Hidden Markov Models in Finance (International Series in Operations Research & Management Science) Review

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(More customer reviews)Hidden Markov Models have come into vogue in recent years in various fields. Notably automatic speech recognition. An HMM is useful in a Bayesian context, where you have to work back from some observations to discern an underlying probability model that is supposedly generating those observations. Often in the presence of noise. Well, it turns out that this general description can also be applied to financial models, which is the book's subject.
Various specific models are tackled. Including the seminal Black-Scholes, where the security market is modelled as a Markov modulated Brownian. Typically, the maths in the book uses sophisticated probabilistic analysis and often assuming Markov processes. As an aside, if your field is electrical engineering or information theory, where you might have used Markov processes, then your background should suffice if you want to migrate to finance. It's not that different, at a certain conceptual level.
The book could be improved by the addition of an index.

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A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance offers the first systematic application of these methods to specialized financial problems: option pricing, credit risk modeling, volatility estimation and more. The book provides tools for sorting through turbulence, volatility, emotion, chaotic events - the random "noise" of financial markets - to analyze core components.

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Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement Review

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(More customer reviews)Measuring information security is the greatest remaining challenge for many of us. Metrics are essential for a scientific management approach, rather than relying purely on gut feel and guesswork. Standards such as ISO/IEC 27001 require the use of objective information about the status and effectiveness of information security controls in relation to the risks, in order to drive appropriate improvements in the Information Security Management System. However, it is not immediately obvious exactly what needs measuring, nor how to do it. This book lays out the foundations on which a rational measurement system can be designed to manage information security in a more objective fashion.
The author encourages readers to consider a wide variety of measurement approaches and apply them sensibly to their information security management issues. In addition to conventional information security metrics, the book draws on governance, risk management, financial management and business analysis methods, a more diverse range of approaches than is normally covered in this field. Introducing measures of organization structure and culture sets this security metrics book apart from most others.
Although the writing style is clear, this is a complex subject covered in depth. Being rather theoretical in approach, the book won't suit practitioners simply looking for a short checklist of `security things to measure'. However, those with the interest and time to study Information Security Management Metrics will be rewarded with a deeper and more rounded understanding of the issue. As such, the book is probably of most value to CISOs and ISMs tasked with implementing better security metrics, and to information security management students.

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Spectacular security failures continue to dominate the headlines despite huge increases in security budgets and ever-more draconian regulations. The 20/20 hindsight of audits is no longer an effective solution to security weaknesses, and the necessity for real-time strategic metrics has never been more critical. Information Security Management Metrics: A Definitive Guide to Effective Security Monitoring and Measurement offers a radical new approach for developing and implementing security metrics essential for supporting business activities and managing information risk. This work provides anyone with security and risk management responsibilities insight into these critical security questions: How secure is my organization?How much security is enough?What are the most cost-effective security solutions?How secure is my organization?Solid metrics are the key to cost-effective information security - you can't manage what you can't measure This volume shows readers how to develop metrics that can be used across an organization to assure its information systems are functioning, secure, and supportive of the organization's business objectives. It provides a comprehensive overview of security metrics, discusses the current state of metrics in use today, and looks at promising new developments. Later chapters explore ways to develop effective strategic and management metrics for information security governance, risk management, program implementation and management, and incident management and response. The book ensures that every facet of security required by an organization is linked to business objectives, and provides metrics to measure it. Case studies effectively demonstrate specific ways that metrics can be implemented across an enterprise to maximize business benefit. With three decades of enterprise information security experience, author Krag Brotby presents a workable approach to developing and managing cost-effective enterprise information security.

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Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research & Management Science) Review

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(More customer reviews)This is an excellent, approachable read for any risk manager; understanding its examples requires only elementary probability, statistics and calculus, though the foundations are much deeper. The author uses direct language, and does not hesitate to declare a fashionable risk analysis technique "worse than useless." The author shows how not to do risk analysis, using simple but devastating examples to illustrate the weaknesses of prioritized investments, subject matter expert opinion, risk matrices and qualitative risk assessments, and the independence assumption. Then, case studies present constructive examples of good practice. Refreshingly, this text clearly distinguishes between threats from Mother Nature, and those posed by an intelligent adversary. There is unevenness, because this is an edited ensemble of papers originally published in a variety of technical journals; however, this is also a strength, because the appeal and scholarship underlying biological, engineering, and social science examples is broad. This is not a how-to guide, and won't help fill in a blank page risk analysis; however, this is an excellent source for the skeptical consumer of contemporary risk management advice and products, and hopefully will have some influence with policy makers who are the source of simplistic and dangerous guidance.

