This paper investigates the impact of uncertainty on investor overconfidence in the Bitcoin market. While prior studies mainly focus on returns and volatility, limited attention has been paid to behavioral responses. Using a nonlinear autoregressive distributed lag (NARDL) model and monthly data from June 2011 to August 2022, we examine the asymmetric effects of major U.S. uncertainty indices (EPU, GPR, CPU, TEU and EURQ). The results reveal significant asymmetries. In the short run, increases in EPU and GPR reduce investor overconfidence, while decreases have the opposite effect. TEU and EURQ negatively affect investor confidence in both the short and long run. These findings highlight the key role of information-based uncertainty in shaping investor behavior and contribute to the behavioral finance literature by providing new evidence from cryptocurrency markets.
This study aims to investigate the nonlinear impact of environmental, social and governance uncertainty (ESGU) on the performance of traditional green finance instruments as well as their Islamic equivalents. Unlike prior work that examines the mean-based regressions between ESG performance and financial performance, to the best of our knowledge, this study is the first to (i) empirically measure the impact of ESGU on conventional green finance and Islamic green finance simultaneously, (ii) apply the machine-learning based QQKRLS approach to reveal the heterogeneous and regime-dependent effects across the distribution of ESGU and returns and (iii) compare the response of Shariah-compliant versus conventional green instruments to ESG-related uncertainty. The dataset covers the period between February 2016 and January 2026. The results shed light on the high degree of heterogeneity in the impact of ESGU on different distributions of returns: the lower the level of ESGU, the stronger the performance of standard green bonds, but at higher quantiles, there are positive and statistically significant associations between ESGU and Islamic green finance assets (particularly, the high-grade Sukuk). This indicates that, during escalated uncertainty about an asset, in the form of ESG-related uncertainty, Islamic sustainable securities might be more resilient, because they follow transparency and are involved in sustainability-related investments. Our findings are directly relevant to financial regulators and investors, as low-to-moderate ESGU is associated with stronger performance of green bonds, while higher ESGU is associated with weaker performance; hence, regulators should design and implement policies that reduce the variance of ESG information quality through standardized ESG disclosure templates for bond issuers to reduce cross-issuer comparability uncertainty. Moreover, Shariah supervisory boards can play a complementary role by enforcing transparency regarding the asset-backing and risk-sharing structures of Sukuk issuances, thereby converting regulatory ESG uncertainty into a differentiating competitive advantage rather than an additional source of market risk.
The influence of macroeconomic indicators on employment opportunities (EO) in the host country remains a topic of debate. This study examines the lead-lag relationship between EO and macroeconomic variables (foreign direct investment (FDI), economic growth (GDP), industrialization (IND), gross capital formation (GCF), gross domestic income (GDI) and population growth (PG)) in Vietnam from 1996 to 2023 using a time-frequency approach. To achieve this, we employ several wavelet techniques, including the wavelet power spectrum, cross-wavelet transform, wavelet coherence and wavelet-based Granger causality. Our analysis identifies contagion and correlation within the frequency domain, revealing that higher time scales are associated with contagion, while lower time scales correspond to interrelations. The findings provide evidence of positive relationships between FDI, GDI, GDP, PG, GCF and EO in the medium and long run, whereas industrialization negatively impacts employment in Vietnam. Additionally, wavelet-based Granger causality analysis suggests bidirectional causality between macroeconomic variables and EO across various time and frequency bands. These findings have significant implications and offer practical recommendations for economic and labor market development.
