Search Results (209)

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this limitation, the study employs a multivariate Gaussian mixture hidden Markov model, which enables the identification of unobservable states based on daily and intraday return patterns. These patterns include open-to-close, open-to-high, and low-to-open returns. The model is estimated using various specifications, and the best-performing structure is chosen based on the Akaike Information Criterion and the Bayesian Information Criterion. The final model reveals three statistically distinct regimes that correspond to bullish, sideways, and bearish conditions. Statistical tests, particularly the Kruskal–Wallis method, confirm that return distributions, trading volume, and open interest differ significantly across these regimes. Additionally, the analysis incorporates risk measures, including expected shortfall, maximum drawdown, and the coefficient of variation. The results indicate that the bearish regime carries the highest risk, whereas the bullish regime is relatively stable. These findings offer practical insights for regime-aware portfolio management in sectoral equity markets. Full article

Zimbabwe’s economy has experienced extreme exchange rate fluctuations over the past decades, driven by persistent macroeconomic instability and episodes of hyperinflation. The instability in exchange rates can significantly impact trade balances, inflation rates, and overall economic resilience. Understanding the impact of exchange rate volatility (ERV) on international trade is crucial in such a context. This study investigates the impact of exchange rate volatility (ERV) on international trade in Zimbabwe, addressing a literature gap related to its unique economic challenges and hyperinflation. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model on data from 1990 to 2023, the study finds a negative relationship between ERV and international trade. The analysis suggests that inflation reduces imports, but foreign direct investment (FDI) and balance of payments (BOP) increase export uncertainties. This study recommends optimal fiscal and monetary management to mitigate ERV and enhance trade stability, offering insights for policymakers to strengthen Zimbabwe’s trade resilience amid exchange rate fluctuations. Full article

This study investigates the dynamic relationship between environmental tax revenue and economic development in the European Union from 2013 to 2022. The findings reveal that these taxes significantly contribute to economic development in the long run, although short-run effects vary by tax type and country. The PMG model results indicate that energy tax revenues increase GDP per capita by 0.038, transport tax revenues by 0.041, and resource tax revenues by 0.018, all of which are statistically significant. Pollution tax revenues have an effect of 0.002 in the long run but are not statistically significant. In the short run, none of the tax variables show significant effects, although pollution tax revenues have a transitional impact of 0.196. The error correction term of −1.321 confirms a strong long-run adjustment, reinforcing the gradual economic benefits of environmental taxation. The results underscore the importance of resource and pollution taxes, which exhibit robust positive impacts, particularly in resource-rich and pollution-intensive economies. Energy and transport taxes also influence economic performance; however, their effectiveness depends on the structural and sectoral differences among countries. This study provides valuable insights for policymakers by highlighting the necessity of designing tailored environmental taxation policies that align with national conditions and long-term sustainability goals. Additionally, this study adopts a systems thinking perspective to capture the interconnectedness between environmental fiscal instruments and macroeconomic sustainability, offering a holistic interpretation of policy impacts. Full article

Navigating the complexity of petroleum futures markets—marked by extreme volatility, geopolitical uncertainty, and macroeconomic shocks—demands adaptive and risk-sensitive strategies. This paper explores an Adaptive Risk-sensitive Transformer-based Deep Reinforcement Learning (ART-DRL) framework to improve portfolio optimization in commodity futures trading. While deep reinforcement learning (DRL) has been applied in equities and forex, its use in commodities remains underexplored. We evaluate DRL models, including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Deep Deterministic Policy Gradient (DDPG), integrating dynamic reward functions and asset-specific optimization. Empirical results show improvements in risk-adjusted performance, with an annualized return of 1.353, a Sharpe Ratio of 4.340, and a Sortino Ratio of 57.766. Although the return is below DQN (1.476), the proposed model achieves better stability and risk control. Notably, the models demonstrate resilience by learning from historical periods of extreme volatility, including the COVID-19 pandemic (2020–2021) and geopolitical shocks such as the Russia–Ukraine conflict (2022), despite testing commencing in January 2023. This research offers a practical, data-driven framework for risk-sensitive decision-making in commodities, showing how machine learning can support portfolio management under volatile market conditions. Full article

