Search Results (247)

This paper investigates whether prediction market settlements create incentives for temporary price pressure in Bitcoin spot markets. Using high-frequency data from February 2025 to January 2026 and actual contract-level data from Polymarket and Kalshi to identify economically relevant contract strikes, we document basis divergence between settlement oracle exchanges (Coinbase) and non-constituent exchanges (Binance) during expiry windows. Employing a difference-in-differences framework with month fixed effects, we find that a one standard deviation increase in strike proximity is associated with a 6.7 basis point constituent exchange price deviation during settlement windows. The estimate is precise under the baseline minute-level HAC specification, while exact paired-month permutation inference based on 12 settlement events yields $p=0.0256$; equal-weight event aggregation produces a larger negative estimate, indicating event heterogeneity. Monthly directional patterns are suggestive, though stricter event-level and above-versus-below-strike tests provide mixed evidence on directional asymmetry. Taken together, these findings provide reduced-form evidence consistent with settlement-related incentives and may raise broader settlement-design considerations for decentralized financial systems. However, the analysis does not directly observe trader intent or the underlying mechanism. Full article

Serbia’s nominal exchange rate has remained near 117 RSD/EUR since 2018, while domestic inflation has persistently exceeded the euro-area rate, producing real appreciation and raising concerns about agricultural competitiveness. This paper assesses dinar misalignment over 2010–2024 using three complementary but non-equivalent benchmarks: purchasing power parity (PPP), the behavioral equilibrium exchange rate (BEER, estimated through Johansen cointegration), and the fundamental equilibrium exchange rate (FEER). The PPP and FEER benchmarks indicate dinar overvaluation of approximately −23% and −13% by end-2024, whereas the fundamentals-based BEER places the dinar close to equilibrium over 2016–2024. These results are interpreted as layered diagnostics of price competitiveness, fundamentals consistency, and external sustainability, rather than as a single jointly identified equilibrium range. Sectoral simulations show strong asymmetry: depreciation raises IRR in export-oriented fruit/IQF production from 3% to 12%, while reducing livestock-fattening IRR from 9% to 6% through higher imported feed costs. Monte Carlo checks confirm these qualitative rankings across plausible pass-through and cost-share ranges. Maintaining the nominal corridor also carries a quasi-fiscal burden: NBS official interest expenses linked to sterilization amount to RSD 111.4 billion over 2021–2024, or 0.07–0.57% of GDP annually. The findings suggest that exchange-rate policy has simultaneous competitiveness, sectoral-distributional and quasi-fiscal dimensions, requiring gradual, transparent and exposure-differentiated policy responses. Full article

Exchange-listed medical data products create information and pricing asymmetry because sellers, buyers, and governance reviewers do not observe the same product boundaries, scenario permissions, processing depth, or compliance costs. This study proposes SM-DPF, an auditable pricing reference framework that makes these asymmetric conditions explicit through a reproducible three-layer chain: a public investment anchor, a locked structural scoring model, and a proposed future market learning governance interface that is not implemented or evaluated in the present study. Using 23 de-identified transaction-descriptive records from a single exchange context across insurance claims, model pretraining, and pharmaceutical R&D, the pricing reference outputs y_0,i show in-sample diagnostic consistency with de-identified transaction prices y_i, with an overall mean absolute percentage error of 4.82% and median absolute percentage error of 4.55%. The same 23 records informed the initial scenario-response calibration and the subsequent diagnostics; the reported errors and correlations are, therefore, in-sample diagnostics only. SM-DPF is a governance-oriented reference tool for transparent listing decisions and audit replay with no transaction price prediction claim. Full article

