Tunisia between 1990 and 2023, emphasizing the aspect of resource diversification. The annual time-series data extracted from the World Bank’s World Development Indicators were analyzed using the Autoregressive Distributed Lag model to outline both the short- and long-run dynamics. The results confirm the existence of a long-term relationship between economic growth and oil, natural gas, mineral, and forest rents. Among them, oil and forest rents have strong positive long-term impacts, whereas natural gas and mineral rents contribute relatively moderately due to the structural inefficiencies and absence of value-added activities in these sectors. It was also found that the labor force participation has been affecting growth adversely with continuous impacts, which are driven by skill mismatches, low productivity, and high unemployment, hence indicating structural labor market imbalance that weakens the growth effect of labor. On the other hand, capital formation is still one of the key drivers of long-term growth. The findings highlight the rationale for diversification of the economy, governance reforms, and sustainable management of resources. However, the study suffers from some limitations due to data availability and excluded institutional variables, apart from being narrowed to a single-country case study, which might affect the generalizability of the results. Future works could consider incorporating the indicators of governance, examining nonlinear effects, or expanding the analysis into a multi-country framework. Full article

This paper investigates the effect of Environmental, Social and Governance (ESG) scores and Credit Default Swap (CDS) spreads on the profitability of Halkbank, one of the biggest state-owned banks in Türkiye, an emerging economy. To this end, we employ Non-linear Autoregressive Distributed Lag (NARDL) and Markov Switching Regression (MSR) methods, taking into account non-linear market risks, using Halkbank’s quarterly data consisting of 63 observations for the period 2009Q1–2024Q3. Moreover, to prevent multicollinearity, we aggregate banking-specific and macroeconomic indicators into a single composite index using Principal Component Analysis (PCA). Our MSR findings suggest that ESG scores and CDS spreads negatively affect bank profitability and that these effects are particularly pronounced during periods of high market volatility. Similarly, NARDL findings suggest that ESG scores have asymmetric effects on bank performance, with both positive and negative changes in ESG performance having a negative impact on profitability, and moreover, negative changes have a more negative impact on profitability. This means that the bank’s sustainability initiatives may be costly and negatively affect profitability in the short run, but these effects will be more negative if initiatives deteriorate. Our findings emphasize the need for banks to adopt a gradual ESG approach that enables them to increase their capacity without compromising financial stability and for regulatory structures to have a flexible and sophisticated risk management framework capable of rapidly adapting to different market conditions. Therefore, our study provides valuable insights to sector managers and policymakers regarding the financial implications of sustainability approaches. Full article

Concerns about the increasing ecological harm caused by human activities have led to greater recognition of the need to address environmental degradation. Policymakers are implementing actions and strategies to alleviate the detrimental effects of climate-change-driven environmental degradation. One of the policy tools for internalizing the external costs of environmental degradation is eco-taxation, which provides incentives for businesses and individuals to adopt cleaner technologies. Investment in renewable energy has surged in solar and wind due to technological advancements, policy backing, and cost reductions. This study examines the long-term environmental effects of eco-taxation and renewable electricity in France between 1998 and 2020, utilizing a novel Fourier autoregressive distributed lag (NARDL) econometric model. The results indicate that eco-taxation and renewable electricity have nonlinear and asymmetric effects on the environmental sustainability of France. In terms of policy implications, these findings provide policymakers in France with nonlinear and asymmetric insights. The government could optimize eco-taxation design and revenue recycling by integrating its existing green budget approaches with mainstream climate objectives into all government spending and taxation, thereby ensuring policy consistency and preventing environmentally harmful subsidies. Additionally, France could accelerate and diversify renewable deployment by committing to higher renewable generation targets, given the positive nonlinear impact without a rebound effect, or investing in grid flexibility and interconnection through grid modernization, smart grids, and cross-border interconnections. Full article

Capital flow, integral to the global economy, is significantly influenced by business potential and institutional environments. As one of the world’s largest economies, China’s outflow plays a crucial role in the rapid development of its economy. This study examines domestic investment into public and private components to avoid aggregation bias, whether China’s outward foreign direct investment (OFDI) serves as a substitute or complement to local investments, and how local institutional quality mediates this relationship. We employed Dynamic Autoregressive Distributed Lag model ARDL simulation methods for the period of 1996–2021 in order to control endogeneity, auto-correlation, cross-sectional bias, as well as heteroscedasticity issues, which normally arise in time-series datasets. Our findings reveal that OFDI has a dual impact on local economies. Firstly, OFDI has a generally positive effect on private and public investment, but this relationship is nonlinear. Furthermore, institutional quality significantly influences private investment more than public investment. Additionally, higher interest rates are shown to adversely affect both private and public investments by increasing borrowing costs. These results offer valuable insights for policymakers aiming to optimize investment flows and economic stability. Specifically, fostering institutional quality can amplify the positive spillovers of OFDI on private investment, while mitigating its crowding-out effects on public investment. Full article

This study looks at the impact of geopolitical risk on the volatility of wheat futures returns over the period 2012–2023, while controlling for inventories, shipping rates, and speculative activity. Using the volatility of CBOT first nearby futures returns, we apply a quantile regression approach to assess the impact of the variables on different parts of the volatility distribution. More specifically, we adopt the Quantile Autoregressive Distributed Lag (QARDL) model, which allows for examining the dynamic short- and long-run effects. We find that geopolitical risk has a non-linear, large positive effect on the top quartile of the distribution of wheat futures returns. We also show that the response of the volatility of wheat futures to shocks in the control variables is mostly non-linear across the conditional quantiles, significant in the tails and not around the median. Full article