Do Oil-Rich GCC Countries Finance US Current Account Deficit?

Given the secrecy that wraps the flows of the GCC countries petrodollar surpluses to the United States and the pressures on these countries to spend and recycle more, this study attempts to uncover the direct and reverse causal relationships between the GCC financial accounts and the US current account deficit. It examines whether the GCC petrodollar surpluses are a global savings glut (an external factor) that causes the US current account deficit or in contrary this deficit is home-grown and the petrodollar savings glut hypothesis does not hold. It particularly focuses on worlds largest oil exporter to find out if the homegrown deficit hypothesis for the worlds largest oil consumer holds. It also investigates which types of investments or components of GCC financial accounts help cause the US deficit the most. The implications and policy recommendations for this growing source of global external imbalances are also provided. --Capital account,Financial account,Direct and reverse causality

Risk management of precious metals

This paper examines volatility and correlation dynamics in price returns of gold, silver, platinum and palladium, and explores the corresponding risk management implications for market risk and hedging. Value-at-Risk (VaR) is used to analyze the downside market risk associated with investments in precious metals, and to design optimal risk management strategies. We compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi-parametric Filtered Historical Simulation approach. Different risk management strategies are suggested, and the best approach for estimating VaR based on conditional and unconditional statistical tests is documented. The economic importance of the results is highlighted by assessing the daily capital charges from the estimated VaRs. The risk-minimizing portfolio weights and dynamic hedge ratios between different metal groups are also analyzed.risk management;value-at-risk;conditional volatility;precious metals

Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies

This paper examines the inclusion of the dollar/euro exchange rate together with four important and highly traded commodities - aluminum, copper, gold and oil- in symmetric and asymmetric multivariate GARCH and DCC models. The inclusion of exchange rate increases the significant direct and indirect past shock and volatility effects on future volatility between the commodities in all the models. Model 2, which includes the business cycle industrial metal copper and not aluminum, displays more direct and indirect transmissions than does Model 3, which replaces the business cycle-sensitive copper with the highly energy-intensive aluminum. The asymmetric effects are the greatest in Model 3 because of the high interactions between oil and aluminum. Optimal portfolios should have more euro currency than commodities, and more copper and gold than oil.hedging;volatility;shocks;MGARCH;transmission;asymmetries

Risk Management of Precious Metals

This paper examines volatility and correlation dynamics in price returns of gold, silver, platinum and palladium, and explores the corresponding risk management implications for market risk and hedging. Value-at-Risk (VaR) is used to analyze the downside market risk associated with investments in precious metals, and to design optimal risk management strategies. We compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi-parametric Filtered Historical Simulation approach. Different risk management strategies are suggested, and the best approach for estimating VaR based on conditional and unconditional statistical tests is documented. The economic importance of the results is highlighted by assessing the daily capital charges from the estimated VaRs. The risk-minimizing portfolio weights and dynamic hedge ratios between different metal groups are also analyzed.Precious metals; conditional volatility; risk management; value-at-risk

"Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies"

This paper examines the inclusion of the dollar/euro exchange rate together with four important and highly traded commodities - aluminum, copper, gold and oil- in symmetric and asymmetric multivariate GARCH and DCC models. The inclusion of exchange rate increases the significant direct and indirect past shock and volatility effects on future volatility between the commodities in all the models. Model 2, which includes the business cycle industrial metal copper and not aluminum, displays more direct and indirect transmissions than does Model 3, which replaces the business cycle-sensitive copper with the highly energy-intensive aluminum. The asymmetric effects are the greatest in Model 3 because of the high interactions between oil and aluminum. Optimal portfolios should have more euro currency than commodities, and more copper and gold than oil.

"Exchange Rate and Industrial Commodity Volatility Transmissions and Hedging Strategies"

This paper examines the inclusion of the dollar/euro exchange rate together with important commodities in two different BEKK, or multivariate conditional covariance, models. Such inclusion increases the significant direct and indirect past shock and volatility effects on future volatility between the commodities, as compared with their effects in the all-commodity basic model (Model 1), which includes the highly-traded aluminum, copper, gold and oil. Model 2, which includes copper, gold, oil and exchange rate, displays more direct and indirect transmission than does Model 3, which replaces the business cycle-sensitive copper with the highly energy-intensive aluminum. Optimal portfolios should have more Euro than commodities, and more copper and gold than oil. The multivariate conditional volatility models reveal greater volatility spillovers than their univariate counterparts.

Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies

This paper examines the inclusion of the dollar/euro exchange rate together with four important and highly traded commodities - aluminum, copper, gold and oil- in symmetric and asymmetric multivariate GARCH and DCC models. The inclusion of exchange rate increases the significant direct and indirect past shock and volatility effects on future volatility between the commodities in all the models. Model 2, which includes the business cycle industrial metal copper and not aluminum, displays more direct and indirect transmissions than does Model 3, which replaces the business cycle-sensitive copper with the highly energy-intensive aluminum. The asymmetric effects are the greatest in Model 3 because of the high interactions between oil and aluminum. Optimal portfolios should have more euro currency than commodities, and more copper and gold than oil.

Shock and volatility spillovers among equity sectors of the Gulf Arab stock markets

Upon examining own volatility dependency for the three major sectors, namely Service, Industrial and Banking, in four GCC economies (Kuwait, Qatar, Saudi Arabia and UAE), the empirical findings suggest that Banking seems to be the least sensitive among the sectors to past own volatility, while Industrial is the most volatile to the onset of past shocks or news. Sector volatility spillovers show that Saudi Arabia has the least inter-sector spillovers, while tiny Qatar has the most. Saudi Arabia seems to be the most sensitive to geopolitics, while Kuwait is the least affected. The constant conditional correlations between the three sectors for all four GCC markets echo different economic advantages and varying roles in the economy. We also provide two examples using the estimates of the GCC equity sector markets for portfolio designs and hedging strategies.

Semi-Automated Image Analysis Methodology to Investigate Intracellular Heterogeneity in Immunohistochemical Stained Sections

The discovery of tissue heterogeneity revolutionized the existing knowledge regarding the cellular, molecular, and pathophysiological mechanisms in biomedicine. Therefore, basic science investigations were redirected to encompass observation at the classical and quantum biology levels. Various approaches have been developed to investigate and capture tissue heterogeneity; however, these approaches are costly and incompatible with all types of samples. In this paper, we propose an approach to quantify heterogeneous cellular populations through combining histology and images processing techniques. In this approach, images of immunohistochemically stained sections are processed through color binning of DAB-stained cells (in brown) and non-stained cells (in blue) to select cellular clusters expressing biomarkers of interest. Subsequently, the images were converted to a binary format through threshold modification (threshold 60%) in the grey scale. The cell count was extrapolated from the binary images using the particle analysis tool in ImageJ. This approach was applied to quantify the level of progesterone receptor expression levels in a breast cancer cell line sample. The results of the proposed approach were found to closely reflect those of manual counting. Through this approach, quantitative measures can be added to qualitative observation of subcellular targets expression