Bayesian Risk Management

Author: Matt Sekerke
Publisher: John Wiley & Sons
ISBN: 9781118708606
Release Date: 2015-09-15
Genre: Business & Economics

A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model–driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning–based methods, the framework presented here allows you to measure risk in a fully–Bayesian setting without losing the structure afforded by parametric risk and asset–pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state–space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset–pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision–making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.

Financial Risk Management with Bayesian Estimation of GARCH Models

Author: David Ardia
Publisher: Springer Science & Business Media
ISBN: 3540786570
Release Date: 2008-05-08
Genre: Business & Economics

This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.

Risk Assessment and Decision Analysis with Bayesian Networks Second Edition

Author: Norman Fenton
Publisher: CRC Press
ISBN: 9781351978965
Release Date: 2018-08-08
Genre: Mathematics

Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary mathematics, probability, and statistics as needed Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.

Risk Management

Author: Terje Aven
Publisher: Springer Science & Business Media
ISBN: 9781846286537
Release Date: 2007-04-26
Genre: Technology & Engineering

This book presents a risk management framework designed to achieve better decisions and more desirable outcomes. It presents an in-depth discussion of some fundamental principles of risk management related to the use of expected values, uncertainty handling, and risk acceptance criteria. Several examples from the offshore petroleum industry are included to illustrate the use of the framework, but it can also be applied in other areas.

Environmental Risk Assessment and Management from a Landscape Perspective

Author: Lawrence A. Kapustka
Publisher: John Wiley & Sons
ISBN: 0470593016
Release Date: 2010-07-15
Genre: Technology & Engineering

An important guide to assessing and managing the environment from a landscape perspective Ecological relationships are nested within the landscape. Identifying the relevant spatial and temporal scales is critical for an effective understanding of ecological functions that human societies depend upon. Moreover, human encroachment into natural areas, or changes in climate, can alter spatial relationships, which in turn can negatively affect vital plant and wildlife patterns—and weaken economic structures needed to sustain human societies. This book is the first to combine multiple disciplines into one cohesive strategy to study these crucial connections, and looks toward building a social paradigm that embraces the dynamics of ecological systems. This book: Integrates landscape ecology, environmental risk assessment, valuation of ecological goods and services, and environmental management decision processes into one single source Includes chapters on quantitative measures, Bayesian modeling,¿economic analysis, and sustainable landscapes Covers marine, forest, agricultural, and pharmaceutical risk assessment Has a chapter on predicting climate change risk to ecosystems Has a companion ftp site with color graphics, animations, and risk assessment tools With material that is accessible across all knowledge levels, Environmental Risk Assessment and Management from a Landscape Perspective moves beyond looking solely at chemical contaminants to diagnose environmental threats, and aims to accomplish practical risk assessment in a manner that supports long-term sustainable management.

Handbook of Integrated Risk Management in Global Supply Chains

Author: Panos Kouvelis
Publisher: John Wiley & Sons
ISBN: 9781118115794
Release Date: 2011-10-26
Genre: Business & Economics

A comprehensive, one-stop reference for cutting-edge research in integrated risk management, modern applications, and best practices In the field of business, the ever-growing dependency on global supply chains has created new challenges that traditional risk management must be equipped to handle. Handbook of Integrated Risk Management in Global Supply Chains uses a multi-disciplinary approach to present an effective way to manage complex, diverse, and interconnected global supply chain risks. Contributions from leading academics and researchers provide an action-based framework that captures real issues, implementation challenges, and concepts emerging from industry studies.The handbook is divided into five parts: Foundations and Overview introduces risk management and discusses the impact of supply chain disruptions on corporate performance Integrated Risk Management: Operations and Finance Interface explores the joint use of operational and financial hedging of commodity price uncertainties Supply Chain Finance discusses financing alternatives and the role of financial services in procurement contracts; inventory management and capital structure; and bank financing of inventories Operational Risk Management Strategies outlines supply risks and challenges in decentralized supply chains, such as competition and misalignment of incentives between buyers and suppliers Industrial Applications presents examples and case studies that showcase the discussed methodologies Each topic's presentation includes an introduction, key theories, formulas, and applications. Discussions conclude with a summary of the main concepts, a real-world example, and professional insights into common challenges and best practices. Handbook of Integrated Risk Management in Global Supply Chains is an essential reference for academics and practitioners in the areas of supply chain management, global logistics, management science, and industrial engineering who gather, analyze, and draw results from data. The handbook is also a suitable supplement for operations research, risk management, and financial engineering courses at the upper-undergraduate and graduate levels.

