Search Results (1,813)

In this study, we aim to provide a comprehensive analysis of the risk management potential of sectoral Credit Default Swaps (CDSs) within financial portfolios. Our objectives are threefold: (i) to investigate the safe haven properties of sectoral CDSs; (ii) to assess their hedging effectiveness and evaluate the diversification benefits of incorporating sectoral CDSs into both conventional and Islamic stock market portfolios; and (iii) to compare these findings with those obtained from alternative assets such as the VSTOXX, gold, and Bitcoin indices. To achieve this, we estimate time-varying hedge ratios using a range of multivariate GARCH (MGARCH) models and subsequently compute hedging effectiveness metrics. Conditional correlations derived from the Asymmetric Dynamic Conditional Correlation (ADCC) model are employed in linear regression analyses to assess safe haven characteristics. This methodology is applied across different subperiods to capture the impact of the crypto currency bubble and the COVID-19 pandemic on hedging performance. Full article

Our study focused on the quantification of aboveground biomass stock and aboveground net primary productivity (ANPP) in young, planted beech (Fagus sylvatica L.). We selected 15 young even-aged stands targeting moderately fertile sites. Three rectangular plots were established within each stand, and all trees were annually measured for height and stem basal diameter from 2020 to 2024. For biomass modeling, we conducted destructive sampling of 111 beech trees. Each tree was separated into foliage and woody components, oven-dried, and weighed to determine dry mass. Allometric models were developed using these predictors: tree height, stem basal diameter, and their combination. Biomass accumulation was closely correlated with stand age, allowing us to scale tree-level models to stand-level predictions using age as a common predictor. Biomass stocks of both woody parts and foliage increased with stand age, reaching 48 Mg ha⁻¹ and 6 Mg ha⁻¹, respectively, at the age of 15 years. A comparative analysis indicated generally higher biomass in naturally regenerated stands, except for foliage at age 16, where planted stands caught up with the naturally regenerated ones. Our findings contribute to a better understanding of forest productivity dynamics and offer practical models for estimating carbon sequestration potential in managed forest ecosystems. Full article

Mangroves play a critical role in global carbon sequestration, biodiversity conservation, and climate change mitigation. Accurately quantifying mangrove biomass is essential for sustainable forest management and carbon accounting. Yet, the structural complexity and species diversity of mangrove ecosystems pose significant challenges for accurate estimation. In this study, we developed an integrated model that combines multispectral imagery and radar data. Using Sentinel-2 and ALOS-2 satellite imagery combined with field measurements, these data were used to construct linear regression and random forest models for the Matang Mangrove Reserve, Malaysia. We further analyzed the relationships between vegetation indices, radar polarization modes, and biomass. Results indicate that the average biomass is approximately 146 t/ha. The Optimized Soil-Adjusted Vegetation Index (OSAVI) and horizontal–vertical (HV) polarization showed the strongest correlation with field-measured biomass, with an R² of 0.735 and a root mean square error (RMSE) of 46.794 t/ha. This study provides a scientific basis and technical support for mangrove carbon stock assessment, ecosystem management, and climate change mitigation strategies, and highlights the potential of integrating optical and radar remote sensing for large-scale mangrove biomass monitoring. Full article

Effective management of Micropterus salmoides resources requires accurate assessment of their abundance and distribution. Fisheries acoustics is a key method for such evaluations, yet its application is limited by insufficient target strength (TS) data. This study combines the Sobel edge detection technique with the Kirchhoff ray-mode model to estimate the TS of Micropterus salmoides cultured in Guangdong, China, and validates the results through in situ measurements. The relationships between TS and fish body length were established at 38 kHz, 70 kHz, 120 kHz, and 200 kHz. At 200 kHz, the average in situ TS was –42.41 dB, with a fitted formula of TS = 32.00 lgL − 88.24. Further validation was performed using time- and frequency-domain analyses of echo signals. The results show that TS increases with swim bladder volume, indicating its dominant influence. The relationship between TS and frequency is nonlinear and affected by the swim bladder angle, swimming posture, and behavioral patterns. This study also improves the computational efficiency of the Kirchhoff ray-mode model. Overall, it provides essential parameters for acoustic stock assessment of Micropterus salmoides, providing a scientific basis for their sustainable management and conservation. Full article

