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

To comprehend the results of a randomized controlled trial (RCT), readers must understand its design, conduct, analysis and interpretation. That goal can only be achieved through complete transparency from authors. Despite several decades of educational efforts, the reporting of RCTs needs improvement. Investigators and editors developed the original CONSORT (Consolidated Standards of Reporting Trials) statement to help authors improve reporting by using a checklist and flow diagram. The revised CONSORT statement presented in this paper incorporates new evidence and addresses some criticisms of the original statement.

http://www.people.vcu.edu/~albest/Guidance/CONSORTjama.pdf

The CONSORT statement: revised recommendations for improving the quality of reports of parallel group randomized trials

Like other sciences and engineering disciplines, software engineering requires a cycle of model building, experimentation, and learning. Experiments are valuable tools for all software engineers who are involved in evaluating and choosing between different methods, techniques, languages and tools. The purpose of Experimentation in Software Engineering is to introduce students, teachers, researchers, and practitioners to empirical studies in software engineering, using controlled experiments. The introduction to experimentation is provided through a process perspective, and the focus is on the steps that we have to go through to perform an experiment. The book is divided into three parts. The first part provides a background of theories and methods used in experimentation. Part II then devotes one chapter to each of the five experiment steps: scoping, planning, execution, analysis, and result presentation. Part III completes the presentation with two examples. Assignments and statistical material are provided in appendixes. Overall the book provides indispensable information regarding empirical studies in particular for experiments, but also for case studies, systematic literature reviews, and surveys. It is a revision of the authors book, which was published in 2000. In addition, substantial new material, e.g. concerning systematic literature reviews and case study research, is introduced. The book is self-contained and it is suitable as a course book in undergraduate or graduate studies where the need for empirical studies in software engineering is stressed. Exercises and assignments are included to combine the more theoretical material with practical aspects. Researchers will also benefit from the book, learning more about how to conduct empirical studies, and likewise practitioners may use it as a cookbook when evaluating new methods or techniques before implementing them in their organization.

Experimentation in Software Engineering

Cost Effectiveness in Health and Medicine.

The quality of reporting of studies of diagnostic accuracy is less than optimal. Complete and accurate reporting is necessary to enable readers to assess the potential for bias in the study and to evaluate the generalizability of the results. A group of scientists and editors has developed the STARD (Standards for Reporting of Diagnostic Accuracy) statement to improve the reporting the quality of reporting of studies of diagnostic accuracy. The statement consists of a checklist of 25 items and flow diagram that authors can use to ensure that all relevant information is present. This explanatory document aims to facilitate the use, understanding, and dissemination of the checklist. The document contains a clarification of the meaning, rationale, and optimal use of each item on the checklist, as well as a short summary of the available evidence on bias and applicability. The STARD statement, checklist, flowchart, and this explanation and elaboration document should be useful resources to improve reporting of diagnostic accuracy studies. Complete and informative reporting can only lead to better decisions in health care.

The STARD Statement for Reporting Studies of Diagnostic Accuracy: Explanation and Elaboration

A previous study examined the performance of a standard rule from Exploratory Data Analysis, which uses the sample fourths, FL and FU , and labels as “outside” any observations below FL – k(FU – FL ) or above FU + k(FU – FL ), customarily with k = 1.5. In terms of the order statistics X (1) ≤ X (2) ≤ X (n) the standard definition of the fourths is FL = X(f) and FU = X (n + 1 − f), where f = ½[(n + 3)/2] and [·] denotes the greatest-integer function. The results of that study suggest that finer interpolation for the fourths might yield smoother behavior in the face of varying sample size. In this article we show that using f i = n/4 + (5/12) to define the fourths produces the desired smoothness. Corresponding to a common definition of quartiles, fQ = n/4 + (1/4) leads to similar results. Instead of allowing the some-outside rate per sample (the probability that a sample contains one or more outside observations, analogous to the experimentwise error rate in simultaneous inference) to vary, some us...

Fine-Tuning Some Resistant Rules for Outlier Labeling

The techniques of exploratory data analysis include a resistant rule for identifying possible outliers in univariate data. Using the lower and upper fourths, FL and FU (approximate quartiles), it labels as “outside” any observations below FL − 1.5(FU — FL ) or above FU + 1.5(FU — FL ). For example, in the ordered sample −5, −2, 0, 1, 8, FL = −2 and FU = 1, so any observation below −6.5 or above 5.5 is outside. Thus the rule labels 8 as outside. Some related rules also use cutoffs of the form FL — k(FU — FL ) and FU + k(FU — FL ). This approach avoids the need to specify the number of possible outliers in advance; as long as they are not too numerous, any outliers do not affect the location of the cutoffs. To describe the performance of these rules, we define the some-outside rate per sample as the probability that a sample will contain one or more outside observations. Its complement is the all-inside rate per sample. We also define the outside rate per observation as the average fraction of outs...

https://www.jstor.org/stable/pdfplus/2289073.pdf

Performance of Some Resistant Rules for Outlier Labeling

An increasing number of statistical software packages offer exploratory data displays and summaries. For one of these, the graphical technique known as the boxplot, a selective survey of popular software packages revealed several definitions. These alternative constructions arise from different choices in computing quartiles and the fences that determine whether an observation is “outside” and thus plotted individually. We examine these alternatives and their consequences, discuss related background for boxplots (such as the probability that a sample contains one or more outside observations and the average proportion of outside observations in a sample), and offer recommendations that lead to a single standard form of the boxplot.

Some Implementations of the Boxplot

In clinical endpoint trials, the association between a baseline covariate and the risk of an endpoint is often measured by the hazard ratio as calculated by a Cox regression model, and illustrated by Kaplan-Meier curves comparing cohorts defined by levels of the covariate. The Cox regression model is easily extended to the case of time-varying covariates; however, there is no clear approach for similarly extending the standard Kaplan-Meier estimator. Various ad hoc procedures that have been used in the medical literature are seriously flawed. This article discusses an extended Kaplan-Meier estimator that can be used with time-varying covariates and illustrates this method using data from a long-term clinical trial.

Illustrating the Impact of a Time-Varying Covariate With an Extended Kaplan-Meier Estimator

A dynamic treatment allocation procedure is proposed for clinical trials which require balancing across several prognostic factors. The treatment allocation decision is based on the minimization of a function which is an easily evaluated approximation to the variance of the treatment effect in a linear model relating the outcome variable to the chosen prognostic factors and selected interactions. By use of simulations, the procedure is shown to be superior to ad hoc procedures proposed by Pocock and Simon (1975, Biometrics 31, 103-115), over a variety of reasonable experimental situations. It is shown that it is feasible to evaluate the procedure by hand calculations and that it is extremely easy if a small programmable calculator is available. Practical problems relating to implementation of the procedure are discussed with special reference to multi-institutional clinical trials.

Boris Iglewicz

Papers

Fine-Tuning Some Resistant Rules for Outlier Labeling

Performance of Some Resistant Rules for Outlier Labeling

Some Implementations of the Boxplot

Illustrating the Impact of a Time-Varying Covariate With an Extended Kaplan-Meier Estimator

A treatment allocation procedure for sequential clinical trials.