Essays on Risk Management of Financial Market with Bayesian Estimation

Author: Zhang, Xi
ISBN: OCLC:1032270325
Release Date: 2017
Genre: Bayesian statistical decision theory

This dissertation consists of three essays on modeling financial risk under Bayesian framework. The first essay compares the performances of Maximum Likelihood Estimation (MLE), Probability-Weighted Moments (PWM), Maximum Product of Spacings (MPS) and Bayesian estimation by using the Monte Carlo Experiments on simulated data from GEV distribution. I compare not only how close the estimates are to the true parameters, but also how close the combination of the three parameters in terms of estimated Value-at-Risk (VaR) to the true VaR. The Block Maxima Method based on student-t distribution is used for analysis to mimic the real world situation. The Monte Carlo Experiments show that the Bayesian estimation provides the smallest standard deviations of estimates for all cases. VaR estimates of the MLE and the PWM are closer to the true VaR, but we need to choose the initial values carefully for MLE. MPS gives the worst approximation in general. The second essay analyzes the movement of implied volatility surface from 2005 to 2014. The study period is divided into four sub-periods: Pre-Crisis, Crisis, Adjustment period and Post-Crisis. The Black-Scholes model based daily implied volatility (IV) is constructed and the time series of IV given different moneyness and time to maturity is fitted into a stochastic differential equation with mean-reverting drift and constant elasticity of variance. After estimating the parameters using a Bayesian Metropolis Hastings algorithm, the comparison across different time periods is conducted. As it is natural to expect abnormality in Crisis and Adjustment period, it is interesting to see the difference between Post-Crisis movement and the Pre-Crisis's. The results reveal that if the catastrophe does not permanently change the investment behavior, the effect from Crisis may last longer than expected. It is unwise to assume the market movement or investment behavior would be identical in Pre-Crisis and Post-Crisis periods. Market participants learn from Crisis and behave differently in Post-Crisis comparing to Pre-Crisis. The third essay attempts to predict financial stress by identifying leading indicators under a Bayesian variable selection framework. Stochastic search variable selection (SSVS) formulation of George and McCulloch (1993) is used to select more informative variables as leading indicators among a number of financial variables. Both linear model and Probit model under normal error assumption and fat tail assumption are used for analysis. Financial stress indexes issued by Federal Reserve Banks combined with Bloom(2009) and Ng(2015)'s paper are used to identify financial stress. An ex-post approach based on historical perspective and ex ante approach combined with rolling window are used for analysis. The results show promising predictive power and the selection of variables can be used to signal financial crisis period.

Proceedings of the Eighth International Conference on Management Science and Engineering Management

Author: Jiuping Xu
Publisher: Springer
ISBN: 9783642551222
Release Date: 2014-05-06
Genre: Business & Economics

This is the Proceedings of the Eighth International Conference on Management Science and Engineering Management (ICMSEM) held from July 25 to 27, 2014 at Universidade Nova de Lisboa, Lisbon, Portugal and organized by International Society of Management Science and Engineering Management (ISMSEM), Sichuan University (Chengdu, China) and Universidade Nova de Lisboa (Lisbon, Portugal). The goals of the conference are to foster international research collaborations in Management Science and Engineering Management as well as to provide a forum to present current findings. A total number of 138 papers from 14 countries are selected for the proceedings by the conference scientific committee through rigorous referee review. The selected papers in the second volume are focused on Computing and Engineering Management covering areas of Computing Methodology, Project Management, Industrial Engineering and Information Technology.

Bayesian Methods in Finance

Author: Svetlozar T. Rachev
Publisher: John Wiley & Sons
ISBN: 0470249242
Release Date: 2008-05-02
Genre: Business & Economics

Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.

Modelling Operational Risk Using Bayesian Inference

Author: Pavel V. Shevchenko
Publisher: Springer Science & Business Media
ISBN: 3642159230
Release Date: 2011-01-19
Genre: Business & Economics

The management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate. This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks. This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk.

