Search Results (296)

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

The efficient market hypothesis, which holds that prices completely reflect available information, is commonly used in financial market analysis. However, emerging empirical evidence shows that market efficiency develops with time, as posited by the adaptive market hypothesis (AMH), with predictability varying across shifting economic and behavioural regimes. Despite the increasing use of deep learning in financial forecasting, there has been little systematic investigation into whether neural network topologies can successfully identify time-varying efficiency trends across diverse markets. Furthermore, the relevance of transfer learning in studying adaptive behaviour between foreign exchange markets and extremely volatile cryptocurrency markets has received little attention. Using these data, we investigate the AMH by comparing the forecasting performance of various deep learning architectures and determining whether knowledge transfer from a relatively stable fiat currency market, Botswana Pula/US Dollar (BWP/USD), improves the predictive accuracy in a highly volatile cryptocurrency market, Bitcoin/US Dollar (BTC/USD). We use daily data from 1 January 2015 to 11 January 2026 to develop deep neural networks (DNNs) and alpha-recurrent neural networks, and, for generalisation, we benchmark using a recurrent temporal neural network (RTNN), a domain-adversarial neural network (DANN), and KLIEP-based importance-weighted regression. A transfer learning technique is used, in which models are initially trained on BWP/USD and then re-estimated on BTC/USD without freezing any network layers, ensuring complete flexibility and enabling parameters to respond to changing market dynamics. Out-of-sample accuracy measures and rolling long-memory diagnostics are used to evaluate forecast performance in terms of time-varying efficiency. The findings reveal that the RTNN regularly outperforms other forecasting models across marketplaces. Predictive accuracy fluctuates with time, and rolling long-memory measurements show persistent departures from random walk behaviour, which supports the AMH. Transfer learning improves predictive stability in the cryptocurrency market by identifying the existence of transferable informational structures between fiat and digital asset markets. Overall, our results support the idea that market efficiency is dynamic rather than static, and they show that adaptive deep learning systems are an excellent way to test the AMH. The paper suggests that cross-market transfer mechanisms and adaptive modelling methodologies be investigated further in growing foreign exchange and cryptocurrency markets. Full article

Carry strategies in developed markets are well studied, but their effectiveness in emerging government bond markets remains less well understood. This study analyzes cross-curve carry strategies in the Indonesian government bond market from June 2009 to June 2025. The findings indicate that a term spread-based carry long–short portfolio delivers positive returns and exhibits persistence across rolling 10-year horizons throughout the sample period. However, performance tends to weaken during episodes of local currency depreciation. Duration-matched long-only carry portfolios also outperform the market benchmark after transaction costs, indicating practical value for investors. Overall, the findings suggest that carry strategies can be effective in the Indonesian government bond market and that the term spread-based carry measure provides a more robust signal than the alternative specification that incorporates roll-down effects. Full article

Exchange-rate forecasting requires modelling non-stationary dynamics, heavy-tailed shocks, and complex temporal dependencies. However, forecasting performance in emerging-market currencies is fundamentally constrained by intrinsic dynamical instability, while most existing approaches are evaluated primarily through predictive accuracy rather than the predictability limits of the underlying system. This paper develops a predictability-aware framework that combines nonlinear dynamical diagnostics with a Lévy-driven neural stochastic differential equation model. Drift and diffusion are parameterized by neural networks and driven by $\alpha$-stable Lévy motion, enabling the representation of non-Gaussian fluctuations, abrupt shocks, and regime changes. To learn under discontinuous dynamics, we introduce a structurally constrained training objective based on a strong-form discretization of the underlying SDE. To characterise intrinsic predictability, we employ phase-space reconstruction and maximal Lyapunov exponent estimation. These diagnostics are interpreted as finite-sample measures of trajectory divergence and effective instability in a stochastic system, rather than evidence of low-dimensional deterministic chaos—a distinction motivated by well-documented limitations of chaos testing in financial data. Experiments on multiple West African currency pairs demonstrate competitive short-horizon forecasting performance relative to econometric and neural baselines while providing a principled framework for analysing predictability degradation under heavy-tailed stochastic dynamics. Across currencies and model classes, forecasting accuracy deteriorates beyond horizons comparable to the estimated Lyapunov time, suggesting that forecast degradation reflects intrinsic dynamical instability rather than model-specific limitations. The results support the view that reliable exchange-rate prediction is fundamentally a short-horizon problem and illustrate how stochastic dynamical modelling and predictability diagnostics can be combined to characterise forecasting limits in heavy-tailed financial systems. Full article

