Study on risk analysis of supply chain enterprises

The sources of supply chain enterprise risk from different aspects including material flow, information flow, cash flow and partner relationship is analyzed. Measures for risk reduction have also been summarized from the aspects of risk sharing, information sharing, change of inventory control mode, and supply chain flexibility. Finally, problems in current research on supply chain risk management are pointed out and a discussion on future research trend is presented.

On the optimal dynamic hedging with nonferrous metals

This paper employs multivariate GARCH to model conditional correlations and to examine volatility spillovers and hedging possibilities with nonferrous metals traded on the London Metal Exchange(LME)market.Three different multivariate GARCH models(diagonal,CCC and DCC)are employed and contrasted.The nonferrous metals studied are copper,aluminum,tin,lead,zinc and nickel and span the period from January 6,2000 to February 29,2016.The multivariate DCC GARCH framework is found to fit the data in an appropriate design and provides results showing the strongest evidence of long-term persistence volatility spillovers between lead and aluminum.We also find that the Hurst exponents given by the R/S method are on average 0.94,indicating the existence of a strong degree of long-range dependence in conditional volatilities.On average,the cheapest hedge is a long position in lead and a short position in nickel.The most expensive hedge is long nickel and short copper.

中国企业海外并购失败了吗？

本文运用市场模型、FF3FM模型和事件研究的基本方法评估1994—2009年中国157个企业海外并购事件的短期和中长期绩效。结果显示,尽管外界对海外并购绩效看法各异,中国企业海外并购事件公告日的市场绩效明显为正,反映了市场对中国企业海外并购的正面评价。从中长期的角度上看,中国企业海外并购整体上取得了非负的超常回报率(Abnormal return),体现了政府"走出去"战略的胜利开局。考虑到行业绩效差异,本文运用Fix-to-fix控制组的方法对并购中长期绩效的决定性因素进行了多元回归分析,结果显示海外并购受益于人民币升值,国有企业的并购绩效明显差于民营企业,中国海外上市公司的绩效优于内地上市的公司。

Spatio-temporal behaviours of tropical cyclones over the bay of Bengal Basin in last five decades

Present research is an endeavour to scrutinise the spatio-temporal climatic characteristics of tropical cyclones(TCs)bustle in the Bay of Bengal basin,found in RSMC-IMD data all through 1971–2020.A large number of TCs,i.e.121 with a decadal average of 35.2 TCs has been examined for the last 50 years where depression(D)and deep depression(DD)have not been taken into account as these are less violent in nature.During the study periods,inter-annual and inter-decadal variation in cyclogenesis,landfall,length,speed,track shape and sinuosity,energy metrics and damage profile have been perceived.The study is clearly showing TCs took the northward track during the pre-monsoon season and made their landfall across the coasts of Bangladesh and Myanmar,while post-monsoon TCs made their landfall directly on the coasts of Orissa and West Bengal.In the post-monsoon phase,VF,ACE and PDI are significantly higher than in the monsoon season in the case of TCs and higher in the pre-monsoon season than in the monsoon season in the case of TCs comparing the energy metrics in different seasons.TC activity is comparatively pronounced during La Niña and El Niño regimes respectively and the genesis position in the BoB is moves to the east(west)of 87°E.During the cold regime,the number of extreme TC above the VSCS category,increased intensely.It is believed that the research findings will help stakeholders of the nation to take accurate strides to combat such violent events with persistent intensification.

Mapping the multi-hazards risk index for coastal block of Sundarban,India using AHP and machine learning algorithms

Global climate change,climate extremes,and overuse of natural resources are all major contributors to the risk brought on by cyclones.In I West Bengal state of India,the Pathar Pratima Block frequently experiences a variety of risks that result in significant loss of life and livelihood.In order to govern coastal society,it is crucial to measure and map the multi-hazards risk status.To depict the multi-hazards vulnerability and risk status,no cutting-edge models are currently being applied.Predicting distinct physical vulnerabilities is possible using a variety of cutting-edge machine learning techniques.This study set out to precisely describe multi-hazard risk using powerful machine learning methods.This study involved the use of Analytic Hierarchical Analysis and two cutting-edge machine-learning algorithms-Random Forest and Artificial Neural Network,which are yet underutilized in this area.The multi-hazards risk was determined by taking into account six criteria.The southern and eastern regions of the research area are clearly identified by the multi-hazards risk maps as having high to extremely high hazards risk levels.Cyclonic hazards and embankment breaching are the main dominant factors among the multi-hazards.The machine learning approach is the most accurate model for mapping the multi-hazards risk where the ROC result of Random forest and artificial neural network is more than the conventional method AHP.Here RF is the most validated model than the other two.The effectiveness,root mean square error,true skill statistics,Friedman and Wilcoxon rank test,and area under the curve of receiver operating characteristic tests were used to evaluate the prediction capacity of newly constructed models.The RMSE values of 0.24 and 0.26,TSS values of 0.82 and 0.73,and AUC values of 88.20%and 89.10%as produced by RF and ANN models,respectively,were all excellent.