Page 1
Page 1
New Frontiers in Enterprise Risk Management
This book provides introductory material about enterprise risk management, and the role of risk in decision making. It presents enterprise risk management from perspectives of finance, accounting, insurance, supply chain operations, and project management. Technology tools are addressed, to include financial models of risk as well as accounting aspects using data envelopment analysis, neural network tools for credit risk evaluation, and real option analysis applied to information technology outsourcing. Three chapters present enterprise risk management experience in China, to include banking, chemical plant operations, and information technology.
Investing amid low expected returns : Making the most when markets offer the least
Investing Amid Low Expected Returns: Making the Most When Markets Offer the Least provides an evidence-based blueprint for successful investing when decades of market tailwinds are turning into headwinds.
Fundamentals of operational risk management : Understanding and implementing effective tools, policies and frameworks
Threats to an organization's operations, such as fraud, IT disruption or poorly designed products, could result in serious losses. Understand the key components of effective operational risk management with this essential book for risk professionals and students. Outlines how to implement a sound operational risk management framework which is embedded in day-to-day business activities. It covers the main operational risk tools including categorisation, risk and control self-assessment and scenario analysis, and explores the importance of risk appetite and tolerance.
Financial risk management with bayesian estimation of GARCH models : Theory and applications
This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of these models from a Bayesian viewpoint is relatively recent and can be considered very promising due to the advantages of the Bayesian approach, in particular the possibility of obtaining small-sample results and integrating these results in a formal decision model. The first two chapters introduce the work and give an overview of the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal innovations and the linear regression models with conditionally Normal and Student-t-GJR errors. The sixth chapter shows how agents facing different risk perspectives can select their optimal Value at Risk Bayesian point estimate and documents that the differences between individuals can be substantial in terms of regulatory capital.
Enterprise Risk Management Models
Offers a comprehensive guide to several aspects of risk, including information systems, disaster management, supply chain and disaster management perspectives. A major portion of the book is devoted to presenting a number of operations research models that have been (or could be) applied to enterprise supply risk management, especially from the supply chain perspective.
Digitalization and the Future of Financial Services : Innovation and Impact of Digital Finance
The aim of this book is to extend our understandings on how digitalization and the future of financial services can be helpful in different business circumstances in many cross-functional financial areas, such as financial markets, financial risk management, financial technologies, investment finance, etc. Thus, the book aims at addressing the relevance of digital finance for different players, highlighting differences in tools and processes as well as identifying innovative practices in financial digitalization.
Machine learning for risk calculations : A practitioner's view
Fundamental Approximation Methods. Machine Learning -- Deep Neural Nets -- Chebyshev Tensors -- The toolkit - plugging in approximation methods. Introduction: why is a toolkit needed -- Composition techniques -- Tensors in TT format and Tensor Extension Algorithms -- Sliding Technique -- The Jacobian projection technique -- Hybrid solutions - approximation methods and the toolkit.
Bio-inspired credit risk analysis : Computational intelligence with support vector machines
Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.
