-Up-to-date with cutting edge topics -Suitable for professional quants and as library reference for students of finance and financial mathematics

Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book's coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.

Thorough presentation of the problem of portfolio optimization, leading in a natural way to the Capital Market Theory Dynamic programming and the optimal portfolio selection-consumption problem through time An intuitive approach to Brownian motion and stochastic integral models for continuous time problems The Black-Scholes equation for simple European option values, derived in several different ways A chapter on several types of exotic options and one on material on the management of risk in several contexts

Leverage the full power of Bayesian analysis for competitive advantage Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and move toward R to do even more. Step by step, with real-world examples, Carlberg shows you how to use Bayesian analytics to solve a wide array of real problems. Carlberg clarifies terminology that often bewilders analysts, and offers sample R code to take advantage of the rethinking package in R and its gateway to Stan. As you incorporate these Bayesian approaches into your analytical toolbox, you'll build a powerful competitive advantage for your organization-and yourself. Explore key ideas and strategies that underlie Bayesian analysis Distinguish prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputs Use grid approximation to solve simple univariate problems, and understand its limits as parameters increase Perform complex simulations and regressions with quadratic approximation and Richard McElreath's quap function Manage text values as if they were numeric Learn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC) Use MCMC to optimize execution speed in high-complexity problems Discover when frequentist methods fail and Bayesian methods are essential-and when to use both in tandem

Analytics is one of a number of terms which are used to describe a data-driven more scientific approach to management. Ability in analytics is an essential management skill: knowledge of data and analytics helps the manager to analyze decision situations, prevent problem situations from arising, identify new opportunities, and often enables many millions of dollars to be added to the bottom line for the organization. The objective of this book is to introduce analytics from the perspective of the general manager of a corporation. Rather than examine the details or attempt an encyclopaedic review of the field, this text emphasizes the strategic role that analytics is playing in globally competitive corporations today. The chapters of this book are organized in two main parts. The first part introduces a problem area and presents some basic analytical concepts that have been successfully used to address the problem area. The objective of this material is to provide the student, the manager of the future, with a general understanding of the tools and techniques used by the analyst.

Master key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS, 9E, written by respected business analytics innovator Cliff Ragsdale. This edition's clear presentation, realistic examples, fascinating topics and valuable software provide everything you need to become proficient in today's most widely used business analytics techniques using the latest version of Excel (R) in Microsoft (R) Office 365 or Office 2019. Become skilled in the newest Excel functions as well as Analytic Solver (R) and Data Mining add-ins. This edition helps you develop both algebraic and spreadsheet modeling skills. Step-by-step instructions and annotated, full-color screen images make examples easy to follow and show you how to apply what you learn about descriptive, predictive and prescriptive analytics to real business situations. WebAssign online tools and author-created videos further strengthen understanding.

Explains modern SDC techniques for data stewards and develop tools to implement them. Explains the logic behind modern privacy protections for researchers and how they may use publicly released data to generate valid statistical inferences-as well as the limitations imposed by SDC techniques.

Developed from the author's course on Monte Carlo simulation at Brown University, Monte Carlo Simulation with Applications to Finance provides a self-contained introduction to Monte Carlo methods in financial engineering. It is suitable for advanced undergraduate and graduate students taking a one-semester course or for practitioners in the financial industry.

The author first presents the necessary mathematical tools for simulation, arbitrary free option pricing, and the basic implementation of Monte Carlo schemes. He then describes variance reduction techniques, including control variates, stratification, conditioning, importance sampling, and cross-entropy. The text concludes with stochastic calculus and the simulation of diffusion processes.

Only requiring some familiarity with probability and statistics, the book keeps much of the mathematics at an informal level and avoids technical measure-theoretic jargon to provide a practical understanding of the basics. It includes a large number of examples as well as MATLAB(r) coding exercises that are designed in a progressive manner so that no prior experience with MATLAB is needed.

Highlights the pitfalls of data analysis and emphasizes the importance of using the appropriate metrics before making key decisions. Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it. The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts.

Handbook of Empirical Economics and Finance explores the latest developments in the analysis and modeling of economic and financial data. Well-recognized econometric experts discuss the rapidly growing research in economics and finance and offer insight on the future direction of these fields. Focusing on micro models, the first group of chapters describes the statistical issues involved in the analysis of econometric models with cross-sectional data often arising in microeconomics. The book then illustrates time series models that are extensively used in empirical macroeconomics and finance. The last set of chapters explores the types of panel data and spatial models that are becoming increasingly significant in analyzing complex economic behavior and policy evaluations. This handbook brings together both background material and new methodological and applied results that are extremely important to the current and future frontiers in empirical economics and finance. It emphasizes inferential issues that transpire in the analysis of cross-sectional, time series, and panel data-based empirical models in economics, finance, and related disciplines.

