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Originality/value—this model contributed to the vast literature on models of change and risk management within organisations, but was not validated empirically for reliability of the factors, and on financial services providers within small jurisdictions. Therefore, the significance and importance of such a study lies firstly on the premise that testing on small countries can be deemed as small laboratories for more complex politics, regulations and policies of larger countries and secondly, the importance of financial services as essential for prosperity in a country's economy. This model will provide an empirically tested proactive model in a specific environment for managing organisational risks to arrive at their objectives with minimal setbacks. ; peer-reviewed

This paper applied mathematical models to conduct an in-depth discussion and empirical analysis of financial market risk management. The daily rate of return data on the S&P 500 index, selected through data processing, included data cleaning, return calculation, data standardization, construction of the GARCH (1, 1) model, and a Copula model for predicting and analyzing risks. Empirical results indicate that the GARCH model has good simulation in capturing the market volatility change, while the Copula model holds clear advantages in modeling multivariate risk dependencies. In the modern economy, managing risks in the financial market plays a vital role. Optimization algorithms such as genetic algorithms and Bayesian optimization significantly improve the prediction accuracy and computational efficiency of the model. Compared with traditional historical simulation methods, these models perform better in risk prediction indicators (VaR and ES), proving their practicality and effectiveness in actual risk management. The research results verify that advanced mathematical models and optimization methods have important application value in financial market risk management, providing a scientific decision-making basis for investors and risk managers.

Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be

This article presents a mathematical model for measuring the global risk of nuclear theft & terrorism. One plausible set of parameter values used in a numerical example suggests a 29 percent probability of a nuclear terrorist attack in the next decade. The expected loss over that period would be $1.17 trillion (undiscounted), or more than $100 billion per year. Historical & other evidence is used to explore the likely values of several of the key parameters, & policy options for reducing the risk are briefly assessed. The uncertainties in estimating the risk of nuclear terrorism are very large, but even a risk dramatically smaller than that estimated in the numerical example used in this article would justify a broad range of actions to reduce the threat. Figures, References. [Reprinted by permission of Sage Publications Inc., copyright 2006 The American Academy of Political and Social Science.]

This textbook, now in its fourth edition, serves as a comprehensive guide to learning various aspects of risk, encompassing supply chain management, artificial intelligence, and sustainability. It demonstrates a wide range of operations research models that have been successfully applied to enterprise supply chain risk management. Each chapter of the book can function as a standalone module focusing on a specific topic, offering dedicated examples, definitions, and discussion notes. The publication of this book comes at a crucial time when the world is facing increasing challenges from various forms of risk. Events such as Covid-19, the energy crisis, wars, and terrorism in the 21st century have all disrupted supply chains, thus highlighting the critical importance of enterprise risk management. Additional risks, such as financial and technological bubbles, along with concerns surrounding rampant artificial intelligence, contribute to a climate that demands enhanced risk management within organizations.

The mathematical methods of waste management assessment and software for various life cycle applications are widely used as one of the decision-making support tools in waste policy. This comparatively new approach to waste management planning offers an option for investigating the life cycle impact of solid waste management processes which generate anthropogenic impact on the environment, of which the most unfavorable is greenhouse gas emission. In the research a software application called WAMPS (waste management planning system) developed by the IVL Swedish Environmental Research Institute within the Reco Baltic 21 Tech project has been used to create a better understanding about waste management processes and their produced impact on climate change. It is the first time when WAMPS software is applied for regional domestic waste management planning for the next seven-year period in the Baltic states, in this particular case for the Piejura region, which is one of the ten waste management planning regions in Latvia. The Piejura region includes one city of national level and nine districts, with the total area of about 5,300 sq. km and the number of inhabitants 153,899. In accordance with national legislation, responsibility for waste management organization has been delegated to local municipalities. In this study the solutions for elaboration of the Piejura region waste management strategy are based on those waste management processes which will produce the best environmental options. The main results of the research were obtained comparing various waste management scenarios in WAMPS: the existent situation in regional waste management, where more than 94% of domestic waste is landfilled; European Union framework demands according to Waste and Landfill directives; and the best technological solutions for the Piejura region based on local circumstances. The next phase of research will draw attention to the implementation and integration of the chosen technological solutions on the basis of economical solutions.

This book describes a system of mathematical models and methods that can be used to analyze real economic and managerial decisions and to improve their effectiveness. Application areas include: management of development and operation budgets, assessment and management of economic systems using an energy entropy approach, equation of exchange rates and forecasting foreign exchange operations, evaluation of innovative projects, monitoring of governmental programs, risk management of investment processes, decisions on the allocation of resources, and identification of competitive industrial clus.

This article presents a mathematical model for measuring the global risk of nuclear theft and terrorism. One plausible set of parameter values used in a numerical example suggests a 29 percent probability of a nuclear terrorist attack in the next decade. The expected loss over that period would be $1.17 trillion (undiscounted), or more than $100 billion per year. Historical and other evidence is used to explore the likely values of several of the key parameters, and policy options for reducing the risk are briefly assessed. The uncertainties in estimating the risk of nuclear terrorism are very large, but even a risk dramatically smaller than that estimated in the numerical example used in this article would justify a broad range of actions to reduce the threat.