This study investigates how climate policy uncertainty (CPU) shapes corporate greenwashing behavior in the United States, focusing on selective disclosure and selective investment greenwashing. Motivated by the limited understanding of how climate policy uncertainty affects both disclosure- and investment-based greenwashing, particularly in the U.S. capital market, this study addresses a critical gap in the literature. Using panel data for publicly listed U.S. firms from April 2005 to November 2024, this study develops a dynamic rolling correlation metric to detect inconsistencies between firms’ environmental investment claims and their realized environmental performance. The empirical results reveal that higher CPU levels significantly increase both selective disclosure and selective investment greenwashing, suggesting that firms respond to policy ambiguity by emphasizing symbolic rather than substantive environmental actions. These findings imply that policy instability weakens external monitoring mechanisms and encourages firms to prioritize reputation signaling over genuine environmental performance. Mechanism analyses indicate that unpredictable regulatory tightening and declining participation by environmentally oriented institutional investors intensify greenwashing incentives. Furthermore, transparency and CSR orientation moderate these effects, with highly transparent and CSR-committed firms exhibiting weaker greenwashing responses to policy uncertainty. This study contributes to the literature by introducing a novel dynamic correlation-based measure of selective investment greenwashing and providing one of the first U.S.-focused empirical analyses jointly examining both dimensions of greenwashing. The study also offers practical value: it helps regulators design more stable and comparable disclosure rules, helps investors and analysts detect symbolic sustainability claims, and provides researchers with a replicable measure of investment-based greenwashing. These findings underscore the need for more stable climate policies, enhanced disclosure standards, and stronger monitoring mechanisms to reduce symbolic sustainability practices in the U.S. capital markets.
This study develops a two-stage behavioral model for real-time option pricing in markets characterized by heterogeneous agents. The framework integrates traders' subjective beliefs about terminal asset prices with their individual execution propensity parameters, which govern the probability of accepting a counterparty's offer. In the first stage, algebraic conditions establish a feasible transaction-price domain based on each participant's beliefs and transaction costs. In the second, a nonlinear optimization process determines reduced price bounds that capture strategic behavior and heterogeneity in execution propensity, thereby generating bid-ask spreads and waiting intervals endogenously. Using S&P 500 Call-option data from 2020 (242,697 contracts), the model is benchmarked against Yoshida's fuzzy-logic (F-L), Black-Scholes and Heston stochastic-volatility frameworks, achieving materially lower pricing errors across all moneyness levels and maturities. Quantitatively, the model reduces average pricing errors by more than 30% relative to benchmark frameworks. The findings demonstrate that behavioral heterogeneity and execution propensity jointly explain option-price deviations and the microstructure of real-time markets. This study contributes to the behavioral-finance literature by integrating real-time microstructure dynamics with heterogeneous beliefs, offering a tractable and empirically validated pricing framework.
Green finance has emerged as a strategic instrument for promoting sustainable development (SD), particularly in emerging and resource-dependent economies that are facing rising environmental pressures and carbon emissions. However, existing studies often provide fragmented evidence, rely on single-country analyses or use limited proxies for green finance, leaving important gaps in understanding its comprehensive role across diverse contexts. In this context, this study investigates whether green finance contributes to SD in the BRICS countries and Saudi Arabia over the period 2001-2024. Using a balanced annual panel dataset, the analysis employs fixed-effects estimation as the baseline approach and System Generalized Method of Moments as a robustness check to address heterogeneity, dynamic persistence and endogeneity. Green finance is measured through three dimensions - green credit, green securities and green investment - while carbon emissions, foreign direct investment (FDI), economic growth, trade openness (TO) and natural resource rent are included as control variables. The results reveal that all dimensions of green finance have a positive and statistically significant impact on SD, whereas carbon emissions have a negative effect. FDI contributes positively, whereas economic growth and TO have generally supportive effects. This study provides one of the first integrated and up-to-date comparative analyses of the BRICS countries alongside Saudi Arabia using a multidimensional green finance framework and recent data up to 2024, offering a more comprehensive perspective than prior studies. These findings have clear implications for strengthening green financial systems and advancing SD strategies.
The efficacy of debt mutual funds in the Indian investment market is critical. Therefore, their technical efficiency must be evaluated. Data envelopment analysis (DEA) was used to analyze the efficacy of long and short categories of debt funds offered to Indian investors and provide suggestions to regulators, practitioners, and policymakers. We use secondary data on 34 large debt mutual funds from the financial reports of the Association of Mutual Funds in India (AMFI) from 2019 to 2023. According to our analysis, Indian debt funds experienced an average efficacy of 95.88% with a variation of 2.66% in their efficiency scores during the study period. We performed a robustness check by dividing the sample into short- and long-term debt mutual funds to investigate the stability of our DEA-based efficiency estimates. The Mann-Whitney test findings show a statistically significant difference in efficiency between the two groups. Long-term funds experienced 94.52% average efficiency with a variation of 5.29%, whereas short-term funds experienced an average efficiency of 97.59%, with a variation of 1.59%. Our results show that 50% of debt funds are maximally efficient in 2021, and the minimum of 26.47% is efficient in 2023. With a variation of 1.36%, the average efficiency increased to 99.24% by 2021, becoming the highest. The minimum efficiency of debt funds was 92.46% in 2020, with an 8.64% variation. This study also proves that the risk of investment influences the performance of the fund more significantly than the associated expenses. By emphasizing risk-sensitive performance, this study provides actionable insights for enhancing the operational efficiency of debt mutual funds and contributes to the limited literature on the efficiency evaluation of fixed-income funds using a non-parametric framework.