Flood Risk Management Research and Practice

Author: Paul Samuels
Publisher: CRC Press
ISBN: 0203883020
Release Date: 2008-10-01
Genre: Science

Floods cause distress and damage wherever and whenever they happen. Flooding from rivers, estuaries and the sea threatens many millions of people worldwide and economic and insurance losses from flooding have increased significantly since 1990. Across the European Union, flood management policy is changing in response to the EU Directive on the assessment and management of flood risks, which requires a move from flood protection and defence to comprehensive flood risk management. Flood Risk Management: Research and Practice includes about 200 contributions from the international conference FLOODrisk 2008 (Oxford, UK, 30 September – 2 October 2008). FLOODrisk 2008 was an initiative of the FLOODsite research project on Integrated Flood Risk Analysis and Management Methodologies. FLOODsite was a major “Integrated Project” in the European Commission Sixth Framework Programme; contract number GOCE-CT-2004-505420. The conference provided a forum for leading researchers, flood risk managers, policy makers and practitioners from government, commercial and research organisations to gain an overview of advances in this important subject. Flood risk management practice crosses several professions and disciplines and these are represented in the breadth of the scope of the conference and these proceedings. The conference covered all aspects of flood risk: the causes of floods, their impacts on people, property and the environment, and portfolios of risk management measuresm, while the principal themes included: climate change, estimation of extremes, flash floods, flood forecasting and warning, inundation modelling, systems analysis, uncertainty, international programmes, flood defence infrastructure and assets, environmental impacts, human and social impacts, vulnerability and resilience, risk sharing, equity and social justice, and, civil contingency planning and emergency management. Flood Risk Management: Research and Practice will be of interest to an international readership, ranging from authorities, consultants and engineers involved in flood management; researchers, post graduate lecturers and students, to policy makers, particularly at national level.

Risk Assessment and Decision Making in Business and Industry

Author: Glenn Koller
Publisher: CRC Press
ISBN: 0849302684
Release Date: 1999-03-01
Genre: Mathematics

Risk Assessment and Decision Making in Business and Industry: A Practical Guide presents an accessible treatment of the procedures and technologies involved in designing and building risk-assessment processes and models. Areas examined include: brokerage-house portfolio management legal decision making construction oil/gas exploration environmental assessments engineering marketing government manufacturing The entire volume is presented as a narrative, keeping statistical jargon to a minimum and explaining all concepts, techniques, and processes in a straightforward manner. The author emphasizes that the technical aspects of a risk-assessment and decision-making effort are secondary to the cultural, organizational, and interpersonal facets of establishing a framework. "Practical" is the operative term throughout the text. Risk Assessment and Decision Making in Business and Industry: A Practical Guide enables readers who are not risk experts to effect an easy execution of the risk model building effort.

Environmental and Health Risk Assessment and Management

Author: Paolo Ricci
Publisher: Springer Science & Business Media
ISBN: 1402037759
Release Date: 2005-11-07
Genre: Medical

This book is about the legal, economical, and practical assessment and management of risky activities arising from routine, catastrophic environmental and occupational exposures to hazardous agents. It includes a discussion of aspects of US and European Union law concerning risky activities, and then develops the economic analyses that are relevant to implementing choices within a supply and demand framework. The book also discusses exposure-response and time-series models used in assessing air and water pollution, as well as probabilistic cancer models, including toxicological compartmental, pharmaco-kinetic models and epidemiological relative risks and odds ratios-based models. Statistical methods to measure agreement, correlation and discordance are also developed. The methods and criteria of decision-analysis, including several measures of value of information (VOI) conclude the expositions. This book is an excellent text for students studying risk assessment and management.

Coherent Stress Testing

Author: Riccardo Rebonato
Publisher: John Wiley & Sons
ISBN: 9780470971482
Release Date: 2010-06-10
Genre: Business & Economics

In Coherent Stress Testing: A Bayesian Approach, industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit. Based on the author's extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks. It constitutes a radical departure from the traditional statistical methodologies based on Economic Capital or Extreme-Value-Theory approaches. The book is split into four parts. Part I looks at stress testing and at its role in modern risk management. It discusses the distinctions between risk and uncertainty, the different types of probability that are used in risk management today and for which tasks they are best used. Stress testing is positioned as a bridge between the statistical areas where VaR can be effective and the domain of total Keynesian uncertainty. Part II lays down the quantitative foundations for the concepts described in the rest of the book. Part III takes readers through the application of the tools discussed in part II, and introduces two different systematic approaches to obtaining a coherent stress testing output that can satisfy the needs of industry users and regulators. In part IV the author addresses more practical questions such as embedding the suggestions of the book into a viable governance structure.