Understanding the complex interplay between trade openness and fundamental economic factors is essential for promoting sustainable economic development. The current study analyses the asymmetric impact of trade openness and exchange rate on South Africa’s economic expansion. The study applied the Nonlinear Autoregressive Distributed Lag (NARDL) and Error Correction Model (ECM) approaches to time-series data covering 1995–2025. The results confirmed the existence of an asymmetric relationship between trade openness, exchange rate, and economic expansion. While economic growth is positively affected by currency appreciation and improvement in trade openness, both currency depreciation and a decline in trade openness negatively influence long-term economic growth. In contrast, the short-run findings reveal that any shocks in the exchange rate (positive or negative) impede economic growth. However, positive changes in trade openness enhance economic growth, even in the short run. Based on these findings, South African policymakers and monetary authorities should ensure the stability of the country’s currency to maintain benefits from trade openness and exchange-rate expansion. Though domestic markets should remain open to global markets, better management of exports/imports is crucial to prevent the country from being an economic dumping site. Full article

This paper examines the impacts of monetary policy announcements on exchange rate volatility of the Mexican peso, a currency that is representative of emerging market currencies, against the US dollar. Narrow windows around policy announcements and high-frequency second-by-second intraday data are used in the analysis. To examine the impact of announcements on exchange rate volatility, we divide the announcement period into a pre-announcement period (five minutes before the announcement), a contemporaneous period (five minutes after the announcement), and a post-announcement period (fifteen minutes after the “contemporaneous period”). While incorporating monetary policy announcements from both the US and Mexico, we find that US monetary policy announcements have greater impacts on the volatility relative to Mexican monetary policy announcements, although both of them lead to significant increases in the volatility around announcements. Furthermore, the increase in volatility resulting from the US announcements lasts for all of the sub-periods, while the Mexican announcements cause an increase in volatility only over the first two periods. In other words, the impact of US monetary policy tends to be more persistent than Mexican monetary policy with respect to peso/dollar volatility. Full article

Fractional differential equations are highly beneficial in economics because they can be used to analyze nonlinear systems with memory effects. This research investigates a group of nonlinear fractional Riccati equations that show up in models of inventory and economic growth. The present work is a combined semi-analytical method for finding deterministic series solutions in the Caputo sense: the Adomian Decomposition–Sumudu Transform Method. Limited studies have examined its usage in memory-affected economic models. This method is effective with nonlinearities due to its ability to operate without the necessity of linearizing or discretizing them. The Mittag-Leffler function is employed to demonstrate that the series converges in a strict manner if it converges in a manner that is both absolute and uniform when the conditions are met. Finally, a Lyapunov stability study is conducted to ensure that the solution can accommodate modifications to the original data. Numerical models with different fractional orders show that the behavior of the system is controlled by the fractional parameter. When the fractional order is small, memory effects are increasing. As the order approaches closer to one, the solutions start to act like classical ones. These results indicate that the current methodology can be used for practical applications such as short-term currency exchange rates and volatility in financial markets. Full article

This paper develops a mathematically rigorous econometric framework for estimating the size and spatial distribution of the informal economy in Europe, contributing to the field of advanced mathematical economics and statistical modelling. A modified Currency Demand Approach (CDA) is formalized within a two-way fixed effects panel model with Driscoll–Kraay standard errors, which accounts for cross-sectional dependence and heteroskedasticity in the presence of spatially correlated residuals. The model links monetary aggregates to fiscal, institutional, and structural determinants, while the conversion of excess cash into informal economy size estimates is performed using the theoretically consistent Ahumada correction, which explicitly incorporates the income elasticity of cash demand into the monetary estimation procedure. National estimates are subsequently disaggregated to NUTS-2 regions through a structural weighting procedure grounded in labour market indicators, human capital measures, and sectoral composition indices. The empirical results reveal substantial cross-country and regional heterogeneity: Northern European countries exhibit comparatively low levels of informal economic activity, while higher shares are concentrated in parts of Southern and Eastern Europe. The proposed mathematical framework advances the literature by offering a replicable, econometrically sound methodology that integrates national monetary models with sub-national structural information, enabling a more granular and statistically grounded quantification of informal economic dynamics across Europe. Full article

