Search Results (408)

In this study, we investigate the return propagation mechanism, hedging effectiveness, and portfolio performance across several common agricultural commodities, crude oil, and S&P 500 index, ranging from July 2000 to June 2024 by using a time-varying parameter vector autoregression (TVP-VAR) connectedness approach and three common multiple assets portfolio optimization strategies. The empirical results show that, the total connectedness peaked during the 2008 global financial crisis, followed by the European debt crisis and the COVID-19 pandemic, while it remained relatively lower at the onset of the Russia-Ukraine conflict. In the transmission mechanism, commodities and S&P 500 index exhibit distinct and dynamic characteristics as transmitters or receivers. Portfolio analysis reveals that, with exception of the COVID-19 pandemic, all three dynamic portfolios outperform the S&P 500 benchmark across major global crises. Additionally, the minimum correlation and minimum connectedness strategies are superior than transitional minimum variance method in most scenarios. Our findings have implications for policymakers in preventing systemic risk, for investors in managing portfolio risk, and for farmers and agribusiness enterprises in enhancing economic benefits. Full article

Blood flow restriction training (BFRT) offers notable advantages, including simplicity and time efficiency. However, no meta-analysis has yet comprehensively evaluated its effects on glucose and lipid metabolism in overweight or obese adults. This meta-analysis examines the potential efficacy of BFRT in improving glycemic and lipid control in overweight/obese adults. The literature was searched in six databases, with the search period up to 31 March 2025. A total of eight randomized controlled trials involving 267 participants were identified. Data were analyzed using Stata 18.0 and RevMan 5.4 with random effects models. Outcomes included fasting blood glucose (FBG), homeostasis model assessment of insulin resistance (HOMA-IR), and lipid profiles, and risk of bias and publication bias (Egger’s test) were assessed. BFRT significantly reduced FBG (Hedges’ g = −1.13, 95% CI: −1.65 to −0.62, p < 0.01; I² = 66.34%) and HOMA-IR (Hedges’ g = −0.98, 95% CI: −1.35 to −0.61, p < 0.01; I² = 17.33%) compared with the controls. However, no significant changes were observed in lipid profiles. Our analysis demonstrates that BFRT exhibits the favorable effect of improving glucose metabolism in overweight/obese adults; however, current evidence does not support significant advantages of BFRT for lipid metabolism improvement. Full article

Background: Persistent Postural-Perceptual Dizziness (PPPD) is a chronic vestibular disorder characterized by dizziness, instability, and visual hypersensitivity. Vestibular Physical Therapy (VPT) is commonly used, but its efficacy remains uncertain due to limited and heterogeneous evidence. Objective: This systematic review and meta-analysis aimed to evaluate the effectiveness of VPT in reducing dizziness and improving balance in individuals with PPPD. Methods: A systematic search of MEDLINE and PEDro was conducted in January 2025. Studies were selected following PRISMA guidelines and included if they assessed VPT interventions in patients diagnosed with PPPD. Risk of bias was assessed using the PEDro scale and the modified Newcastle–Ottawa Scale. The meta-analysis focused on pre- and post-intervention changes in Dizziness Handicap Inventory (DHI) scores using a random-effects model. Results: Six studies met the inclusion criteria. VPT significantly reduced DHI scores (pooled Hedges’ g = 1.60; 95% CI: 0.75–2.45), indicating a moderate to large improvement. Additional outcomes included improvements in postural control (e.g., mini-BESTest and posturography) and psychological well-being (anxiety and depression questionnaires). However, high heterogeneity (I² = 92%) was present across studies. Conclusions: VPT may improve dizziness and balance in PPPD, though evidence is limited. Further high-quality trials with standardized protocols are needed. Full article

