"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.

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.

Enriched transcriptome analysis of laser capture microdissected populations of single cells to investigate intracellular heterogeneity in immunostained FFPE sections

To investigate intracellular heterogeneity, cell capture of particular cell populations followed by transcriptome analysis has been highly effective in freshly isolated tissues. However, this approach has been quite challenging in immunostained formalin-fixed paraffin-embedded (FFPE) sections. This study aimed at combining the standard pathology techniques, immunostaining and laser capture microdissection, with whole RNA-sequencing and bioinformatics analysis to characterize FFPE breast cancer cell populations with heterogeneous expression of progesterone receptor (PR). Immunocytochemical analysis revealed that 60% of MCF-7 cells admixture highly express PR. Immunocytochemistry-based targeted RNA-seq (ICC-RNAseq) and in silico functional analysis revealed that the PR-high cell population is associated with upregulation in transcripts implicated in immunomodulatory and inflammatory pathways (e.g. NF-κB and interferon signaling). In contrast, the PR-low cell population is associated with upregulation of genes involved in metabolism and mitochondrial processes as well as EGFR and MAPK signaling. These findings were cross-validated and confirmed in FACS-sorted PR high and PR-low MCF-7 cells and in MDA-MB-231 cells ectopically overexpressing PR. Significantly, ICC-RNAseq could be extended to analyze samples captured at specific spatio-temporal states to investigate gene expression profiles using diverse biomarkers. This would also facilitate our understanding of cell population-specific molecular events driving cancer and potentially other diseases

Detection of advanced persistent threat using machine-learning correlation analysis

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented sy

Network traffic analysis for threats detection in the Internet of Things

As the prevalence of the Internet of Things (IoT) continues to increase, cyber criminals are quick to exploit the security gaps that many devices are inherently designed with. Users cannot be expected to tackle this threat alone, and many current solutions available for network monitoring are simply not accessible or can be difficult to implement for the average user, which is a gap that needs to be addressed. This article presents an effective signature-based solution to monitor, analyze, and detect potentially malicious traffic for IoT ecosystems in the typical home network environment by utilizing passive network sniffing techniques and a cloud application to monitor anomalous activity. The proposed solution focuses on two attack and propagation vectors leveraged by the infamous Mirai botnet, namely DNS and Telnet. Experimental evaluation demonstrates the proposed solution can detect 98.35 percent of malicious DNS traffic and 99.33 percent of Telnet traffic for an overall detection accuracy of 98.84 percent

A Systematic Review of the Availability and Efficacy of Countermeasures to Internal Threats in Healthcare Critical Infrastructure

Insider attacks are becoming increasingly detrimental and frequent, affecting critical infrastructure at a massive scale. Recent attacks such as the UK National Health Service (NHS) WannaCry ransomware attack which partly depends on internal users for initial infection highlight the increasing role of the malicious insiders in cyber attack campaigns . The objective of this research is to ascertain the existing technological capability to mitigate insider threats within computer security systems by way of a mixed-method systematic review. Evidence was acquired from major sources of mainstream and grey literature by analysing about 300, 000 papers. Crude aggregated results were analysed across the literature, the results were TPR 0.75, FPR 0.32, σ 0.24 and 0.36 respectively, σ 2 0.06 and 0.13 respectively. In totality, the literature evidence suggests that there is high heterogeneity across crude data indicating that the effectiveness of security measures varies significantly. No solution is able to totally mitigate an insider threat. Themes when set against that data suggest that most, if not all, security measures require breaches to occur before an analysis of malicious activity can prevent it in future through recall. Such a reactive approach is not effective to protect our critical infrastructure including our healthcare systems. Consequently, there is a major theoretical shortfall in current cyber defence architecture

Energy prices and CO2 emission allowance prices : a quantile regression approach

We use a quantile regression framework to investigate the impact of changes in crude oil prices, natural gas prices, coal prices, and electricity prices on the distribution of the CO2 emission allowance prices in the United States. We find that: (i) an increase in the crude oil price generates a substantial drop in the carbon prices when the latter is very high; (ii) changes in the natural gas prices have a negative effect on the carbon prices when they are very low but have a positive effect when they are quite high; (iii) the impact of the changes in the electricity prices on the carbon prices can be positive in the right tail of the distribution; and (iv) the coal prices exert a negative effect on the carbon prices.COMPETE, QREN, FEDER, Fundação para a Ciência e a Tecnologia (FCT