Post-disaster reconstruction programmes create an irreversible window for embedding or foreclosing residential energy efficiency at scale. This study examines the structural determinants of per capita residential electricity consumption (K_MES) across all 81 provinces of Türkiye over 2013–2022 using a balanced province-year panel. We develop two complementary panel models, both estimated by two-way fixed effects (province + year) with cluster-robust standard errors, and supported by GLS-AR(1) and random-effects GLS robustness checks. Note that K_MES measures the electricity component of residential energy use only; we, therefore, also estimate the building-stock model with a constructed total-energy dependent variable that combines residential electricity (H_MES) and natural-gas consumption (X_DG) in kWh-equivalent units. Model 1 isolates the macroeconomic transmission channel through which exchange-rate volatility shapes residential electricity demand. Because the USD/TRY rate has no cross-sectional variation, its identifying power in two-way fixed effects comes from its interaction with province-level natural-gas-heating exposure (sh_gas × EV_DA). The interaction is robustly negative across all full-sample specifications (β ≈ −0.022, p < 0.01), indicating that provinces with greater gas-heating penetration are buffered against currency-depreciation pass-through into electricity demand. Provincial GDP carries the dominant direct macro coefficient (β ≈ 0.27–0.29, p < 0.01), establishing income elasticity rather than the exchange rate as the headline aggregate driver. Model 2 decomposes the building stock by structural system, filler material, heating system, and heating fuel. The dominant predictors are the share of electric heating (β ≈ 1.16–1.27, p < 0.01) and the share of AC-only heating (β ≈ −1.0 to −1.13, p < 0.05), with a total-energy specification reaching R² = 0.92. In the comparative subsample of the eleven Kahramanmaraş-affected provinces, masonry construction emerges as the dominant pre-disaster predictor of per capita electricity consumption (β = 14.04, p < 0.05), revealing structurally distinct stock characteristics that pre-date the February 2023 earthquake. Two re-framings are required. First, since the panel covers 2013–2022, the disaster-province estimates capture pre-disaster structural heterogeneity rather than post-disaster market rupture. Second, the macroeconomic mechanism that prior work attributed to the exchange-rate level is more accurately understood as a fuel-mix-mediated exposure channel. The combined evidence implies that mandatory building-code enforcement and natural-gas grid extension are complementary policy levers in the 488,000-unit Turkish Housing Development Administration reconstruction programme: gas grid expansion reduces the macroeconomic vulnerability of residential energy demand, while masonry-replacement construction standards address the largest pre-disaster structural determinant of energy intensity in the affected region. Full article

This study examines the dynamic relationship between exchange rates and stock returns in Taiwan, focusing on asymmetry and time-varying dependence. Using monthly and daily data from 1994 to 2024, we employ ARDL, NARDL, and error correction models (ECM), together with a time-varying copula framework. We contribute to the literature in three ways. First, we provide a unified framework that jointly captures long-run equilibrium, short-run dynamics, and nonlinear dependence. Second, we document robust asymmetric effects, showing that currency depreciation stimulates stock returns, whereas appreciation exerts adverse effects, reflecting Taiwan’s export-oriented economic structure. Third, we show that the dependence between exchange rates and stock returns is time-varying and highly persistent. Overall, the findings highlight the importance of nonlinear and time-varying approaches in understanding exchange rate–stock market interactions and offer important implications for investors and policymakers. Full article

The interaction between crude oil prices and exchange rates is central to understanding global financial stability and macro-economic balances. Contrary to traditional static analyses, the heterogeneous market hypothesis argues that market participants have different time horizons and that multi-scale analysis is necessary to capture dynamic changes in crisis periods. This study examines volatility spillovers between WTI crude oil and the Russian ruble using wavelet coherence, phase difference, and predictive information flow analysis in a time–frequency framework. The analysis separates short-term [2–32 days] transient shocks from long-term [32–256 days] structural changes. Findings show that a negative spillover, initially led by WTI, with evidence of dynamic, frequency-dependent leadership shifts during the 2020 shock, was interpreted as a result of the overnight price gap and a failure of microstructural synchronisation. With the outbreak of the 2022 Russia–Ukraine war, the relationship shifted to a strong, positive, and high-intensity risk transfer, consistent with contagion theory. Crucially, by 2024, a structural decoupling emerged due to geoeconomic fragmentation, signalling that the ruble no longer exhibits traditional petro-currency behaviour. These results offer critical signals for policymakers regarding reserve management and for market participants regarding new liquidity risks. Full article