Background: Despite growing interest in the literature on Parkinson’s disease (PD) on cognitive functioning, financial incompetence—a crucial aspect of daily living—and its modulation susceptibility by PD treatment regimens remains relatively understudied. Objective: This systematic review and meta-analysis aimed to synthesize existing evidence on how PD treatments affect financial capacity, assessing both direct financial competence and cognitive or behavioral proxies of financial decision-making. Methods: A comprehensive literature search according to PRISMA protocol was conducted across major biomedical databases, supplemented by gray literature and manual reference list checks. Eligible studies assessed financial capacity directly or indirectly through cognitive proxies (e.g., executive function, decision-making) or financial risk behaviors (e.g., impulse control disorders). Two separate meta-analyses were performed. Heterogeneity (I²), publication bias (Egger’s test), and sensitivity analyses were conducted to assess robustness. Results: Twenty-three studies met inclusion criteria. One study directly measured financial capacity and was analyzed narratively, reporting diminished competence in patients on levodopa therapy. A meta-analysis of cognitive proxies (10 studies) showed a moderate effect size (Hedges’ g = 0.70, 95% CI [0.45, 0.92], p < 0.001), indicating that PD treatments negatively affect executive function and financial decision-making. A second meta-analysis of impulse control and financial risk behaviors (12 studies) revealed a larger effect size (Hedges’ g = 0.98, 95% CI [0.75, 1.22], p < 0.001), strongly linking dopamine agonists to increased financial risk-taking. Moderate heterogeneity (I² = 45.8–60.5%) and potential publication bias (Egger’s test p = 0.027) were noted. Conclusions: These findings suggest that PD treatments negatively impact financial decision-making both directly and indirectly through cognitive and behavioral pathways. Integrating financial decision-making assessments into PD care, particularly for patients on dopamine agonists, is recommended. Future research should prioritize longitudinal studies and standardized neuropsychological measures to guide clinical practice and optimize patient outcomes. Full article

Background/Objectives: The importance of resistance training for functional recovery in Total Knee Arthroplasty (TKA) patients has been emphasized. Therefore, this systematic review and meta-analysis was conducted to analyze its effects on pain, muscle strength, and function in patients with TKA. Methods: The following databases were used: PubMed, Web of Science, the Cochrane Library, and Embase. Randomized controlled trials that administered resistance training to patients undergoing TKA and measured pain, strength, and function were included. The risk of bias was assessed using the Cochrane Risk of Bias 2.0 tool. Effect sizes were calculated using Hedges’ g and are presented as Standardized Mean Differences (SMDs) with 95% Confidence Intervals (CIs). Subgroup analyses were conducted to determine the effect size based on the type and duration of the intervention. Results: The study selection process resulted in the inclusion of seven studies comprising a total of 439 participants. The bias assessment found that three studies had a low risk of bias and four had some concerns. Resistance training was effective in improving pain (SMD: 0.84, 95% CI: 0.11; 1.57, I²: 89.6%), muscle strength (SMD: 1.03, 95% CI: 0.29; 1.77, I²: 83.1%), self-reported function (SMD: 1.58, 95% CI: 0.15; 3.01, I²: 93.1%), and performance-based function (SMD: 0.74, 95% CI: 0.38; 1.11, I²: 68.9%). Subgroup analysis revealed significant differences in pain, strength, and performance-based function by comparison group, performance-based function by intervention duration, and self-reported function by intervention type. Conclusions: Resistance training improves pain, muscle strength, and function in TKA patients. Additionally, resistance training appears particularly effective when implemented as a standalone intervention or for durations under 12 weeks. These findings suggest that the design of resistance training protocols should be considered in clinical practice. Full article

Loneliness is a recognized public health risk factor associated with increased morbidity and mortality. However, the effectiveness of interventions targeting loneliness remains unclear—particularly in relation to baseline severity. This systematic review and meta-analysis assessed intervention effectiveness and the influence of baseline severity and intervention characteristics. A total of 25 studies were included, of which 16 randomized controlled trials (RCTs; k = 21) were meta-analyzed. Interventions produced a moderate pooled effect at post-intervention (Hedge’s g = 0.65, 95% CI [0.05, 1.26], p = 0.037), though with high heterogeneity. Sensitivity analyses confirmed a moderate effect (g = 0.55, 95% CI [0.22, 0.88], p = 0.003). Higher baseline loneliness predicted greater intervention effects (b = 0.04, 95% CI [0.02, 0.07], Z = 3.36, p < 0.001), with cognitive-behavioral therapy (CBT) showing the largest effect size (g = 0.73). No significant effects were observed at follow-up. These findings underscore the need for dual strategies: targeted psychological interventions (e.g., CBT) for individuals with severe loneliness, and universal, context-based approaches for the broader population. This aligns with Geoffrey Rose’s distinction between individual-level treatment and population-level prevention and highlights the urgency of embedding loneliness interventions into public health frameworks and policy agendas focused on promoting social connectedness and equity. Full article

