Search Results (244)

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

Background: Musculoskeletal disorders significantly affect healthcare professionals, particularly physiotherapists, due to the physical demands of their work. The link between physical ailments and psychological distress is especially prominent in clinical settings. Objectives: To assess the prevalence of work-related low back pain (LBP), stress, anxiety, and depression among physiotherapists in Saudi Arabia, and to identify associated local risk factors. Methods: A cross-sectional study using convenience sampling included 710 licensed physiotherapists across Saudi Arabia. Participants completed an online survey containing demographic data and the validated measures, including the Visual Analog Scale (VAS) for pain, the Oswestry Disability Index (ODI), and the Depression, Anxiety, and Stress Scale-21 (DASS-21) for psychological distress. Data were analysed using descriptive statistics, chi-square tests, correlation, and regression analyses. Results: Of 710 responses, 697 were valid; 378 physiotherapists reported work-related LBP. The mean pain intensity was 4.6 (SD = 1.6), with 54.2% experiencing moderate to severe disability. Mental health results showed 49.7% had depressive symptoms and 33.9% experienced some level of anxiety. Significant correlations were observed between disability and psychological distress (anxiety: r = 0.382; depression: r = 0.375; stress: r = 0.406; all p < 0.001). Regression analyses indicated psychological distress significantly predicted disability, with R² values ranging from 0.125 to 0.248, being higher among inpatient physiotherapists. Conclusions: This study reveals a high prevalence of LBP and psychological distress among Saudi physiotherapists, with stress being the strongest predictor of LBP severity. Integrated ergonomic and mental health interventions, including workplace wellness programs and psychological support, are recommended to reduce risks and promote a healthier, more sustainable physiotherapy workforce. Full article

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the model to account for both asymmetric volatility and evolving correlation patterns over time. We adopt a fully Bayesian inference approach using adaptive Markov chain Monte Carlo (MCMC) techniques, allowing for the joint estimation of model parameters, Value-at-Risk (VaR), and Marginal Expected Shortfall (MES). The accuracy of VaR forecasts is assessed through two standard backtesting procedures. Our empirical analysis involves both simulated data and real-world financial datasets to evaluate the model’s effectiveness in capturing downside risk dynamics. We demonstrate the application of the proposed method on three pairs of daily log returns involving the S&P500, Bank of America (BAC), Intercontinental Exchange (ICE), and Goldman Sachs (GS), present the results obtained, and compare them against the original model framework. Full article

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design. Full article

This study investigated the accumulation characteristics and associated health risks of 11 trace elements (Al, Rb, Cr, Ni, Mo, Sr, Pb, Ba, Ag, As, and Ga) in four crucian carp varieties: gene-edited intermuscular bone-free crucian carp (Carassius auratus, WUCI) and its sibling wild-type (Carassius auratus, WT), Fangzheng silver crucian carp (Carassius gibelio var Fangzheng, FZYJ), and Songpu silver crucian carp (Carassius gibelio var Songpu, SPYJ). Results showed that Al and Rb were the most abundant elements across all groups. WUCI exhibited distinct accumulation patterns, including significantly higher hepatic Mo concentrations (0.265 ± 0.032 mg/kg) and muscle/liver Rb levels (muscle: 8.74 ± 1.21 mg/kg; liver: 12.56 ± 2.05 mg/kg) compared to other varieties (p < 0.05), which supports the hypothesis of genotype-specific differences in heavy metal accumulation. Correlation analysis revealed that WUCI exhibited similar elemental interactions with WT and SPYJ (e.g., Al-Ni positive correlation, |rs| ≥ 0.8), while SPYJ displayed distinct patterns with fifteen negative correlations compared to three to five in others varieties, suggesting a potential alteration in elemental homeostasis. Pollution index (P_i) assessments indicated mild contamination for Pb in SPYJ liver (P_i = 0.265) and Cr/As in WUCI muscle (P_i = 0.247/0.218). Despite these values, all hazard indices remained below the established safety thresholds (THQ < 0.1, HI < 0.25, TCR < 10⁻⁶), reinforcing the overall safety of the tested fish. Notably, muscle As levels (0.86 ± 0.15 mg/kg) exceeded hepatic concentrations (0.52 ± 0.09 mg/kg), potentially due to differential detoxification mechanisms. These findings demonstrate the food safety of all tested varieties, while highlighting genotype-specific metabolic adaptations, providing critical data for evaluating gene edited aquatic products. Full article

The aim of this article is to forecast the systemic risk contribution and exposure measured by the delta conditional value at risk (ΔCoVaR) and the marginal expected shortfall (MES), respectively. We first estimate the ΔCoVaR and MES for banks in 16 European countries for the 2002–2016 period. We then predict systemic risk measures using machine learning techniques, such as artificial neural network (ANN) and support vector machine (SVM), and we use AR-GARCH specification. Finally, we compare the methods’ forecasting values and the actual values. Our results show that two hidden layers of artificial neural networks perform efficiently in forecasting systemic risk. 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