We study an agent who tracks several independent, unobserved, slowly drifting states and is paid by how well a chosen action matches each state but who can process only a bounded amount of information per period. The payoff environment is deliberately symmetric—quadratic matching losses, Gaussian drift, Gaussian observation noise—and the agent’s objective contains no asymmetry: we treat both the risk-neutral (linear) objective and the long-run log-growth (Kelly) objective. Within this symmetric environment, we show that the value of attentionis sharply asymmetric in the sign of the agent’s surprise. Because the matching payoff is maximized when action equals state, a surprisingly low payoff is strong evidence of a state mismatch that is worth correcting, whereas a surprisingly high payoff is evidence either of noise or of a match already achieved—in both cases carrying little decision-relevant information. We prove (Theorem 1) that the posterior expected mismatch, and hence the value of information, is strictly decreasing in the realized payoff, negligible for good surprises and rising steeply for bad ones, with a correspondingly asymmetric slope. We then show that an information-constrained agent optimally adopts a threshold attention policy (Theorem 2), which, under one explicit and standard bridge—that valuation inherits attention weight, as in salience and rational-inattention theories of choice—projects onto a reference-dependent value function with a kink at the expected payoff and a loss-side slope strictly steeper than its gain-side slope (Corollary 1): precisely the signature of loss aversion. The mechanism supplies the structure of loss aversion—its sign, its reference point, and how it varies with the environment—while its magnitude is one calibrated parameter that places the implied coefficient in the empirical range. Risk aversion follows as a corollary (Theorem 3): the kink induces first-order risk aversion over small symmetric gambles, inverting the usual hierarchy in which (second-order) risk aversion is primitive, and loss aversion is an add-on. The mechanism is immune to the Rabin calibration critique. Simulations benchmark the myopic policy against the computed optimum, map the mechanism’s robustness across noise tails, and locate the implied coefficient; we close with extensions to endogenous gain-seeking in convex (“gold-rush”) environments, population heterogeneity through learned priors, and a reading of hedonic affect as the Lagrange multiplier that prices a scarce attentional resource. Full article

Reducing carbon emissions from highway construction is vital for mitigating climate change. Based on an expressway project in central China, this study conducted a fine-grained CO₂ emission accounting and analysis. The findings reveal that bridges and culverts rank first in emissions among all subprojects, emerging as a “carbon hotspot” with an exceptionally high emission intensity, followed by subgrade and pavement. Additionally, traffic safety facilities, accounting for 6.40% of total emissions, are also not negligible. Regarding emission sources, material emissions exhibit an extreme dominance at 89.44%, whereas off-road machinery and transport vehicles demonstrate distinct patterns of input-emission asymmetry and single-vehicle-type dominance, respectively. Consequently, this study delineates decarbonization pathways, quantitatively verifies the low-carbon efficacy of four mitigation technologies, and focuses on evaluating the environmental-economic dual feasibility of substituting core equipment with pure electric alternatives. The results demonstrate that the resin-bonded gravel sound barrier achieves an emission reduction rate of 73.26%; furthermore, under a 50% penetration rate, deploying 30-ton class EV dump trucks can cut transportation emissions by 9.98%, maintaining exceptional financial resilience even under a 30% plunge in diesel prices. This study provides a replicable, scientific decision-making basis for the deep decarbonization of the transportation infrastructure sector. Full article

The paper examines the multifractal and asymmetric behavior of oil, freight and exchange rate markets in the global trade system with the help of the Asymmetric Multifractal Detrended Fluctuation Analysis (AMF-DFA) technique. Based on daily data of West Texas Intermediate (WTI) crude oil, the Baltic Dry Index (BDI), and the exchange rate between the RMB/USD over the period of post-COVID-19 (2021–2024), the analysis focuses on whether efficiency in the markets varies across time scales and directional regimes. The findings show that there is strong evidence of multifractality in all markets, which implies that the scaling behavior is heterogeneous, and that it is long-range-dependent. Notable directional persistence is found between up and down movements with oil and exchange rate markets showing stronger directional persistence, especially at longer horizons with the freight markets displaying relatively weaker directional persistence. Additional results imply that temporal dependence and nonlinearity are the main drivers of multifractality in oil and exchange rate markets, and short-term fluctuations are prevalent factors in the dynamics of the freight market. These findings not only refute the classical Efficient Market Hypothesis but also provide empirical evidence in support of the Adaptive Market Hypothesis, and how efficiency is dynamic, dependent on scale, and directionally asymmetric. The study contributes by examining asymmetric multifractal efficiency across three trading markets during the post-COVID period, while recognizing that formal cross-market spillover analysis remains a direction for future research. Full article