Using daily data from Investing.com South Africa, this study investigates the forecasting performance of four Rand currency rate markets (EUR/ZAR, CHF/ZAR, BRL/ZAR, and CNY/ZAR) from 13 February 2018 until 24 February 2025. The predictive fitness of three competing models, Support Vector Regression (SVR), Principal Component Regression (PCR), and eXtreme Gradient Boosting (XGBoost), is explored between 80%/20% and 95%/5% training-testing splits. Forecasting accuracy is evaluated based on evaluation errors, i.e., Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The Diebold–Mariano test is employed to check for statistical significance. Empirical results show that the linear SVR model outperforms PCR across all markets, while XGBoost achieves competitive predictive accuracy on average; the trade-offs between SVR and XGBoost are often very small. The data indicate that linear kernel methods provide a robust prediction pipeline, especially when macroeconomic factors (gold, oil, platinum prices, and the USD/ZAR exchange rate) and calendar-based factors are taken into account, and offer a strong framework for predicting daily exchange rate fluctuations. The results of this research provide practitioners (traders, risk managers, and policymakers) with insights into the relative efficiency of the kernel vs. ensemble learning approaches for forecasting the value of emerging-market currencies in the presence of structural volatility. Full article

This study examines the relationship between house prices and the factors driving their growth during the transition from a long-standing currency board regime to Eurozone membership. The main objective is to identify and quantify the key factors explaining the variation in house price growth in Bulgaria under conditions of prolonged currency convergence. The study applies a set of econometric techniques, including stationarity tests (ADF and KPSS), diagnostic checks for normality, serial correlation and heteroscedasticity, and robustness checks. The study is based on 40 quarterly observations covering the period 2015Q4–2025Q3 and 48 selected predictors of the General house price index. The final ARIMAX(0,2,1) model is estimated using second-differenced data. The model includes a first-order moving average component and three exogenous regressors: the owner-occupiers’ housing expenditures, the actual rentals for housing in Bulgaria and the homeowners’ utility expenses. The model explains 87% of the variation in house price acceleration, with a comparatively low mean squared error. The diagnostic analysis confirms model adequacy. The three exogenous regressors are statistically significant at the 1% level with strong and stable effects on house price dynamics. No statistically significant relationship is found for the set of traditional macroeconomic, demographic, financial, and sectoral factors. The results show that during Bulgaria’s transition from a currency board to the Eurozone, the sustained house price growth was driven by country-specific factors. The three statistically significant determinants of the house price acceleration in Bulgaria reflect, respectively, the active investment behaviour of homeowners in improving existing properties, the rational assessment by housing market participants of the balance between mortgage and rental payments, and the burden of utility and maintenance costs borne by owners and tenants, depending on property size and energy efficiency. The first factor is most influential for homeowners, the second for tenants, and the third has a similarly significant impact on both groups. Full article

Foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting and trend analysis, there is still a need for an automated, intelligent model to understand patterns and predict future trends. The creation of such prediction models can provide assistance for investors, financial institutions, and policymakers in governments. To overcome these issues, the proposed study has developed a novel hybrid deep learning model that encompasses a Bidirectional Long Short-Term Memory, an additive attention approach, and a random forest regressor (for long-horizon historical data), attempting to provide a prediction model for the following year’s official exchange rates (LCU per USD). The random forest regressor models the nonlinear interaction of features and assists with generalization, the attention layer focuses on the most influential time steps, and the Bidirectional Long Short-Term Memory (Bi-LSTM) captures all historical data for exchange rate series and temporal dependencies (or dependencies of a sequence of historical data). The use of a time partition (1960–2018 training data + 2019–2023 validation data + 2024 testing data) to train and evaluate the model provides realistic forecasting and prevents temporal leakage. The global panel dataset for more than 250 and 60+ year countries and regions demonstrate that all of the proposed models are better than all classical machine learning models, stand-alone deep learning models, and naive persistence models. The hybrid model shows the most significant prediction error reduction with R² as 0.98, proving long-horizon currency forecasting is extremely robust. Full article

This research examines the clustering structure and volatility spillover among steel-related products in monthly data from July 2004 to September 2025. Using various clustering methods, K-means, hierarchical techniques and market network analysis with correlations, four distinct marketing clusters have been identified: (1) US (United States) steel products, (2) global cyclical raw materials, (3) US iron ore market, and (4) global base metals. The overall volatility spillover index stands at 15.39%, exhibiting significant dynamics that vary over time, driven by major economic events, including the 2008 global financial crisis, the 2015 Chinese currency devaluation, the COVID-19 outbreak, the 2022 Ukrainian conflict, and the 2025 Trump trade tariffs. The primary driver of volatility in global trade is US carbon steel wire prices, while the largest net recipient of volatility shocks is the global copper price. These findings have key implications for understanding the global interconnectedness of steel markets in the current context. Full article