This study investigates the hedging effectiveness and dependence structure between gold, gold ETFs, and both conventional and clean energy ETFs through copula-based models, including static and time-varying copulas. Daily return data from 2020 to 2023 is employed, with marginal volatilities filtered via GARCH models. Results show gold ETFs exhibit both upper and lower tail dependence with energy ETFs, with Gumbel and Clayton tail dependence coefficients ranging from 0.14 to 0.24 (upper) and 0.01 to 0.14 (lower), respectively. Conversely, gold displays weaker tail dependence, with Clayton lower-tail coefficients near zero for most energy ETF pairs. Time-varying copulas reveal that gold maintains negative average correlations (as low as -0.37) with traditional energy ETFs during crises, highlighting its role as a reliable hedge. These findings suggest that gold serves as a more effective hedging asset than gold ETFs, particularly in turbulent periods, and is especially suited for hedging traditional energy ETF exposures. The study contributes to the literature by integrating GARCH filtering with multiple copula types to capture dynamic, nonlinear, and asymmetric dependencies - a methodologically superior approach for assessing hedging effectiveness. It holds important implications for portfolio managers and risk-averse investors seeking to mitigate energy sector exposure through alternative asset allocations.

With the advancement of the microarray data, reduction in the data dimensions becomes a research hotspot. High-dimensional datasets need to be pre-processed using data reduction techniques. Features selection techniques are used to handle the dimensionality problem. Clustering techniques are also used to reduce the dimensions of data. It selects features highly correlated to the class labels, while less correlated among the features. In this paper, we proposed a new method called the Greedy Heuristic Fuzzy Clustering (GHFClust), which can be used in high dimensional datasets to improve the accuracy and reduce the high dimensionality problems. In this study, the minimum subset of features is selected using the greedy approach, in which interquartile range and relative covering analysis are used. For the remaining data, the fuzzy-c-means clustering technique is used. The results show that the GHFClust has a higher accuracy rate compared to the other methods using benchmark datasets.

Brent crude oil prices are volatile, nonlinear, and highly sensitive to market shocks, making challenges to forecast it accurately via standalone and conventional econometric forecasting models. Therefore, oil price prediction has gathered great attention from scholars and policymakers. Driven by this critical concern, this study proposes a decomposition-ensemble forecasting framework for daily Brent crude oil prices. Forecasting models are then applied to the reconstructed components and then the final prediction is obtained by integrating component-based forecasts. The proposed method is evaluated against the baseline models ARIMA, LSTM, and its variants using multiple error measures, directional forecast accuracy, and the Diebold-Mariano test. The empirical results show that the proposed method achieved lowest error values, including MSE = 0.74, MAPE = 0.75, MAE = 0.68 and SMAPE = 0.75 and the directional forecasting value is 80.22%. These findings indicate that the proposed method can reduce the irrelevant and redundant information and enhance the stability of Brent crude oil price forecasts.

Based on trade data from 2005 to 2020, this study investigates the driving forces behind China’s grain virtual water (VW) import trade, with a particular focus on the role of the Belt and Road Initiative (BRI). By incorporating economic distance (ED) and institutional distance (ID) into the gravity model framework and applying a high-dimensional fixed-effects Poisson pseudo-maximum likelihood estimation method, the study offers new empirical insights. The results indicate that ED is negatively associated with virtual water trade (VWT) in grains, while ID exhibits an inverted U-shaped relationship with VWT. Furthermore, the BRI significantly moderates the effects of ED and ID, weakening their influence on VWT. Additionally, the initiative demonstrates a clear trade creation effect, promoting increased VW imports. These findings contribute to a deeper understanding of the mechanisms shaping VWT and offer valuable policy guidance for enhancing international cooperation under the BRI framework.

This study introduces EGARCH-Copula models to show the conditional dependence structure for both average and extreme behavior. For this analysis, we used four rainfall series for station St. Pierre, St. Severin, St. Flavien and Scott and two streamflow series at station Bras D’Henri and Beaurivage. Both average and dependence structure has been captured through the best selected static and time varying copula models. The time varying SJC (Symmetrized Joe-Clayton) copulas performing best in all pairs of rainfall and runoff series. The Rainfall-Runoff time series have extreme upper and lower tail dependence which illustrating if there is an increase or decrease rainfall then streamflow is affected in same way. Further, positive correlation existed among all pairs of rainfall-runoff series as observed from both normal and student-t copula parameters. Which indicate that as rainfall increases (or decreases) streamflow also increases (or decreases).

Background Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate zones. Objectives We examined influences of medium-term residential exposure to fine particulate matter (PM 2.5 ), NO 2 , SO 2 , air temperature and precipitation on influenza incidence. Methods To obtain complete spatial coverage of Baden-Württemberg, we modeled environmental exposure from data of the Copernicus Atmosphere Monitoring Service and of the Copernicus Climate Change Service. We computed spatiotemporal aggregates to reflect quarterly mean values at post-code level. Moreover, we prepared health insurance data to yield influenza incidence between January 2010 and December 2018. We used generalized additive models, with Gaussian Markov random field smoothers for spatial input, whilst using or not using quarter as temporal input. Results In the 3.85 million cohort, 513,404 influenza cases occurred over the 9-year period, with 53.6% occurring in quarter 1 (January to March), and 10.2%, 9.4% and 26.8% in quarters 2, 3 and 4, respectively. Statistical modeling yielded highly significant effects of air temperature, precipitation, PM 2.5 and NO 2 . Computation of stressor-specific gains revealed up to 3499 infections per 100,000 AOK clients per year that are attributable to lowering ambient mean air temperature from 18.71 °C to 2.01 °C. Stressor specific gains were also substantial for fine particulate matter, yielding up to 502 attributable infections per 100,000 clients per year for an increase from 7.49 μg/m 3 to 15.98 μg/m 3 . Conclusions Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.

In this study, we examine the average and extreme dependence between Exchange Traded Funds ETFs (both energy & commodity) and WTI crude oil prices by using EGARCH-copula models. We use both static (Normal, Student-t, Gumbel and Clayton) and time-varying (Normal and SJC) copulas to explore both average and extreme dependence. Based on the Akaike information criterion (AIC), our results show that time-varying copulas outperform the static copulas. Further, we have found strong enough positive correlations of energy and commodity ETFs with oil prices to suggest that they could be used as a tool for managing oil price risk. Also, contrasting results of time-varying copulas with each other provide useful information regarding the hedge or safe-haven properties of energy and commodity ETFs.

Exchange rate plays a crucial part in the development of the country and it highlights the prosperity of the country's economy. This study takes various set of determinants, which affect the instability of rate of exchange in the country. This study investigated the relationship of interest rate, inflation, Forex, trade balance and inflow of net foreign capital with exchange rate. The study analyzed the data for the period 1972 to 2014 for Pakistan. Multi-level statistical estimation techniques, VECM, Johnson co-integration, impulse response, variance decomposition and granger causality are applied. The results demonstrated long relationship of interest rate, trade balance, foreign exchange reserve, net foreign capital inflow and exchange rate. The study also confirmed the positive effect of these variables on exchange rate. The study can be especially significant for the government, to make appropriate action to better deal with exchange rate volatility. Keywords: VECM, Granger Causality, Instability of Exchange Rate, Pakistan.