The aim of this study is the estimation of the biodiversity of pure oak stands within the jurisdiction of the Forest Service of Xanthi in northeastern Greece. Using a published graded biodiversity index that operates on management-plan description sheets, we scored five stand-level attributes (total wood stock, age of trees, canopy density, presence of regeneration, and stand aspect/orientation) for every eligible stand and classified biodiversity as low, moderate, or high. These data were sourced from the description sheets of pure oak stands found in the management plans of public forest complexes. Moderate biodiversity predominates (63.4% of stands), followed by low (33.5%), while high biodiversity is scarce (3.1%). Forest practice can influence all the factors which were used for the assessment of the biodiversity characterization of the stands except the aspect of the stand. From these factors the total amount of wood stock and the canopy density were the main factors which determined the low percentage of high-biodiversity stands. On the other hand, the age structure and the regeneration existence were the main factors which counterbalanced the negative influence of the total amount of wood stock and of the canopy density and thus led to the dominance of the stands characterized as having moderate biodiversity score. Full article

The escalating frequency and severity of natural disasters present significant challenges to the stability and sustainability of global financial systems, with the US stock market being especially vulnerable. This study examines sector-level exposure and contagion dynamics during climate-related disaster events, providing insights essential for sustainable investing and resilient financial planning. Using an advanced econometric framework—dynamic conditional correlation GARCH (DCC-GARCH) augmented with Generalized Hyperbolic Processes (GHPs) and an asymmetric specification (ADCC-GARCH)—we model daily stock returns for 20 publicly traded US companies across five sectors (insurance, energy, automotive, retail, and industrial) between 2017 and 2022. The results reveal considerable sectoral heterogeneity: insurance and energy sectors exhibit the highest vulnerability, with heavy-tailed return distributions and persistent volatility, whereas retail and selected industrial firms demonstrate resilience, including counter-cyclical behavior during crises. GHP-based models improve tail risk estimation by capturing return asymmetries, skewness, and leptokurtosis beyond Gaussian specifications. Moreover, the ADCC-GHP-GARCH framework shows that negative shocks induce more persistent correlation shifts than positive ones, highlighting asymmetric contagion effects during stress periods. The results present the insurance and energy sectors as the most exposed to extreme events, backed by the heavy-tailed return distributions and persistent volatility. In contrast, the retail and select industrial firms exhibit resilience and show stable, and in some cases, counter-cyclical, behavior in crises. The results from using a GHP indicate a slight improvement in model specification fit, capturing return asymmetries, skewness, and leptokurtosis indications, in comparison to standard Gaussian models. It was also shown with an ADCC-GHP-GARCH model that negative shocks result in a greater and more durable change in correlations than positive shocks, reinforcing the consideration of asymmetry contagion in times of stress. By integrating sector-specific financial responses into a climate-disaster framework, this research supports the design of targeted climate risk mitigation strategies, sustainable investment portfolios, and regulatory stress-testing approaches that account for volatility clustering and tail dependencies. The findings contribute to the literature on financial resilience by providing a robust statistical basis for assessing how extreme climate events impact asset values, thereby informing both policy and practice in advancing sustainable economic development. Full article

In this paper, we consider the parameter estimation for the fractional Black–Scholes model of the form $S_t^H=S_0^H+\mu {\int }_0^t{S}_s^H\mathrm{d}s+\sigma {\int }_0^t{S}_s^H\mathrm{d}{B}_s^H,$ where $\sigma >0$ and $\mu \in \mathbb{R}$ are the parameters to be estimated. Here, $B^H=\{B_t^H,t\ge 0\}$ denotes a fractional Brownian motion with Hurst index $0<H<1$. Using the quasi-likelihood method, we estimate the parameters $\mu$ and $\sigma$ based on observations taken at discrete time points $\{t_i=ih,i=0,1,2,\dots ,n\}$. Under the conditions $h=h\left(n\right)\to 0$, $nh\to \infty$, and $h^{1+\gamma }n\to 1$ for some $\gamma >0$, as $n\to \infty$, the asymptotic properties of the quasi-likelihood estimators are established. The analysis further reveals how the convergence rate of $n{h}^{1+\gamma }-1$ approaching zero affects the accuracy of estimation. To validate the effectiveness of our method, we conduct numerical simulations using real-world stock market data, demonstrating the practical applicability of the proposed estimation framework. Full article

This paper proposes a novel transport quasi-Monte Carlo framework that combines randomized quasi-Monte Carlo sampling with a neural autoregressive flow architecture for efficient sampling and integration over complex, high-dimensional distributions. The method constructs a sequence of invertible transport maps to approximate the target density by decomposing it into a series of lower-dimensional marginals. Each sub-model leverages normalizing flows parameterized via monotonic beta-averaging transformations and is optimized using forward Kullback–Leibler (KL) divergence. To enhance computational efficiency, a hidden-variable mechanism that transfers optimized parameters between sub-models is adopted. Numerical experiments on a banana-shaped distribution demonstrate that this new approach outperforms standard Monte Carlo-based normalizing flows in both sampling accuracy and integral estimation. Further, the model is applied to A-share stock return data and shows reliable predictive performance in semiannual return forecasts, while accurately capturing covariance structures across assets. The results highlight the potential of transport quasi-Monte Carlo (TQMC) in financial modeling and other high-dimensional inference tasks. Full article

