This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems. Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book's methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives. Fully revised and expanded to reflect developments in the field since the financial crisis Features shorter chapters to facilitate teaching and learning Provides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricing Includes a new chapter on market risk and new material on risk measures and risk aggregation

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Quantitative Risk Management: Concepts, Techniques, and Tools

Series Preface. One: Introduction to Psychological Tests and Their Uses. Two: Essential Statistics for Testing. Three: Essentials of Test Score Interpretation. Four: Essentials of Reliability. Five: Essentials of Validity. Six: Essential Test Item Considerations. Seven: Essentials of Test Use. Appendix A: Commercially Available Tests. Appendix B: Addresses of Test Publishers and Distributors. Appendix C: Table of Areas and Ordinates of the Normal Curve. References. Index. Acknowledgments. About the Author.

Essentials of Psychological Testing

Neoinstitutionalists have developed a rich array of theoretical and empirical insights about how new practices become established via legitimacy and diffusion, but have paid scant attention to their origins. This blind spot has been reinforced by recent work on institutional entrepreneurship which has too often celebrated the actions of a single or small number of actors, and deflected attention away from the emergent, multilevel nature of how new kinds of activities emerge and provide a foundation for the creation of a new practice. In this paper, we examine the case of the creation of active money management practice in the US mutual fund industry, drawing on both institutional and practice scholarship, to develop a process model of new practice creation that redirects attention toward the multiplicity of actors that interactively produce change.

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New Practice Creation: An Institutional Perspective on Innovation

This paper proposes a new definition, and conceptual framework for social protection, grounded in social risk management. The concept repositions the traditional areas of social protection (labor market intervention, social insurance, and social safety nets) in a framework that includes three strategies to deal with risk (prevention, mitigation, and coping), three levels of formality of risk management (informal, market-based, public), and, many actors (individuals, households, communities, non-governmental organizations, governments at various levels, and international organizations) against the background of asymmetric information, and different types of risk. This expanded view of social protection emphasizes the double role of risk management instruments - protecting basic livelihood, as well as promoting risk taking. It focuses specifically on the poor, since they are the most vulnerable to risk, and typically lack appropriate risk management instruments, which constrains them from engaging in riskier, but also higher return activities, and hence gradually moving out of chronic poverty.

Social risk management : a new conceptual framework for social protection and beyond

The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

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Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.

ural and Political Observations Made upon the Bills of Mortality (1662) stands as the first great example of modern statistical data analysis, Bernstein also spends some time telling us about the genesis of Lloyd’s of London, the famous insurance firm. This weaving of topics that are standard in the history of probability and statistics with many that are not is one of the strengths and attractions of the book. Another is the obvious zest with which Bernstein describes the many intriguing people and tantalizing mysteries so peculiar to the mathematics of chance.

Against the Gods: The Remarkable Story of Risk

• Links nonparametric or distribution-free procedures to other statistical approaches • Presents the latest methodology on censored data and bootstrapping • Covers interactions in factorial structures and multiple comparison tests • Features a range of realistic, worked-out examples from medicine, biology, engineering, and the social sciences • Includes an insert with a substantially discounted offer on the student edition of StatXact • Contains numerous end-of-chapter exercises and selected solutions in an appendix • Provides a full solutions manual with qualifying course adoptions

Applied Nonparametric Statistical Methods

Statistical Distributions in Engineering

(2003). Introduction to Linear Regression Analysis. The American Statistician: Vol. 57, No. 1, pp. 67-67.

Introduction to Linear Regression Analysis

book are deliberately tailored for the beginner including trend analysis, effects of seasonality and the study of cycles in six chapters out of the 12 chapters. The coverage of evaluation and combining forecasts in chapter 11 is comprehensive enough for the intended level of this book. The Problems and Complements sections form an integral part of each chapter and should prove helpful when teaching from the text. There is an Instructor’s Manual, which contains solutions or hints to some problems and complements. The book is application-oriented with examples like exchange rate forecasting. Most of the examples are done in EViews which provides regression and forecasting tools in modern Windows environments. It illustrates all methods with detailed real world applications which were designed to study typical forecasting situations. Thus, as a learning tool by itself, Diebold’s text is a good place to start. Diebold is careful to describe how to go about performing actual computations and how to interpret the outputs from the EViews program. A nice feature of the book is its rich graphical illustration. Diebold devotes one entire chapter to explain statistical graphics for forecasting. Graphical analysis is often so simple and straightforward it is apt to be misleading. However, Diebold carefully illustrates graphs to help the student with getting intuitionand circumventing mistakes. Overall, the book is well laid out and contains few typographical errors. However, the second edition does not differ much from the previous one, only minor changes with new additional problems. I do not advise purchasing this new edition if one already has the earlier version. I believe that Diebold’s text would be an excellent choice for a undergraduate course after an introductory econometric course.

Sreenivasa Rao Jammalamadaka

Papers

Against the Gods: The Remarkable Story of Risk

Applied Nonparametric Statistical Methods

Statistical Distributions in Engineering

Introduction to Linear Regression Analysis

Statistics in Psychology: An Historical Perspective