Download or read book Correlated Default Modeling with a Forest of Binomial Trees written by Santhosh Bandreddi and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper exploits the endogenous default function framework of Das and Sundaram (2007) to develop an approach for modeling correlated default on binomial trees usually used for pricing equity options. We show how joint default contracts may be valued on these trees. The model accommodates different correlation assumptions and practical implementation considerations. Credit portfolio characteristics are examined within the model and found to be consistent with stylized empirics. Risk premia for default are computable and shown to be relatively higher for poor quality firms. Equity volatility is shown to impact correlated credit loss distributions substantially. Two kinds of default dependence are explored, one coming from default intensity correlations, and the other from further conditional dependence in defaults after accounting for intensity correlations (residual copula correlation). Both are found to impact credit loss distributions, though the absence of either makes these distributions less sensitive to correlation assumptions; on balance intensity correlations are more critical.
Download or read book Innovations in Investment Management written by H. Gifford Fong and published by John Wiley & Sons. This book was released on 2010-05-13 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Founded by Gifford Fong in 2003, the Journal Of Investment Management (JOIM) is a premier publication that bridges the theory and practice of investment management. The JOIM Conference Series showcases the leading thinkers in finance from both the academic and professional worlds. Their research is presented to an exclusive—and equally prestigious—audience. This book is a selection of the ideas offered at the first two conference series. Created from the presentations and background papers of each speaker, the resulting chapters cover a variety of topics in investment management, distilled to the essence of what financial professionals need to know. Contributors include legendary market researchers Andrew W. Lo, Nobel Prize-winner Robert Merton, Zvi Bodie, Barton Waring, Sanjiv Das, Ananth Madhavan, George Chacko, and Terry Marsh.
Download or read book Credit Risk written by Niklas Wagner and published by CRC Press. This book was released on 2008-05-28 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Featuring contributions from leading international academics and practitioners, Credit Risk: Models, Derivatives, and Management illustrates how a risk management system can be implemented through an understanding of portfolio credit risks, a set of suitable models, and the derivation of reliable empirical results. Divided into six sectio
Download or read book Business Periodicals Index written by and published by . This book was released on 2007 with total page 2892 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Healthcare Finance written by Andrew W. Lo and published by Princeton University Press. This book was released on 2022-11-15 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why healthcare finance? -- From the laboratory to the patient -- Present value relations -- Evaluating business opportunities -- Valuing bonds -- Valuing stocks -- Portfolio management and the cost of capital -- Therapeutic development and clinical trials -- Decision trees and real options -- Monte Carlo simulation -- Healthcare analytics -- Biotech venture capital -- Securitizing biomedical assets -- Pricing, value, and ethics -- Epilogue : a case study pf royalty pharma.
Download or read book Antimicrobial Resistance As a Global Public Health Problem How Can We Address It written by Ilana L. B. C. Camargo and published by Frontiers Media SA. This book was released on 2020-12-21 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Download or read book Canadian Journal of Fisheries and Aquatic Sciences written by and published by . This book was released on 2014 with total page 738 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Managing Deep sea Ecosystems at Ocean Basin Scale Volume 1 written by J. Murray Roberts and published by Frontiers Media SA. This book was released on 2022-04-01 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bioinformatics analysis of single cell sequencing and multi omics in the aging and age associated diseases written by Shouneng Peng and published by Frontiers Media SA. This book was released on 2024-03-13 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Derivatives written by Sanjiv Das and published by McGraw-Hill Education. This book was released on 2010-03-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been the authors' experience that the overwhelming majority of students in MBA derivatives courses go on to careers where a deep conceptual, rather than solely mathematical, understanding of products and models is required. The first edition of Derivatives looks to create precisely such a blended approach, one that is formal and rigorous, yet intuitive and accessible. The main body of this book is divided into six parts. Parts 1-3 cover, respectively, futures and forwards; options; and swaps. Part 4 examines term-structure modeling and the pricing of interest-rate derivatives, while Part 5 is concerned with credit derivatives and the modeling of credit risk. Part 6 discusses computational issues.
Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Download or read book Ecological Models and Data in R written by Benjamin M. Bolker and published by Princeton University Press. This book was released on 2008-07-21 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Download or read book Database and Expert Systems Applications written by Norman Revell and published by Springer Science & Business Media. This book was released on 2007-08-21 with total page 927 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the 18th International Conference on Database and Expert Systems Applications held in September 2007. Papers are organized into topical sections covering XML, data and information, datamining and data warehouses, database applications, WWW, bioinformatics, process automation and workflow, knowledge management and expert systems, database theory, query processing, and privacy and security.
Download or read book Building Better Models with JMP Pro written by Jim Grayson and published by SAS Institute. This book was released on 2015-08-01 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building Better Models with JMP® Pro provides an example-based introduction to business analytics, with a proven process that guides you in the application of modeling tools and concepts. It gives you the "what, why, and how" of using JMP® Pro for building and applying analytic models. This book is designed for business analysts, managers, and practitioners who may not have a solid statistical background, but need to be able to readily apply analytic methods to solve business problems. In addition, this book will greatly benefit faculty members who teach any of the following subjects at the lower to upper graduate level: predictive modeling, data mining, and business analytics. Novice to advanced users in business statistics, business analytics, and predictive modeling will find that it provides a peek inside the black box of algorithms and the methods used. Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross-validation, model comparison and selection, and data reduction techniques. Full of rich examples, Building Better Models with JMP Pro is an applied book on business analytics and modeling that introduces a simple methodology for managing and executing analytics projects. No prior experience with JMP is needed. Make more informed decisions from your data using this newest JMP book.
Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