Gerald G. Brown
Distinguished Professor of Operations Research
Naval Postgraduate School
National Academy of Engineering
INFORMS Fellow


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In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems - systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence - and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

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Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level (The Wiley Finance Series) Review

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(More customer reviews)Michael Rees succeeds to a large extent in his endeavor of writing a text that addresses the financial modeling process instead of Excel functionality, financial theory, or mathematical models. To his credit, Rees has put together a large number of useful modeling examples in the CD-ROM that is sold with the text. Rees' book assumes that readers have at least an intermediate knowledge of both statistical and financial concepts.
After reviewing select Excel functions and tools relevant to financial modeling, Rees gives his audience of modelers many practical tips about how to design, structure, and build models that are relevant, accurate, and easily understandable. Whoever has experience with models will probably agree with Rees when he writes that the majority of models built are in practice of mediocre quality. Someone other than the author of the model will often experience several challenges in dealing with the model at hand, i.e., too much time spent on understanding the model, complexity of the auditing and validating processes, hard to share with others, over-reliance on the original modeler to maintain or use it, lack of clarity of objectives, and presence of errors and implicit assumptions.
Rees then goes into the modeling of financial statements that is often required in the world of corporate finance for forecasting profit and cash, assessing financing requirements, analyzing credit risk and valuation, etc. This chapter is a little gem. It contains many practical tips. Once again, readers will be reminded that there is not always 100% agreement on the definition of some financial concepts.
Rees then uses Palisade Corporation's add-ins @RISK and PrecisionTree for many modeling examples in the two chapters that he dedicates to risk modeling and real option modeling, respectively. Having some understanding of both statistical and financial concepts is particularly important here to benefit from reading both chapters. Probably, many readers with an advanced knowledge of Excel 2007 will regret that the above-mentioned functionality that Palisade Corporation offers has not yet been systematically integrated into at least Microsoft Office Professional.
Finally, Rees discusses the use of Visual Basic for Applications (VBA) in a range of practical financial modeling situations. Rees points out that many otherwise competent modelers never learn VBA. For this reason, Rees makes the assumption that his audience is not very familiar with VBA. Rees shows how macros, i.e., subroutines and user-defined functions, can be used in a variety of modeling contexts.
In conclusion, Rees has made a valuable contribution to the field of financial modeling. The CD-ROM that is sold with the text plays a key role in achieving this objective.


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Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level is a practical, comprehensive and in-depth guide to financial modelling designed to cover the modelling issues that are relevant to facilitate the construction of robust and readily understandable models.
Based on the authors extensive experience of building models in business and finance, and of training others how to do so this book starts with a review of Excel functions that are generally most relevant for building intermediate and advanced level models (such as Lookup functions, database and statistical functions and so on). It then discusses the principles involved in designing, structuring and building relevant, accurate and readily understandable models (including the use of sensitivity analysis techniques) before covering key application areas, such as the modelling of financial statements, of cash flow valuation, risk analysis, options and real options. Finally, the topic of financial modelling using VBA is treated. Practical examples are used throughout and model examples are included in the attached CD-ROM.
Aimed at intermediate and advanced level modellers in Excel who wish to extend and consolidate their knowledge, this book is focused, practical, and application-driven, facilitating knowledge to build or audit a much wider range of financial models.
Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

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Operational Risk : Modeling Analytics (Wiley Series in Probability and Statistics) Review