The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoin’s volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions. Full article

This study responds to the call by some scholars to establish a framework for ignorance. It challenges the myth that ignorance is all bad and an utterly undesirable state in organizations and proposes a new framework for the application of ignorance analytics in organizations. It includes a taxonomy of deliberate and unconscious ignorance in decision-making and judgment as well as the drivers of personal and corporate deliberate ignorance and their behavioral implications. Ignorance plays a substantial role in competency development, scientific progress, innovation, and organizational strategic advantage. The proposed framework can help developers of talent, including management trainers, educators, and HR practitioners, to recognize the drivers of willful ignorance and help managers design effective interventions to move employees from unconscious incompetence to mastery. This paper suggests an agenda and identifies opportunities for future research. Full article

This systematic review and meta-analysis evaluates (1) the effectiveness of psychedelic-assisted therapy (PAT) using psilocybin and ketamine for psychosocial symptoms in adults with cancer, (2) contextualizes findings with non-randomized and exploratory studies of other psychedelics, and (3) examines the role of therapeutic frameworks in shaping outcomes. We searched PubMed, Cochrane Library, PsycINFO, and EMBASE (2000–2024) for randomized controlled trials (RCTs) and non-randomized studies investigating psychedelic agents in cancer populations. Meta-analyses pooled RCTs of psilocybin or ketamine using random-effects models. Non-randomized studies were synthesized narratively. Risk of bias and evidence certainty were assessed via Cochrane ROB 2.0, NIH Before–After tool, and GRADE. Eleven placebo-controlled RCTs and four single open-label studies were included. Meta-analysis of four ketamine RCTs (n = 354) showed large, rapid effects on depression/anxiety (Hedges’ g = −1.37, 95% CI: −2.66 to −0.08; I² = 92%). Three psilocybin RCTs (n = 101) showed a large effect of psilocybin on alleviating depression (Hedges’ g = −3.13, 95% CI: −10.04 to 3.77; I² = 95%). MDMA and LSD trials suggested promise but lacked rigor. PAT may offer meaningful relief for cancer-related distress, though effects vary by therapeutic model and context. Oncology-specific trials are needed to standardize and scale for implementation. Full article

This paper demonstrates that perfectly calibrating a multi-asset model to observed market prices of all basket call options is insufficient to uniquely determine the price of a best-of call option. Previous research on multi-asset option pricing has primarily focused on complete market settings or assumed specific parametric models, leaving fundamental questions about model risk and pricing uniqueness in incomplete markets inadequately addressed. This limitation has critical practical implications: derivatives practitioners who hedge best-of options using basket-equivalent instruments face fundamental distributional uncertainty that compounds the well-recognized non-linearity challenges. We establish this non-uniqueness using convex analysis (extreme ray characterization demonstrating geometric incompatibility between payoff structures), measure theory (explicit construction of distinct equivalent probability measures), and geometric analysis (payoff structure comparison). Specifically, we prove that the set of equivalent probability measures consistent with observed basket prices contains distinct measures yielding different best-of option prices, with explicit no-arbitrage bounds $[a_K,b_K]$ quantifying this uncertainty. Our theoretical contribution provides the first rigorous mathematical foundation for several empirically observed market phenomena: wide bid-ask spreads on extremal options, practitioners’ preference for over-hedging strategies, and substantial model reserves for exotic derivatives. We demonstrate through concrete examples that substantial model risk persists even with perfect basket calibration and equivalent measure constraints. For risk-neutral pricing applications, equivalent martingale measure constraints can be imposed using optimal transport theory, though this requires additional mathematical complexity via Schrödinger bridge techniques while preserving our fundamental non-uniqueness results. The findings establish that additional market instruments beyond basket options are mathematically necessary for robust exotic derivative pricing. Full article

