A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network

The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%

Tail risk, systemic risk and spillover risk of crude oil and precious metals

The relationship between oil prices and metal prices has been extensively investigated. However, the tail risk, systemic risk and spillover risk of oil prices have not been investigated via extreme value theory (EVT). We use this novel approach to determine the tail risk of oil, precious metals, how much risk they pose to the financial system and to what extent a shock in oil prices spill over to other precious metals as well as from the financial system. We use long time series of daily data from 1st January 1987 to 31st December 2021 as long time series is required for the EVT. The data is based on the total return index (RI) of four precious metals including gold, platinum, palladium and silver. Our results show that the tail risk of these metals is lower during the crisis period except the Covid-19 pandemic crisis. Most importantly, gold is a safer asset due to the lowest tail risk among four precious metals, indicating the claim that gold is a precious asset to mitigate the returns during market downturns and acts as a ‘safe haven’. Moreover, we also find that extreme systemic risk (tail-β) for crude oil and selected precious metals reduces during crisis period. This is also recognising the fact that these commodities act as a prospective asset for portfolio diversification to hedge against financial assets' volatility. Finally, the spillover risk among crude oil and selected precious metals varies over time, especially during the crisis period and crude oil is an important stimulator of the spillover risk for precious metals. By using our findings, financial market investors can improve their investment planning to attain the maximum advantage of portfolio diversification. Financial managers can further apply these results in forecasting to estimate future global oil market trends for improving their hedging skills and portfolio performance

DAWM: cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches

Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors

Climate change poses enormous ecological, socio-economic, health, and financial challenges. A novel extreme value theory is employed in this study to model the risk to environmental, social, and governance (ESG), healthcare, and financial sectors and assess their downside risk, extreme systemic risk, and extreme spillover risk. We use a rich set of global daily data of exchange-traded funds (ETFs) from 1 July 1999 to 30 June 2022 in the case of healthcare and financial sectors and from 1 July 2007 to 30 June 2022 in the case of ESG sector. We find that the financial sector is the riskiest when we consider the tail index, tail quantile, and tail expected shortfall. However, the ESG sector exhibits the highest tail risk in the extreme environment when we consider a shock in the form of an ETF drop of 25% or 50%. The ESG sector poses the highest extreme systemic risk when a shock comes from China. Finally, we find that ESG and healthcare sectors have lower extreme spillover risk (contagion risk) compared to the financial sector. Our study seeks to provide valuable insights for developing sustainable economic, business, and financial strategies. To achieve this, we conduct a comprehensive risk assessment of the ESG, healthcare, and financial sectors, employing an innovative approach to risk modelling in response to ecological challenges

Carbon neutrality:The role of banks in optimal environmental management strategies

This study explores the ecological ambitions of banks by studying the coincidence of economic realities with environmental management strategies. We address this question by studying the environmental performance of US banks and its impact on their tail risk as US is not committed to carbon neutrality in COP 21. We proxy economic reality with tail risk of banks and employ a novel extreme value theory to measure this. We use Asset4 ESG data for environmental performance score and test our hypothesis with a sample of 256 US banks. The results indicate that the US banks are ecologically ambitious and their environmental strategies are likely to reduce their tail risk. This provides evidence that better environmental strategies do coincide with the economic realities. We test the consistency of our results by using alternate proxies for tail risk and find our results robust. Our results are also not driven by endogeneity concerns. Finally, our additional results show that the nature of relationship differs with corporate governance levels, CSR committee existence, institutional ownership presence and crisis period

Spillover of energy commodities and inflation in G7 plus Chinese economies

We investigate the spillover trends of energy commodities and the Consumer Price Index (CPI) in the G-7 plus China by using the Continuous Wavelet Transform (CWT) methodology. We use data January 2016 to October 2022 and we divide our analysis into pre and post-Covid-19 periods to capture the effects of this major event. The CWT graphs demonstrate distinct levels of inflation across the countries under scrutiny, highlighting remarkable disparities in CPI and energy commodities in both the pre and post-Covid-19 eras, particularly for Canada, China, and the United States. Additionally, the Wavelet Transform Coherence (WTC) analysis reveals noteworthy relationships across all three energy commodities. These findings bear significant policy implications for macroeconomic goals and domestic policies such as monetary and fiscal measures. The variations noted in CPI and energy commodities before and after the Covid-19 era emphasize the need for policy discussions to address the implications for macroeconomic stability. Policymakers can leverage our study to gain a better understanding of the relationship between CPI and energy commodities, considering both internal and external macroeconomic conditions

Tail risk and systemic risk of finance and technology (FinTech) firms

Technology firms are increasingly moving to finance. They are able to make use of a large stock of user data and offer a range of services that otherwise were not possible. This move may pose fresh challenges to financial stability. This paper empirically evaluates the tail risk and systemic risk of technology firms. Our data sample consists of technology firms, and for comparison we also evaluate the tail risk and systemic risk of finance firms. We use daily equity returns data from 2 April 1992 to 31 December 2019 and we adopt the univariate extreme value theory (EVT) to determine equity tail risk. Our selection criteria is the market capitalisation and we choose the top twenty technology and the top twenty finance firms to evaluate tail risk and systemic risk. We found that the tail risk of technology firms is higher than the financial firms, whereas they are less likely to be in distress conditional upon a shock from the system. However, this finding for technology firms reverses when we use recent data via our six-year rolling estimates. We conclude that, similar to finance firms, there should be tighter regulations for technology firms since technology firms are riskier than the finance firms. Our paper has significant implications for both national and global financial regulators

Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors.

Climate change poses enormous ecological, socio-economic, health, and financial challenges. A novel extreme value theory is employed in this study to model the risk to environmental, social, and governance (ESG), healthcare, and financial sectors and assess their downside risk, extreme systemic risk, and extreme spillover risk. We use a rich set of global daily data of exchange-traded funds (ETFs) from 1 July 1999 to 30 June 2022 in the case of healthcare and financial sectors and from 1 July 2007 to 30 June 2022 in the case of ESG sector. We find that the financial sector is the riskiest when we consider the tail index, tail quantile, and tail expected shortfall. However, the ESG sector exhibits the highest tail risk in the extreme environment when we consider a shock in the form of an ETF drop of 25% or 50%. The ESG sector poses the highest extreme systemic risk when a shock comes from China. Finally, we find that ESG and healthcare sectors have lower extreme spillover risk (contagion risk) compared to the financial sector. Our study seeks to provide valuable insights for developing sustainable economic, business, and financial strategies. To achieve this, we conduct a comprehensive risk assessment of the ESG, healthcare, and financial sectors, employing an innovative approach to risk modelling in response to ecological challenges

The impact of carbon emissions on country risk: Evidence from the G7 economies

This paper empirically investigates the effect of carbon emissions on sovereign risk? To answer this question, we use fixed effects model by using annual data from G7 advanced economies, which includes Canada, France, Germany, Italy, Japan, UK and USA, for the period from 1996 to 2014. We employ a novel extreme value theory to measure sovereign risk. The results indicate that climate change (carbon emissions) are likely to increase sovereign risk significantly. We also expand our analysis to some specific sectors, as some of the sectors emit more carbon than others. Specifically, we take top three polluting sectors namely: transportation, electricity and industry and show that they are more likely to increase the sovereign risk. Our results are robust to change in risk measures, estimation in differences and dynamic version of econometric models. Therefore, we have robust consideration that the carbon emissions significantly explain the sovereign risk