Value-at-Risk (VaR) estimation using the GARCH model is an important topic in financial data analysis. It allows for an increase in the accuracy of risk assessment by controlling time-varying volatility. In this paper, we enhance this feature by exploring the functional path of the financial data. More precisely, we study the nonparametric estimation of the multi-functional VaR function using the local linear method, construct an estimator, and establish its stochastic consistency. The derived asymptotic result provides a rigorous mathematical foundation that permits boosting the use of the VaR function in financial data analysis. Furthermore, an empirical analysis is performed in order to examine the efficiency of the proposed algorithm. Additionally, a real data application is created to highlight the multi-functionality of the VaR estimation for multi-asset risk management. Full article

This study proposes a hybrid forecasting model that integrates the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a Long Short-Term Memory (LSTM) neural network to estimate Value at Risk (VaR) in the Rwandan foreign exchange market. The model is designed to capture both volatility clustering and temporal dependencies in daily exchange rate returns. Using daily data on USD, EUR, and GBP from 2012 to 2024, we evaluate the model’s performance relative to standalone GARCH(1,1) and LSTM models. Empirical results show that the hybrid model improves VaR estimation accuracy by up to 10%, especially during periods of elevated market volatility. These improvements are validated through MSE, MAE, and backtesting statistics. The enhanced accuracy is particularly relevant in emerging markets, where exchange rate dynamics are highly nonlinear and sensitive to external shocks. The proposed approach offers practical insights for financial institutions and regulators seeking to improve market risk assessment in emerging economies. Full article

The market risk measurement of a trading portfolio in banks, specifically the practical implementation of the value-at-risk (VaR) and expected shortfall (ES) models, involves intensive recalls of the pricing engine. Machine learning algorithms may offer a solution to this challenge. In this study, we investigate the application of the Gaussian process (GP) regression and multi-fidelity modeling technique as approximation for the pricing engine. More precisely, multi-fidelity modeling combines models of different fidelity levels, defined as the degree of detail and precision offered by a predictive model or simulation, to achieve rapid yet precise prediction. We use the regression models to predict the prices of mono- and multi-asset equity option portfolios. In our numerical experiments, conducted with data limitation, we observe that both the standard GP model and multi-fidelity GP model outperform both the traditional approaches used in banks and the well-known neural network model in term of pricing accuracy as well as risk calculation efficiency. Full article

Climate change is reshaping the distribution of forest species globally, yet its effects on the temperate tree genus Carpinus in China remain understudied. This study used an ensemble species distribution modeling framework to predict current and future suitable habitats for 32 Carpinus taxa under three shared socioeconomic pathway (SSP) climate scenarios for the 2090s. Five algorithms were integrated, and models with high predictive performance (AUC > 0.9) were used to generate ensemble forecasts. The ensemble models achieved AUC values no lower than 0.987 and TSS values no lower than 0.904. The results showed a clear trend of northwestward and upslope range shifts, with substantial habitat contractions under high-emission scenarios. Temperature seasonality and annual precipitation were identified as key environmental drivers. Two narrowly distributed species, C. omeiensis and C. londoniana var. lanceolata, are projected to lose all suitable habitats under SSP585, indicating a high extinction risk. These findings emphasize the importance of integrating climate-based risk assessments into conservation strategies and highlight the need to prioritize vulnerable species and high-elevation refugia to safeguard the long-term persistence of Carpinus diversity in China. Full article

In this study, the Gumbel distribution is utilized to construct exact analytical representations for two pivotal measures in financial risk evaluation: Value at Risk (VaR) and Conditional Value at Risk (CVaR). These refined formulations are developed with the intention of offering resilient and practically implementable tools to address the complexities inherent in economic risk analysis. Moreover, the newly established expressions are seamlessly integrated into the GARCH modeling framework, thereby enriching its predictive capabilities. In order to verify both the practical relevance and theoretical soundness of the presented methodology, it is systematically employed regarding the daily return series of a varied portfolio of stocks. The outcomes of the numerical experiments provide compelling evidence of the approach’s reliability and effectiveness, emphasizing its suitability for advancing contemporary risk management strategies in financial environments. Full article

Studying the risk propagation mechanisms in agricultural systems is crucial for maintaining agricultural stability and promoting sustainable development. This research analyzes the risk effects and risk propagation mechanisms in agricultural systems using the DCC-t-Copula-CoVaR model, multi-layer network structures, and the mixed-frequency regression MIDAS model. The study finds that there is significant heterogeneity in risk spillover and absorption in agricultural systems; the risk propagation in agricultural systems is stable, and the stronger the connectivity of industry nodes, the greater the risk. Taking the seed industry as an example, its structural indicator values consistently range between 1.0 and 1.1, with fluctuations closely linked to industry development and policy adjustments. Major risks are caused by risk resonance across multiple industries, not triggered by a single industry alone; the interconnections between industries within the agricultural system can disperse risks, forming a collective risk-sharing mechanism. Understanding these dynamics is essential for developing resilient agricultural practices that support long-term sustainability, ensuring food security, and mitigating environmental impacts. By addressing risk propagation and fostering interconnected risk-sharing mechanisms, agricultural systems can better adapt to challenges such as climate change, resource scarcity, and market volatility, ultimately contributing to a more sustainable and stable global food system. Full article