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(More customer reviews)I train and consult bankers in credit and operational risk modeling for a living. I mention this because I think I have a decent feel for the pedagogy necessary for helping practitioners grasp the step-by-step application of these often highly theoretical concepts. Not all of us are rocket-scientists from birth and even though I have a quantitative PhD from Princeton, my often slow brain likes having things explained in simple terms. Panjer's book does this beautifully.
He has found the perfect balance between rigor and application--both in the exposition and scope of the book. He also includes examples of every concept that are easy to follow, replicate and extend. Moreover, Panjer takes you right to the edge of where advanced modeling of operational risk is at present. For example, he discusses EVT, copulas, infinite-mean models, kernel smoothing, robust methods, Bayesian methods, aggregation principles and compound processes as well as model selection--all of which characterize the current state of operational risk modeling and research. Indeed, beyond these points of technical modeling, most researchers (even mathematicians) are starting to agree that the returns are greatly diminishing and better qualitative/quantitative mixtures need to be developed (see Neslehova, et.al. Journal of Operational Risk, Spring, (2006)).
"Weak" spots in the book relate, for example, to how it fails to base itself firmly within the current operational risk literature. This is without loss of generality during this the infancy of operational risk modeling but I believe subsequent editions should address (at least to comment on) the growing number of papers popping up on websites and in trade journals. On the other hand, since I am not a statistician, I appreciated the author's references within that field as many of the topics are not part of the standard statistics canon. Finally, the book does not discuss operational risk management though, buying a book entitled "Operational Risk Modeling", I am not sure whether or why anyone would expect it to.
If this book were twice the price (such as for RiskBooks) I would still be happy with my purchase.

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Discover how to optimize business strategies from both qualitative and quantitative points of viewOperational Risk: Modeling Analytics is organized around the principle that the analysis of operational risk consists, in part, of the collection of data and the building of mathematical models to describe risk. This book is designed to provide risk analysts with a framework of the mathematical models and methods used in the measurement and modeling of operational risk in both the banking and insurance sectors.Beginning with a foundation for operational risk modeling and a focus on the modeling process, the book flows logically to discussion of probabilistic tools for operational risk modeling and statistical methods for calibrating models of operational risk. Exercises are included in chapters involving numerical computations for students' practice and reinforcement of concepts.Written by Harry Panjer, one of the foremost authorities in the world on risk modeling and its effects in business management, this is the first comprehensive book dedicated to the quantitative assessment of operational risk using the tools of probability, statistics, and actuarial science.In addition to providing great detail of the many probabilistic and statistical methods used in operational risk, this book features:* Ample exercises to further elucidate the concepts in the text* Definitive coverage of distribution functions and related concepts* Models for the size of losses* Models for frequency of loss* Aggregate loss modeling* Extreme value modeling* Dependency modeling using copulas* Statistical methods in model selection and calibrationAssuming no previous expertise in either operational risk terminology or in mathematical statistics, the text is designed for beginning graduate-level courses on risk and operational management or enterprise risk management. This book is also useful as a reference for practitioners in both enterprise risk management and risk and operational management.

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Regression Modeling with Actuarial and Financial Applications (International Series on Actuarial Science) Review

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(More customer reviews)This text covers regression techniques which the author views as the most commonly used statistical technique especially in the world of insurance and finance. Since the book is in a series for actuarial science I expected the presentation to be elementary to intermediate and to only cover regression. But some of the latter topics were quite advanced. In my mind survival analysis which is extremely important to actuaries and time series analysis which in very important to finance would not be covered as they do not fall into the category of regression. But thankfully they are included.
Some time series models such as polynomial functions of time can be viewed as linear regression models where time is the predictor variable for the response. But the main models like exponetial smoothing, Box-Jenkins and ARCH/GARCH which are the main ones applied in financial forecasting are really not regression in my view. But these too are covered in the book.
The book starts out in Chapter 1 with a very elementary review of statistics and simple forms of regression. Then Chapter 2-6 form part I which is titled Regression. Chapter 2 presents the basics of simple linear regression. Chapters 3 and 4 cover multiple regression. This is not a cookbook of techniques. The author provides background, historical developments and important concepts and mixes in applications to actuarial science and finance throughout. At the end of most chapters are a large number of exercises with solutions for selected problems in the back. In chapter 3 the author explains least squares presents the modeling assumptions and introduces the Gauss-Markovas well as all the standard concepts of hypothesis testing that a regression parameter is significant, R-square and theorem (hence also the concept of minimum variance among unbiased estimators). In Chapter 4 he provides the unified theme of the general linear hypothesis as he covers categorical predictor variables, the analysis of variance and covariance (all general linear models) In Chapter 5, leverage points, multicollinearity, and regression diagnostics are presented in the context of variable selection. Chapter 6 is all about interpretation and limitations.
Later in Parts III and IV the author introduces nonlinear regression models, logistic regression, probit and tobit models, Poisson and negative binomial regression, generalized linear models,and specialized techniques such as bootstrapping, mixed linear models, proportional hazards regression, generalized additive models and the Bayesian approach to regression. The coverage gets more advanced as you move through the chapters
Part II on time series includes seasonal models, discussion of stationary and longitudinal and panel data models.
In Part III survival analysis is included in Chapter 14. This includes the Kaplan-Meier estimates, proportional hazards regression, accelerated failure time models and even the analysis of recurrent events.
Part IV specifically focuses on actuarial applications and it is here that heavytailed distributions are dealt with using quantile regression and extreme value probability models.
With such an extensive list of topics the book is a large volume of over 560 pages. But even so it is not possible to do justice to this extensive list. The author provides an outstanding list of references at the end of the chapters that provides additional reading on the various topics.
In addition the author prvides programs in SAS and R as well as output form these packages. More detailed examples and projects can be found on the books website.