This study explores the historical evolution and short-term predictive modeling of the U.S. 10-year Treasury bond yield, a critical indicator in global financial markets. Recognizing its sensitivity to macroeconomic conditions, the research integrates economic variables, including the federal funds rate, core Consumer Price Index (CPI), real Gross Domestic Product (GDP) growth rate, and the U.S. federal debt growth rate, to assess their influence on yield movements. Four forecasting models are employed for comparative analysis: linear regression (LR), decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural networks. Using historical data from the Federal Reserve Economic Data (FRED), this study finds that the RF model offers the most accurate short-term predictions, achieving the lowest mean squared error (MSE) and mean absolute error (MAE), with an R² value of 0.5760. The results highlight the superiority of ensemble-based nonlinear models in capturing complex interactions between economic indicators and yield dynamics. This research not only provides empirical support for using machine learning in economic forecasting but also offers practical implications for bond traders, system developers, and financial institutions aiming to enhance predictive accuracy and risk management. Full article

This study examines how national public funding and macroeconomic conditions affect higher education performance, measured by graduation rates. A panel dataset covering 2013–2022 and ten Lithuanian public universities integrates economic, financial, and institutional variables. Lithuania applies a mixed higher education funding model that combines institutional support with elements of student-based financing, where part of the public resources follow individual enrollment patterns. Both immediate and lagged effects are analyzed using multiple regression models with time-lag factors. A review of academic literature indicates that increased funding does not necessarily lead to better outcomes; instead, the strategic allocation of resources to priority areas is particularly important. The results confirm that macroeconomic factors are statistically significant and that overall public funding does not have a positive impact unless it is allocated efficiently. On the contrary, funding directed toward research and infrastructure consistently shows a positive effect. These findings underscore the importance of evaluating the effectiveness of education policy through lagged impact analysis. Full article

This study explores the drivers and evolution of the shadow economy in Bosnia and Herzegovina—a transitional, post-conflict country facing persistent institutional fragility. Using the Multiple Indicators and Multiple Causes (MIMIC) model, an extension of Structural Equation Modeling, the paper estimates the size and dynamics of the shadow economy from 1996 to 2022. The model integrates macroeconomic indicators (employment rate, GDP per capita, tax revenues) and institutional variables (rule of law, control of corruption), with data primarily sourced from the World Bank. The results show that institutional quality, tax burden, and labor market conditions are significant determinants of the informal sector. The model demonstrates strong statistical validity (CFI = 0.986, RMSEA = 0.05), supported by robustness checks including unit root tests, structural break analysis, and the exclusion of controversial benchmarking methods. The shadow economy responds markedly to major shocks such as the 2008 global financial crisis and the 2014 floods. Findings provide valuable policy insights: strengthening institutions, simplifying tax systems, and encouraging formal labor market participation can significantly reduce informality. The study supports evidence-based reforms to enhance transparency, resilience, and sustainable development in Bosnia and Herzegovina. Full article

In recent years, corporate innovation has faced growing pressures from macroeconomic fluctuations and intensifying industry competition, making the maintenance of uninterrupted innovation increasingly crucial. This study selected Chinese listed firms from 2015 to 2022 as samples and adopted a panel fixed-effect model to examine the impact of corporate ESG performance on sustained innovation, with particular attention to external environmental pressures, including macroeconomic uncertainty, industry competition, and market attention. The results demonstrate that corporate ESG performance significantly promotes corporate sustained innovation. Mechanism analyses indicate that from the dual perspectives of resource effects and governance effects, ESG performance primarily enhances sustained innovation by increasing investment in R&D funding and personnel, as well as avoiding managerial myopia. Specifically, macroeconomic uncertainty dampens the positive effect of ESG performance, whereas, under industry competitive and market scrutiny pressures, the beneficial impact of ESG performance on sustained innovation becomes more evident. The research findings expand the internal drivers for sustained innovation, enrich the study of economic consequences of ESG performance, and clarify the differentiated moderating effects of various external pressures under VUCA scenarios. By integrating internal drivers and external complex environments, the paper offers practical insights for firms to leverage ESG practices for innovation resilience and long-term growth, particularly under dynamic market conditions. Full article