Over the last decade, global food price volatility has become a significant threat to food security. Many market participants and policymakers have long suspected a link between the unprecedented price rise and institutional investment in commodity futures markets; however, there has been considerable debate. Existing literature has largely focused on price level and volatility, with limited attention to how financialization affects the price discovery function of futures markets. This study examines whether and how index investment and speculative sentiment distort the price discovery efficiency of the futures market for indexed and non-indexed agricultural commodity futures in the USA. The empirical results of the panel quantile regression analysis indicate that both index investments and speculative sentiment significantly reduce the informational leadership of futures markets, particularly in higher quantiles, suggesting a shift from fundamentals-driven to sentiment-driven price formation. These findings imply that financialization affects not only prices but the quality of information transmission, with consequences for hedging, production decisions and policy design. This study contributes to the literature by examining financialization through a market-microstructure lens using Information Leadership Share and by introducing trader position-based sentiment into price discovery analysis.
This paper examines the temporal and frequency connectedness among macroeconomic variables, commodities and investor sentiment in G7 countries over the period from January 2014 to July 2025, with particular attention to the 2022 Russia-Ukraine war. Using Diebold and Yilmaz (2012) for time-domain analysis and Barun & iacute;k and K & rcaron;ehl & iacute;k (2018) for frequency-domain decomposition, we investigate the transmission of shocks over short- and long-term horizons. The results reveal pronounced heterogeneity: financial markets serve as short-term net transmitters, whereas commodities such as oil, gas and wheat propagate persistent long-term shocks. Monetary indicators typically act as net receivers, while the Google Sentiment Index reflects immediate shifts in investor sentiment. These findings underscore the asymmetric and horizon-dependent nature of contagion, highlighting the importance of horizon-specific investment strategies and policy measures to mitigate systemic risk.
Digital investment brokers have become popular as more and more consumers around the world assume personal responsibility for their finances. In order to identify the factors that cause this adoption, a research model was constructed based on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology. The model was piloted on a first set of 279 Brazilians, of which 126 were digital investment brokers adopters. The study establishes that perceived usefulness, data visualization and subject norm have a positive influence on the behavioral intention to use digital investment brokers. Another mediating variable through which perceived ease of use affects adoption intention is data visualization. The study suggests the need to undertake the research in other countries and to incorporate other variables in the research model, since there is limited research on individual investor behavior in adopting digital investment brokers.
The Cauchy distribution is widely used for modeling heavy-tailed and extreme-value data, but its lack of flexibility limits its effectiveness in many applied contexts. In this paper, we study the transmuted Cauchy (TC) distribution, obtained by applying the quadratic rank transmutation map to the Cauchy model. We derive several key mathematical properties of the TC distribution, including its tail behavior, characteristic function, quantile function, mean deviation, truncated distribution and mode. Multiple classical estimation methods maximum likelihood, maximum product spacing, least squares, Cram & eacute;r-von Mises, Anderson-Darling and right-tail Anderson-Darling are compared along with a Bayesian estimation framework. A simulation study evaluates estimator performance, and two empirical applications demonstrate the usefulness of the TC distribution for modeling financial and material strength datasets. The originality of this work lies in (i) deriving several new properties of the TC distribution not previously available in the literature, (ii) presenting a comprehensive comparison of six classical estimation methods along with a full Bayesian framework, (iii) establishing a new autoregressive minification process with TC marginals and (iv) demonstrating the strong empirical performance of the TC model relative to the Cauchy and skew-Cauchy alternatives.