Accurate forest biomass estimation is essential for quantifying carbon stocks, guiding sustainable forest management, and informing climate change mitigation strategies. Kenya’s forests are diverse, ranging from Afromontane and mangrove ecosystems to dryland woodlands and plantations, each presenting unique challenges for biomass measurement. This review synthesizes literature on field-based, remote sensing, and machine learning approaches applied in Kenya, highlighting their effectiveness, limitations, and integration potential. A systematic search across multiple databases identified peer-reviewed studies published in the last decade, screened against defined inclusion and exclusion criteria. The main findings are (1) Field-based techniques (e.g., allometric equations, quadrat sampling) provide reliable and site-specific estimates but are labor-intensive and limited in scalability. (2) Remote sensing methods (LiDAR, UAVs, multispectral and radar imagery) enable large-scale and repeat assessments, though they require extensive calibration and investment. (3) Machine learning and hybrid approaches enhance prediction accuracy by integrating multi-source data, but their success depends on data availability and methodological harmonization. This review identifies opportunities for integrating field and remote sensing data with machine learning to strengthen biomass monitoring. Establishing a national biomass inventory, supported by robust policy frameworks, is critical to align Kenya’s forest management with global climate and biodiversity goals. Full article

Mangroves are critical blue carbon ecosystems, yet accurately estimating their aboveground carbon (AGC) stocks remains challenging due to structural complexity and spectral saturation in dense canopies. This study aims to develop a scalable AGC estimation framework by integrating high-resolution canopy height (CH) data from UAV-LiDAR with multi-source satellite features from Sentinel-1, Sentinel-2, and ALOS PALSAR-2. Using the Maowei Sea mangrove zone in Guangxi, China, as a case study, we extracted structural, spectral, and textural features and applied Random Forest regression with Recursive Feature Elimination (RFE) to optimize feature combinations. Results show that incorporating UAV-derived CH significantly improves model accuracy (R² = 0.75, RMSE = 14.18 Mg C ha⁻¹), outperforming satellite-only approaches. CH was identified as the most important predictor, effectively mitigating saturation effects in high-biomass stands. The estimated total AGC in the study area was 88,363.73 Mg, with a mean density of 53.01 Mg C ha⁻¹. This study highlights the advantages of cross-scale UAV–satellite data fusion for accurate, regionally scalable AGC mapping, offering a practical tool for blue carbon monitoring and coastal ecosystem management under global change. Full article

Accurate estimation of tree structural and morphological parameters is essential in precision fruit farming, supporting optimised irrigation management, biomass estimation and carbon stock assessment. While traditional field-based measurements remain widely used, they are often time-consuming and subject to operator-induced errors. In recent years, Terrestrial Laser Scanning (TLS) and UAV-based photogrammetry have been successfully employed to generate high-resolution 3D reconstructions of plants; however, their cost and operational constraints limit their scalability in routine field applications. This study investigates the performances of a low-cost, consumer-grade device—the iPhone 13 Pro equipped with an integrated LiDAR sensor and RGB camera—for 3D scanning of fruit tree structures. Cylindrical targets with known geometric dimensions were scanned using both the LiDAR and photogrammetric (Photo) modes of the Polycam© application, with accuracy and precision assessed by comparing extracted measurements to reference values. Field applicability was also tested on hazelnut trees, assessing height, stem diameter and leaf area: the Photo mode delivered the highest accuracy (systematic error of 0.007 m and R² = 0.99) and strong agreement with manual leaf measurements (R² = 0.93). These results demonstrate that smartphone-based 3D scanning can provide a practical, low-cost approach for structural characterisation in fruit orchards, supporting more efficient crop monitoring. Full article

