Catering and Events Solutions
A guide to the growing importance of extreme value risk theory, methods, and applications in the financial sector
Presenting a uniquely accessible guide, Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications features a combination of the theory, methods, and applications of extreme value theory (EVT) in finance and a practical understanding of market behavior including both ordinary and extraordinary conditions.
Beginning with a fascinating history of EVTs and financial modeling, the handbook introduces the historical implications that resulted in the applications and then clearly examines the fundamental results of EVT in finance. After dealing with these theoretical results, the handbook focuses on the EVT methods critical for data analysis. Finally, the handbook features the practical applications and techniques and how these can be implemented in financial markets. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications includes:
• Over 40 contributions from international experts in the areas of finance, statistics, economics, business, insurance, and risk management
• Topical discussions on univariate and multivariate case extremes as well as regulation in financial markets
• Extensive references in order to provide readers with resources for further study
• Discussions on using R packages to compute the value of risk and related quantities
The book is a valuable reference for practitioners in financial markets such as financial institutions, investment funds, and corporate treasuries, financial engineers, quantitative analysts, regulators, risk managers, large-scale consultancy groups, and insurers. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications is also a useful textbook for postgraduate courses on the methodology of EVTs in finance.
FranÃ§ois Longin, PhD, is Professor in the Department of Finance at ESSEC Business School, France. He has been working on the applications of extreme value theory to financial markets for many years, and his research has been applied by financial institutions in the risk management area including market, credit, and operational risks. His research works can be found in scientific journals such as The Journal of Finance. Dr. Longin is currently a financial consultant with expertise covering risk management for financial institutions and portfolio management for asset management firms.
Corporate Finance by Booth, Cleary, & Drake is designed for a corporate finance course that focuses on decision making for a business enterprise.
Authors Booth, Cleary, and Drake introduce students to the fundamental concepts in corporate finance through a step-by-step approach to working problems using extensive spreadsheet and calculator assistance. The authors also provide in-depth coverage at challenging topics in finance including derivatives and leasing.
As part of a robust digital and print program, Corporate Finance includesWileyPLUS, a student-centered learning and assessment environment where students work through practice questions and assigned end-of-chapter problems, video assessments, and interactive animations alongside an embedded e-Textbook. Access to WileyPLUS is sold separately.
Given world news on topics like the financial crisis, the need for in depth knowledge for tomorrow's leaders in finance, and the importance of ethical decision making in financial management,Corporate Finance is tailored for tomorrow's professional.
The book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally.
This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties.
It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.
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