This paper examines whether standard price-discovery measures can reliably identify directional predictive precedence in a highly correlated commodity-equity system. Using 21 years of daily data for Petrobras and the Ibovespa (2005–2026), the study separates a measurement problem in forecast error variance decomposition from the reduced-form question of directional predictability in the conditional mean. The empirical strategy combines Monte Carlo simulation, generalized and Cholesky forecast error variance decompositions, full-sample and rolling-window Granger causality tests, a continuous Granger Leadership Index, Gaussian mixture regime classification, robustness checks, and out-of-sample forecasting validation. The results show that Cholesky-based FEVDs can be systematically misleading in high-correlation settings: at the observed contemporaneous correlation, generalized FEVD symmetry is mechanically induced by row normalization, while Cholesky attribution changes sharply under alternative orderings. By contrast, first-moment predictability reveals a directional asymmetry from Petrobras to the Ibovespa, interpreted as conditional-mean predictive precedence rather than structural informed trading or definitive price discovery. This asymmetry survives alternative lag structures, weekly aggregation, univariate GARCH filtering, within-dataset proxy controls, and a stylized equal-weight ex-Petrobras benchmark. Rolling evidence further identifies five persistent predictive regimes that alternate between firm-led, neutral, and macro-dominant states, indicating that firm-index predictive relations are regime dependent rather than static. Out-of-sample forecasting shows that the identified predictive precedence does not generate exploitable one-step-ahead gains (RMSE ratio = 1.002, OOS-R² = −0.003, DM p = 0.451), thereby delimiting the economic scope of the findings. Overall, the results support a reduced-form interpretation of Petrobras–Ibovespa predictive dynamics and highlight the need to distinguish variance connectedness from conditional-mean predictive content when contemporaneous correlation is high. Full article

This study analyzes the asymmetric effects of oil prices on Saudi Arabia’s trade balance from 1990 to 2024 using a nonlinear autoregressive distributed lag (NARDL) model. By decomposing oil price changes into positive and negative partial sums, this model estimates short- and long-run asymmetric responses and traces adjustment paths using dynamic multipliers. Our results indicate a stable cointegrating relationship and pronounced asymmetry: a $1 increase in oil prices raises the trade balance by approximately $1.56 billion in the long run, whereas a $1 decrease reduces it by about $1.85 billion. The dynamic multipliers show substantial immediate effects, approximately +$2.76 and −$2.88 at horizon 0, which gradually converge to their respective long-run levels, with negative shocks producing larger and more persistent adverse effects than positive shocks. These findings emphasize KSA’s external vulnerability to oil price declines and underscore the need for countercyclical fiscal rules, reserve buffers, and accelerated non-oil export growth to mitigate downside risks and effectively manage external trade in response to global oil market volatility. Full article

This paper examines the structural changes that the Indian equity market has experienced between 2015 and 2025 under the influence of major macroeconomic and geopolitical shocks—including the November 2016 demonetisation, the IL&FS liquidity crisis of 2018, the COVID-19 pandemic of 2020–2021, the Russo–Ukrainian conflict of 2022, and the synchronised global monetary tightening of 2022–2024. The primary objective is to test whether the volatility-modelling architecture proposed by a 2017 benchmark study for the 1992–2016 period continues to hold under the structurally different post-2015 regime, and to identify how persistence, asymmetry, and ARCH-order properties have evolved across the BSE Sensex, NSE CNX Nifty, and twenty-seven sectoral indices. A unified GARCH-family framework comprising GARCH(1,1), GJR-GARCH(1,1), and GARCH(2,1) is estimated on daily log-returns over an eleven-year sample of approximately 2750 observations per index. The empirical evidence confirms that volatility clustering and persistence are pervasive in the post-2015 decade, with the persistence measure (α1 + β1) rising relative to the initial 2017 study for most indices. Asymmetric volatility has intensified—negative shocks generate disproportionately larger volatility responses than positive shocks, particularly in the banking, FMCG, and energy sectors. A higher-order GARCH(2,1) specification is the preferred model for four indices in which lag-2 ARCH effects remain significant or in which integrated-GARCH behaviour rules out the standard GARCH(1,1). The findings have direct implications for portfolio risk management, option pricing, and the design of prudential policy in an increasingly retail-driven and derivative-intensive market ecosystem. Full article