Remote sensing offers an effective alternative for estimating mangrove carbon stocks by analyzing the relationship between satellite pixel values and field-based carbon measurements. This research was carried out in the mangrove forests of western Bali, Indonesia, encompassing three areas situated in a non-conservation mangrove forest area. This study assessed 32 remote sensing vegetation indices derived from Sentinel-2 satellite imagery to identify the optimal model for quantifying the above-ground carbon (Cag) content in mangrove ecosystems. Field data were collected using stratified random sampling and were used to develop regression models linking the Cag with vegetation indices. The Simple Ratio (SR) index exhibited the highest correlation (r = 0.847, R² = 0.707), while the Three Index Vegetation Above-Ground Carbon (TrIVCag) model, combining the SR, Specific Leaf Area Vegetation Index (SLAVI), and Transformed Ratio Vegetation Index (TRVI) indices, achieved the best performance (r = 0.870, R² = 0.728). The model validation results confirmed the reliability of the TrIVCag model, as indicated by a correlation of 0.852 between the model estimates and measured Cag values from independent field data. In 2023, the mangrove area in western Bali (excluding West Bali National Park) was estimated at 376.85 ha, with a total above-ground carbon stock of 34,994.55 tonC/ha. Region A had the highest average Cag at 98.97 tonC/ha, followed by Regions B (66.58 tonC/ha) and C (86.98 tonC/ha). This model offers a practical and scalable approach to carbon monitoring and is expected to play a valuable role in supporting blue carbon conservation efforts and contributing to climate change mitigation. Full article

This study investigates the dynamics of financial contagion using a flexible mixture copula framework, specifically a combination of the Survival Clayton and Survival Gumbel copulas, to estimate the lower tail dependence coefficient, interpreted as a measure of extreme downside co-movement or contagion. The model captures nonlinear and asymmetric dependencies between the global stock market and nine national markets: Australia, China, Hungary, India, New Zealand, Spain, Thailand, the United Kingdom, and the United States. The analysis spans the period from 2018 to 2024 and focuses on three major global crises: the China–U.S. trade war, the COVID-19 pandemic, and the Russia–Ukraine conflict. The results reveal substantial heterogeneity in contagion intensity across countries and crises. The COVID-19 pandemic generated the highest and most synchronized levels of contagion, with tail dependence exceeding 0.8 in the United States and above 0.6 in several developed and emerging markets. The China–U.S. trade war resulted in moderate contagion, particularly in countries with close trade links to the U.S. and China. The Russia–Ukraine conflict produced elevated contagion in European and energy-sensitive markets such as the UK and Spain. Conversely, China and New Zealand exhibited relatively lower levels of contagion across all periods Full article

Many companies engage in earnings manipulation that obscures their actual financial condition and sustainability efforts, undermining the credibility of financial reports and eroding stakeholder trust. To address these concerns, the United Kingdom has strictly adhered to International Financial Reporting Standards (IFRS), enhancing financial transparency and reducing the risk of manipulation. This study applies agency theory to examine the effects of earnings management and IFRS adoption on corporate sustainability performance, while also assessing the moderating roles of institutional ownership and board independence. Data were drawn from 248 companies listed on the London Stock Exchange between 2002 and 2024, using purposive sampling and sourced from Thomson Reuters Eikon DataStream. Advanced estimation techniques, specifically the Augmented Mean Group (AMG) and fixed effects models with Driscoll-Kraay standard errors, were employed to address cross-sectional dependence and slope heterogeneity. The results indicate that earnings management, as measured by discretionary accruals, has a significant negative impact on sustainability performance. In contrast, the adoption of IFRS has a positive and significant influence on sustainability outcomes. Additionally, institutional ownership and board independence significantly moderate the adverse effects of earnings management, leading to improved sustainability performance. The findings suggest that managers should enhance the clarity and accountability of financial reporting by implementing robust internal systems aligned with IFRS, conducting regular compliance audits, and training finance staff on current disclosure standards. Full article

To address the issue of spatial mismatch of real estate resources at its source, we incorporate the multidimensional urbanization speeds into the real estate market stock-flow model, simulate the regional real estate resource allocation process from a dynamic perspective, and explore the impact of urbanization on housing prices, as well as the characteristics that a coordinated multidimensional urbanization should possess. Utilizing data on population flow, economic development, and the relative increment of newly built housing units that meet delivery standards from 2008 to 2022 in 35 large- and medium-sized cities in China. We employ the dynamic panel system GMM approach to estimate the direct effect of single-dimensional urbanization on housing prices, and utilize the threshold effect model to examine the comprehensive effect of multidimensional urbanization on housing prices. The findings reveal that population, economic, and spatial urbanization influence housing prices by altering the flow of real estate supply and demand, with their effects being significantly shaped by the scarcity of stock real estate resources. The dynamic coordination of multidimensional urbanization ρ has a significant threshold effect on housing price changes. When Vsu and Vpu reach the optimal match, the real estate market achieves dynamic equilibrium, and housing prices remain relatively stable. This not only underscores the significance of multidimensional urbanization as a driver of urban housing price variations but also provides valuable insights for cities on how to adjust the quantity of new residential construction (or land supply) during the dynamic urbanization process, thereby enhancing the spatial allocation rationality of real estate resources from the source. Full article