Identifying and forecasting macro-financial conditions is critical to stabilizing the economy. This study aims to develop a novel methodology for constructing China’s Financial Conditions Index, utilizing monthly data from six major Chinese financial markets (comprising 33 key financial indicators) along with 25 external macroeconomic variables from both China and the United States, spanning January 2002 to June 2024. Although the traditional TVP-FAVAR model can capture the linear relationship in the financial market, it cannot adequately characterize the nonlinear or asymmetric nature of the macro-financial conditions exhibited when major risk events occur at home and abroad. In this paper, we propose an innovative kernel factor-augmented time-varying parameter vector autoregressive model (TVP-KFAVAR), which can better capture the nonlinear nature of the macro-financial situation. It is shown that the TVP-KFAVAR model successfully reflects the impact of major domestic and international risk events on China’s Financial Conditions Index. Meanwhile, the ARIMA model and five machine learning techniques (GRU, LSTM, BiLSTM, TCN and Transformer) are used in this study to predict the Macro-Financial Conditions Index, and it is found that the vast majority of the machine learning techniques outperform the traditional time-series models in terms of forecasting performance. TCN has the outstanding prediction performance under different input configurations. This study can provide policymakers with a powerful tool for macro-financial regulation and risk early warning, and help improve macro-financial management in emerging markets. Full article

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

Accurate gold price forecasting is essential for informed financial decision-making, as gold is sensitive to economic, political, and social factors. This study presents a hybrid framework for multivariate gold price prediction that integrates classical econometric modelling, traditional machine learning, modern deep learning methods, and their combinations. The framework incorporates financial, macroeconomic, and sentiment indicators, allowing it to capture complex temporal patterns and cross-variable relationships over time. Empirical validation on an eleven-year dataset (2014–2024) demonstrates the framework effectiveness across diverse market conditions. Results show that advanced supervised techniques outperform traditional econometric models under dynamic market environment. Key advantages of the framework include its ability to handle multiple data types, apply a structured variable selection process, employ diverse model families, and support model hybridisation and meta-modelling, providing practical guidance for investors, institutions, and policymakers. Full article

This paper proposes a novel control function approach to identify and estimate linear models with endogenous variables in the absence of valid instrumental variables. The identification strategy exploits time-varying volatility to address the multicollinearity problem that arises in conventional control function methods when instruments are weak. We establish the identification conditions and show that the proposed method is $\sqrt{T}$-consistent and asymptotically normal. We apply the proposed approach to estimate the elasticity of intertemporal substitution, a key parameter in macroeconomics. Using quarterly data on aggregate stock returns across eleven countries, we find that the data exhibit substantial time variation in volatility, supporting the identifying assumptions. The proposed method yields confidence intervals that are broadly consistent with the general findings in the literature and are substantially narrower than those obtained using weak-instrument robust methods. Full article

Incorporating economic policy uncertainty into the Melitz and Ottaviano theoretical model, this study systematically examines the impact of economic policy uncertainty on firm markups, contributing to our understanding of how macroeconomic conditions affect business sustainability. The results reveal a significant negative relationship between economic policy uncertainty and firm markups, with particularly adverse effects observed in labor-intensive industries, smaller firms, and export-driven companies. As investment irreversibility increases, so does the detrimental impact of economic policy uncertainty on business markups. Importantly, it is discovered that innovation efforts can mitigate these negative effects, promoting sustainable business practices under high policy uncertainty. This research extends the mechanism through which EPU affects markups and highlights the critical roles of investment irreversibility and innovation behavior as moderators. By exploring these dynamics, our findings contribute to the broader discourse on sustainability by identifying strategies for enhancing corporate resilience and competitiveness amidst economic uncertainties. Full article

Soybean is a crucial economic crop and it is one of the most marketized and internationalized bulk agricultural products in China. As fluctuations in soybean prices directly impact national food security and agrarian stability, it is essential to predict this price accurately. Soybean price is influenced by multiple factors, such as macroeconomic data (typically low-frequency, measured quarterly or monthly), weather conditions, and investor sentiment data (high-frequency, for example, daily). In order to incorporate mixed-frequency data into a forecasting model, the Mixed Data Sampling (MIDAS) model was employed. Given the complexity and nonlinearity of soybean price fluctuations, machine learning techniques were adopted. Therefore, a MIDAS-SVR model (combining the MIDAS model and support vector regression) is proposed in this paper, which can capture the nonlinear and non-stationary patterns of soybean prices. Data on the soybean price in China (January 2012–January 2024) were analyzed and the mean absolute percentage error (MAPE) of the MIDAS-SVR model was 1.71%, which demonstrates that the MIDAS-SVR model proposed in this paper is effective. However, this study is limited to a single time series, and further validation across diverse datasets is needed to confirm generalizability. Full article

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to further research in learning-based methods for volatility forecasting. Full article