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. Full article

There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and diversification benefits of integrating cryptocurrencies into South African bank equity portfolios. Using advanced financial engineering techniques, including multi-asset particle swarm optimizer (MA-PSO), random optimizer, and a static equal-weighted portfolio (EWP) model, this study analyzed the dynamic portfolio performance and diversification of cryptocurrencies in the 2017–2024 period. The portfolio performance of the three methods is also compared with the results from the traditional one-period mean–variance optimization (MVO) method. The findings underscore the superiority of dynamic models over static EWP in assessing the impact of cryptocurrency inclusion in bank equity portfolios. While pre-COVID-19 studies identified cryptocurrencies as effective hedges against market downturns, this protective role appears attenuated in the post-COVID-19 era. The dynamic MA-PSO model emerges as the optimal approach, delivering better-diversified portfolios. Consequently, South African portfolio managers must carefully evaluate investor risk tolerance before incorporating cryptocurrencies, with regulators imposing stringent guidelines to mitigate potential losses. Full article

This paper proposes the Digital Metacognitive Question–Answer Relationship (dmQAR) Framework, an adaptation of traditional QAR models for the complexities of digital reading environments. In response to the nonlinear, multimodal, and algorithmically curated nature of online texts, the dmQAR Framework scaffolds purposeful metacognitive questioning to support comprehension, evaluation, and critical engagement. Drawing on research in metacognition, critical literacy, and digital reading, the framework reinterprets “Right There,” “Think and Search,” “Author and Me,” and “On My Own” question categories to align with the demands of digital spaces. Practical instructional strategies, including think-alouds, student-generated questioning, digital annotation, and reflection journals, are detailed to support implementation across diverse educational contexts. The paper emphasizes that developing self-regulated questioning is essential for fostering critical literacy and resisting surface-level engagement with digital texts. Implications for instruction highlight the need for explicit metacognitive scaffolding and equitable access to digital literacy tools. Future research directions include empirical validation of the framework’s impact on digital reading comprehension and exploration of developmental differences in metacognitive questioning practices. In an era of widespread misinformation and algorithmic bias, embedding metacognitive questioning into literacy education is vital for preparing students to navigate digital landscapes critically and reflectively. Full article

This research examines the challenges of issuing green bonds due to a lack of established benchmarks. We compare regional differences between the U.S. and the E.U., hypothesizing that issuers of green bonds stand to benefit from comparing them to conventional (black) bonds. As most investors prioritize net positive returns as opposed to intangible sustainability metrics, the existence of a “green premium”, defined as the opportunity to price green bonds differently, remains to be proven. To this end, we employ a time-varying parameter vector autoregression (TVP-VAR), first deriving dynamic variance–covariance matrices and then conducting variance decomposition analysis to gauge connectedness and spillover effects of various bond benchmarks. Implementing multivariate portfolio construction strategies, we investigate the hedging capabilities of green and black bonds. Our findings show that both green and black bonds contribute to portfolio diversification as a risk management strategy. The paper highlights the role played by green bonds in promoting financial stability. Full article

This study presents a bibliometric analysis, using spatial approach, of 943 articles from 2003 to March 2025 showing the growing importance of CDSs in the literature and their role in credit risk management. The Web of Science’s Core Collection database was used for bibliometric mapping. The bibliographic data were grouped and analyzed using VOSviewer to create network visualization maps that included country-wise, document-wise, and source-wise citations analysis, bibliographic coupling, and the co-occurrence of keywords. Subsequently, significant terms were identified through the analyses where risk assessment, risk management, and credit derivatives were found to be the most used keywords. Further, USA turns out to be the country where the most research was published on CDSs with maximum citations, highlighting the growing popularity of this research topic in this region. In addition, bibliographic coupling appears to capture information from 13 clusters formed during the analysis on bibliographically linked documents with their link strength. The bibliometric analysis of the CDS literature illustrates the intellectual framework of research on this topic, traces the progression of the research topic over time, and identifies the areas where this research field might develop in the future. Full article