This paper presents a stochastic modelling framework for climate-sensitive and policy-relevant variables crucial to Australia's clean energy transition. Traditional Ornstein-Uhlenbeck (OU) processes capture mean-reversion but cannot represent the state-dependent volatility and extreme-event behavior observed in temperature anomalies, carbon prices and energy market indices. To address these limitations, we introduce constant, linear and quadratic diffusion specifications and apply Lamperti transformations to obtain unit-diffusion processes that preserve mean-reversion while enabling stable simulation and efficient estimation. The framework offers a unified and extensible approach for modelling variables across different volatility regimes. Illustrative applications show that the transformed drift terms reproduce nonlinearities and tail-risk features, allowing the model to capture stable climate processes, moderately volatile carbon markets and highly volatile energy indices within a coherent structure. This provides a flexible and numerically robust foundation for analyzing climate-finance interactions under uncertainty. By enabling transparent scenario generation and improved representation of volatility amplification under shocks, the model supports evidence-based planning relevant to Australia's national priorities, including emissions reduction, energy security and climate risk assessment. The framework directly aligns with SDG7 (Affordable and Clean Energy), SDG9 (Industry, Innovation and Infrastructure) and SDG13 (Climate Action). Future work includes multivariate extensions, regime-switching dynamics, and data-driven calibration to capture complex environmental-financial interactions.
This study evaluates the efficiency of London and New York, the two predominant global financial hubs, in the context of the United Kingdom's withdrawal from the European Union (Brexit). By jointly combining DEA meta-frontier analysis with a Difference-in-Differences (DiD) framework, the efficiency levels of 70 financial firms over the period 2007-2019 are estimated, and the impact of Brexit on banking efficiency is assessed. The findings show that London-based banks initially outperform their New York counterparts in average meta-frontier efficiency; however, a process of convergence emerges, with New York reaching London's efficiency levels by 2018. The DiD estimates indicate that Brexit played a decisive role in this shift, highlighting how political and regulatory disruptions shape efficiency dynamics in global finance.
Market models which re ect stylised properties of the interest rate term structure are widely used for modelling and pricing interest rate derivatives. We consider a market model involving the short rate and a diversified global stock index. We illustrate the stylised properties of the interest rate term structure implied by a system of stochastic differential equations specifying the short rate and the discounted stock index under the benchmark approach. Comparison with empirical evidence demonstrates the explanatory power of a discounted stock index modelled by a squared Bessel process.
Growing financialization, technological advancement and the dynamic nature of financial instruments have not only assisted in generating risk diversification opportunities but have also made financial markets susceptible to economic shocks and crises. In this context, the present study attempts to explore the dynamic risk transmission and interconnectedness of G7 stock indices and major currency pairs (JPY/USD and CHF/USD) from 2020 to 2024. The quantile and time-frequency analysis indicate notable global financial linkages, with the SP500 acting as a dominant net transmitter of risk, particularly influencing North American and European markets. Key European indices such as DAX 40 and CAC 40 also demonstrate substantial regional risk spillovers, while the Nikkei and major exchange rates primarily act as net receivers, absorbing shocks during periods of market stress. The study highlights the changing landscape of market connectedness, with peaks observed during the COVID-19 pandemic and a gradual return to stability thereafter. These findings provide useful insights into the dynamics of global risk transmission, highlighting the necessity for robust risk management strategies aimed at mitigating market volatility.
We employ linear and nonlinear ARDL models using monthly data from January 1985 to April 2025 to examine whether domestic and foreign economic policy uncertainty (EPU) exert asymmetric effects on exchange rates. The analysis focuses on five major economies relative to the United States: Australia, Canada, Japan, the United Kingdom, and the Euro Area. The baseline linear ARDL model, excluding EPU variables, fails to establish a stable long-run relationship between exchange rates and economic fundamentals. In contrast, an extended linear ARDL model with domestic and foreign EPU identifies stable long-run relationships across all countries. The nonlinear ARDL (NARDL) model confirms long-run relationships and reveals significant short- and long-run asymmetries in EPU shock effects. Our findings demonstrate the importance of including domestic and foreign EPUs in exchange rate models, and the benefits of using nonlinear models to capture asymmetries between exchange rates and economic fundamentals. The Global Financial Crisis (GFC) reinforced the long-term relevance of EPU in exchange rate determination, while the COVID-19 pandemic introduced heightened short-run volatility and modest long-run structural adjustments, particularly in countries more vulnerable to external shocks.