Global retail e-commerce sales have surged, yet product fit uncertainty remains a significant challenge, leading to rising product return rates. To address consumer concerns about return shipping costs, major Chinese online retail platforms have introduced return shipping insurance (RSI). Retailers can choose between Retailer-RSI (RRSI), which is provided by the retailer, and Customer-RSI (CRSI), which is purchased by consumers. Despite these options, information asymmetry causes insurers to assess return rates with bias—referred to as managerial confidence bias. Consequently, platforms are increasingly partnering with insurers to enhance their RSI offerings. This study develops a game-theoretical model to examine the dynamics between a platform and an insurer, as well as the impact of managerial confidence bias on RSI strategies. Our analysis reveals that the platform–insurer relationship is crucial in determining the optimal RSI strategy. Under an independent insurer, RSI is viable only if the insurer underestimates product return rates (i.e., exhibits overconfidence bias); RRSI is preferred if the bias is sufficiently strong, whereas CRSI is chosen otherwise. In contrast, under a dependent insurer, CRSI is favored by the retailer only when its return handling costs are substantially high; otherwise, RRSI is preferred. Furthermore, RSI consistently increases consumer surplus by reducing return hassle costs while only mildly raising the product price. However, the independent insurer’s bias leads to its own profit loss, resulting in a “loss–win–win–win” scenario across stakeholders. In contrast, the dependent insurer, supported by platform subsidies, can yield a “win–win–win–win” outcome that aligns stakeholder interests and enhances long-term platform benefits. Full article

Using a dataset covering January 1975 to March 2026 and six agricultural commodities, wheat, corn, soybeans, oats, sugar, and coffee, this paper explores the role of geopolitical risk (acts and threats) in shaping cross-market connectedness. It proposes a multilayer methodology based on the time-varying parameter vector autoregressive (TVP-VAR), the exponential GARCH with exogenous variables (EGARCH-X), and the wavelet quantile correlation (WQC) frameworks. This methodology captures cross-market volatility spillovers, assesses the effects of geopolitical risk and its components on the strength and instability of connectedness, and incorporates nonlinearity and asymmetry across investment horizons and market conditions. The results show a time-varying pattern in agricultural cross-market connectedness. Corn and soybeans transmit volatility shocks, while the other commodities are net receivers. These commodities have a central position in the connectivity network, whereas sugar and coffee are in the peripheral zone. The EGARCH-X results show that geopolitical acts and threats do not significantly alter the overall level of connectedness but intensify its volatility, suggesting that geopolitical tensions primarily influence stability rather than the intensity of connectedness. Economic policy uncertainty and oil price volatility have similar effects. In line with these results, the WQC analysis uncovers significant nonlinearity and state-dependent linkages, underscoring that the effect of geopolitical acts and threats becomes prominent over medium- and long-term horizons and during periods of market stress. These findings contribute to the literature by differentiating the effects of geopolitical incidents on agricultural market connectedness versus volatility. From an operational standpoint, these results imply that policymakers and market operators should enhance their risk-monitoring and hedging strategies during periods of high geopolitical stress, as such events can amplify instability across agricultural commodity markets. Full article

This study provides strong evidence of structural asymmetries between religious and non-religious tourism demand in Saudi Arabia over the period 2015Q1–2024Q4. The unit root results indicate that all variables are integrated of order one, supporting the application of the ARDL framework. The bounds test confirms the existence of a long-run equilibrium relationship for both religious and non-religious tourism. However, the strength and determinants of these relationships differ across tourism segments, providing evidence of structural heterogeneity in tourism demand. The empirical findings show that global oil prices do not have a statistically significant direct effect on either tourism segment in both the short run and the long run, suggesting that their influence is indirect and transmitted through broader macroeconomic channels. In contrast, non-oil GDP exerts a positive effect on non-religious tourism and remains weakly significant in the long run, highlighting the critical role of economic diversification and sustained income growth under Vision 2030. Religious tourism, however, remains largely unaffected by economic growth, reflecting its institutional and policy-driven nature. The COVID-19 pandemic had a severe and persistent negative impact on tourism demand, with a more immediate and pronounced effect on religious tourism due to the suspension of Hajj and Umrah activities. Adjustment dynamics indicate that both tourism segments converge toward their long-run equilibrium following short-run shocks, although religious tourism exhibits a somewhat faster speed of adjustment. Diagnostic tests confirm that all econometric assumptions are satisfied, supporting the robustness and reliability of the results. Full article