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Statistical techniques can be used to address new situations. This is important in a rapidly evolving risk management and financial world. Analysts with a strong statistical background understand that a large data set can represent a treasure trove of information to be mined and can yield a strong competitive advantage. This book provides budding actuaries and financial analysts with a foundation in multiple regression and time series. Readers will learn about these statistical techniques using data on the demand for insurance, lottery sales, foreign exchange rates, and other applications. Although no specific knowledge of risk management or finance is presumed, the approach introduces applications in which statistical techniques can be used to analyze real data of interest. In addition to the fundamentals, this book describes several advanced statistical topics that are particularly relevant to actuarial and financial practice, including the analysis of longitudinal, two-part (frequency/severity), and fat-tailed data. Datasets with detailed descriptions, sample statistical software scripts in "R" and "SAS," and tips on writing a statistical report, including sample projects, can be found on the book's Web site: http://research.bus.wisc.edu/RegActuaries.

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Catastrophe Modeling: A New Approach to Managing Risk (Huebner International Series on Risk, Insurance and Economic Security) Review

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(More customer reviews)An excellent survey of disaster insurance premium estimation. The volume is written by a variety of contributors, but they follow an outline predetermined by the editors. The material starts with an introduction to natural disasters (hurricanes and earthquakes), then reviews the primary 'technique' recommended by the authors: using GIS databases and sophisticated computer models to predict disaster costs (disaster models). Unfortunately, the details of running these models are hard to communicate without giving the reader access to an interface of a sample model.
Despite this difficulty, there is an excellent chapter detailing the legal battles between the insurance industry and consumer advocates. The battle was fought after the Northridge, CA earthquake, and the GIS data and disaster models were the heavy artillery employed by the insurance brokers. The authors review difficult issues regarding 'fair premium price' determination for regulated retail insurance policies. The purchasers of disaster insurance tend to see the brokers collecting risk-free profits. The brokers counter that the Northridge earthquake insurance payouts exceed all the premiums paid in California for over 20 years. Elsewhere the authors mention hurricane Andrew insurance payouts exceeded all insurance payments ever collected in Florida.
The final chapter covers 'terrorism insurance', and represents an excellent survey of issues facing the insurance industry after September 11. One of the interesting issues raised in the mismatch between industry assessment of 'fair premium' and public assumptions that a 9/11 type disaster could not happen, again. At least this is what sale of terrorism insurance demonstrates.
The book will probably suggest more questions than it answers. In particular, the chapter on terrorism raises interesting issues about 'governmental' coverage versus 'private' coverage. At a certain level, victims of terrorism can expect taxpayers to 'aid' those suffering from the disaster. 'Aid' is another term for insurance, but 'coverage' is universal and payments hidden in various taxes. Coming up with fair 'retail terrorism insurance premiums' seems beyond the capabilities of the US insurance system. The problems are structural and won't go away any time soon.

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Based on the research that has been conducted at Wharton Risk Management Centerover the past five years on catastrophic risk.Covers a hot topic in the light of recent terroristic activities and nature catastrophes.Develops risk management strategies for reducing and spreading the losses from future disasters.Provides glossary of definitions and terms used throughout the book.

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