In this study, Gaussian Process regression (GPR) is implemented as a method to denoise observed financial data and to make predictions at densely interpolated time points. The methodology is applied to various empirical financial datasets. A Gaussian kernel with data-dependent initialization is implemented to derive predictive means and confidence intervals. With this procedure, synthetic data points are created from the denoised dataset for better prediction accuracy. The drawdown of the denoised dataset is calculated using the predicted mean derived by GPR, and the model for predictions is trained with those values. Finally, various machine/deep learning-based approaches are implemented to show that the data densification with GPR improves the detection of upcoming significant fluctuation of a given financial dataset. It is shown that with shorter time intervals of the data, the improvements are more significant.
This research examines potential asymmetric interactions between interest rate changes and sector returns in the US, focusing on some of the major economic and geopolitical events over recent years, such as the COVID-19 pandemic and the Russia-Ukraine conflict. These events have triggered political and economic responses that have deeply affected financial markets. Using the Nonlinear Autoregressive Distributed Lag (NARDL) methodology, the study focuses on 11 sector indices of the S&P 500. It also decomposes nominal interest rates into their real interest rate and inflation expectation components. The analysis covers the period from April 7, 2019 to April 28, 2024, dividing the sample into two distinct sub-periods characterized by declining and rising interest rates, respectively, for robustness purposes. The results reveal several key findings. First, the sensitivity of sector returns is sector-, model- and period-dependent. Second, disaggregating nominal interest rates into inflation expectations and real interest rates significantly improves the analysis of US stock market dynamics at the sector level. Inflation expectations are found to be positively correlated with sector returns, whereas real interest rates tend to have a negative effect. Third, short-term asymmetries in shifts in interest rates and inflation expectations have a significant impact on the US stock market. Finally, the explanatory power of the model is significantly stronger in the first sub-period, which is characterized by declining and low interest rates and the economic impact of the COVID-19 crisis. These results have important implications for portfolio managers and other financial market participants.
While many studies report correlations between a stationary time series Yt and a non-stationary time series Xt, it is still an open problem whether traditional correlation tests are appropriate for assessing the significance of such relationships. To address this gap, we first hypothesize that applying standard regression-based correlation tests in this context may yield spurious or non-informative results. Furthermore, we conjecture that standard correlation statistics are not suitable for evaluating such relationships, and the appropriate test statistic differs from that used for stationary or jointly random series.We first validate our conjectures through simulation studies. In our experiments, Yt follows a stationary AR(1) process with parameter phi, while Xt follows a random-walk model. The empirical rejection rate exceeds the nominal 5% level, increasing from 7.86% when phi=0.1 to around 62.3% when phi=0.9. Our findings support our claims about the spurious nature of the correlation and the inadequacy of standard tests in this setting.Thereafter, we develop the estimation and testing theory for the correlation between a stationary Yt and a non-stationary Xt. We have proved that the standard correlation statistic cannot be used in this setting and that the resulting test statistic differs from the one used to test the correlation between two random series Yt and Xt, concluding that the traditional correlation test cannot be used to test for the correlation between a stationary time series Yt and a non-stationary time series Xt.
This paper develops a comprehensive methodology for optimal portfolio selection in semi-martingale and non-stationary financial markets. Employing rigorous statistical tests, we first establish that real market data exhibit both non-stationary behavior and long-memory properties, challenging the validity of traditional stationary models. Motivated by these empirical findings, we propose an innovative asset price model based on sub-mixed fractional Brownian motion (smfBm). The core theoretical contribution of this work is the derivation of a novel, closed-form analytical solution for geometric Asian option pricing within this smfBm framework. This formula is a significant extension of existing results, as it explicitly incorporates the non-stationary, time-dependent volatility structure of the smfBm for H is an element of(3/4,1), providing an exact and arbitrage-free valuation tool not previously available in the literature. Our primary practical contribution lies in the novel integration of these smfBm-priced options into a Cardinality-Constrained Mean-Variance (CCMV) portfolio model. We demonstrate that this hybrid strategy uniquely mitigates risk and enhances returns, with numerical results showing that a concentrated 5-asset portfolio can achieve a 37.52% return. Thus, this work makes distinct contributions through its original pricing formula and its pioneering application to constructing efficient, non-stationary portfolios.