This study examines the relationship between Environmental, Social, and Governance (ESG) performance and financing and pricing outcomes in green and conventional bond markets in China over the period of 2017–2024. Drawing on signaling theory, information asymmetry theory, and market segmentation theory, the study argues that the role of ESG performance differs across bond types because green and conventional bonds operate within different institutional and informational environments. Using a comparative analysis of green and conventional bonds, the findings show that ESG performance is more strongly and consistently associated with conventional bond characteristics, particularly in relation to issuance amount, yield to maturity, and credit spreads. In contrast, ESG effects in green bonds are weaker and less consistent, suggesting that investors place greater emphasis on certification mechanisms, environmental project objectives, and sustainability-related bond characteristics than on broader issuer-level ESG disclosures. The findings also suggest that ESG information does not affect all debt instruments in the same way or always functions as a purely risk-reducing signal. In the Chinese market, stronger ESG exposure may also be associated with transition risks, regulatory pressures, and sector-specific sustainability challenges, particularly in conventional bond markets. Overall, the results indicate that the financial relevance of ESG performance depends not only on firm characteristics but also on the institutional and informational environment of the financial instrument itself. The findings remain robust across alternative model specifications and sensitivity analyses, providing additional confidence in the reported differences between green and conventional bond markets. The study contributes to the sustainable finance literature by showing that the pricing relevance of ESG information is instrument-specific rather than uniform across debt markets. It also provides practical implications for regulators, investors, and issuers by highlighting the importance of disclosure quality, transparency standards, and external verification mechanisms in strengthening investor confidence and reducing potential greenwashing risks in sustainable finance markets. Full article

This study introduces a comprehensive framework for understanding sustainable online shopping preferences, validated using survey data collected in Serbia and Slovenia in 2025 (n = 572), thereby enhancing its generalizability. The primary aim of this research is to examine the extent to which specific environmental, social, and economic indicators influence decision-making processes for online purchasing and delivery. A detailed quantitative analysis was conducted using a structured questionnaire that included a wide range of variables related to online shopping behaviors and delivery preferences. The findings indicate that preferences for sustainability are inherently complex and multifaceted, shaped by critical factors such as environmental concerns, social responsibility, trust, skepticism towards sustainability claims, willingness to pay (WTP), and price sensitivity. Demographic variables, particularly gender and age, show consistent links to preferences for environmental considerations and corporate social responsibility (CSR), while income impacts trust-related behaviors and WTP. Furthermore, the analysis distinguishes between two distinct decision-making approaches: a value-driven sustainability cluster represented by EcoIndex, SocialIndex, and WTPIndex, and a cost-minimization strategy focused on price sensitivity (PriceIndex), with trust acting as a related yet separate factor (CredibilityIndex). Overall, this study emphasizes that a range of interconnected dimensions significantly shape sustainable online shopping preferences. The study was conducted in two developing European countries. Additionally, the findings highlight the need to address universal market barriers, such as price sensitivity, information asymmetry, and consumer skepticism. In a business context, they underscore the importance of adopting advanced analytical methods to enhance decision-making and optimize sustainable business strategies. Full article

Against the backdrop of intensifying global climate change, climate policy uncertainty (CPU) and investor sentiment have become critical factors influencing the stability of financial markets. In this study, a quantitative index of investor sentiment is constructed using stock trading volume, turnover rate, price-to-earnings ratio, circulating market value, and the consumer confidence index. The QVAR-DY model is employed to analyze the risk contagion mechanisms among CPU, investor sentiment, and China’s financial sub-markets across different quantiles. Furthermore, five machine learning models—LSTM, BiLSTM, CNN, XGBoost, and LightGBM—are used to forecast risk spillover indices, and their performance is compared with three benchmark models (ARIMA, Persistence, and HistMean) to systematically evaluate the advantages of machine learning models in capturing tail risk spillover effects. The findings reveal significant cross-market risk contagion in financial markets, characterized by asymmetry. The level of risk spillover under extreme conditions is substantially higher than under normal conditions, indicating high sensitivity to extreme events and major policies. CPU exhibits the most pronounced spillover effect on the money market, while investor sentiment has the greatest impact on the stock market. The stock, real estate, and commodity markets act simultaneously as sources of risk and receivers of shocks. In terms of forecasting performance, LightGBM performs best under normal conditions, whereas LSTM achieves the highest prediction accuracy under extreme conditions. Full article