* Explores the exciting and new topic of econophysics * Multidisciplinary approach, that will be of interest to students and researchers from physics, engineering, mathematics, statistics, and other physical sciences * Useful to both students and researchers

A classic text for accuracy and statistical precision. Statistics for Business and Economics enables readers to conduct serious analysis of applied problems rather than running simple "canned" applications. This text is also at a mathematically higher level than most business statistics texts and provides readers with the knowledge they need to become stronger analysts for future managerial positions. The eighth edition of this book has been revised and updated to provide readers with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics.

Models for repeated measurements will be of interest to research statisticians in agriculture, medicine, economics, and psychology, and to the many consulting statisticians who want an up-to-date expository account of this important topic. The second edition of this successful book has been completely revised and updated to take account of developments in the area over the last few years. This book is organized into four parts. In the first part, the general context of repeated measurements is presented. In the following three parts, a large number of concrete examples, including data tables, is presented to illustrate the models available. The book also provides a very extensive and updated bibliography of the repeated measurements literature.

Thijs ten Raa, author of the acclaimed text The Economics of Input-Output Analysis, now takes the reader to the forefront of the field. This volume collects and unifies his and his co-authors' research papers on national accounting, input-output coefficients, economic theory, dynamic models, stochastic analysis, and performance analysis. The research is driven by the task to analyze national economies. The final part of the book scrutinizes the emerging Asian economies in the light of international competition.

The time series in the first part of this third biennial compilation show the values of each country's imports and exports, the trading blocs, and each country's most important trading partners. Next, imports and exports of selected commodities are detailed by quantity and by value over the years. Annual and cumulative trade balances are listed and a breakdown is given by Standard International Trade Classification groups. Part Four provides balance of payment figures and indicates changes in indebtedness at various times. A detailed list of tables and an alphabetical index permit quick and easy access to any information required.

A highly readable, logically presented, unique guide to asset allocation strategies and technical analysis, this work covers numerous investment alternatives including mutual funds and fixed income securities. Aby and Vaughn provide a comprehensive examination of point and figure charting and vertical bar analysis, combined with an approach that both improves timing and emphasizes the minimization of errors in data interpretation and investment decision making. The authors discuss ways to estimate price targets and provide unique forecasting methods for fixed-income and aggregate equity markets, using an intermarket perspective. This is an important and useful resource for professionals and other knowledgeable investors.

Throughout the book, Aby and Vaughn challenge conventional and acceptable academic thinking. Through emphasis on smaller, more obscure capitalization issues, they reduce complex concepts to a highly readable framework pervaded by comprehensive coverages of a large number of investment options. Major topics featured include the illustration and application of critical concepts underlying vertical bar chart analysis; extensive coverage on contemporary strategies that improve timing and challenge past criticisms of point and figure charting; a unique approach utilizing the point and figure charts to reveal how mutual fund selection can be improved; and intermarket technical analysis, a method through which movements in bond prices and yields are predicted.

There is no book currently available that gives a comprehensive treatment of the design, construction, and use of index numbers. However, there is a pressing need for one in view of the increasing and more sophisticated employment of index numbers in the whole range of applied economics and specifically in discussions of macroeconomic policy. In this book, R. G. D. Allen meets this need in simple and consistent terms and with comprehensive coverage.

The text begins with an elementary survey of the index-number problem before turning to more detailed treatments of the theory and practice of index numbers. The binary case in which one time period is compared with another is first developed and illustrated with numerous examples. This is to prepare the ground for the central part of the text on runs of index numbers. Particular attention is paid both to fixed-weighted and to chain forms as used in a wide range of published index numbers taken mainly from British official sources.

This work deals with some further problems in the construction of index numbers, problems which are both troublesome and largely unresolved. These include the use of sampling techniques in index-number design and the theoretical and practical treatment of quality changes. It is also devoted to a number of detailed and specific applications of index-number techniques to problems ranging from national-income accounting, through the measurement of inequality of incomes and international comparisons of real incomes, to the use of index numbers of stock-market prices. Aimed primarily at students of economics, whatever their age and range of interests, this work will also be of use to those who handle index numbers professionally. "R. G. D. Allen" (1906-1983) was Professor Emeritus at the University of London. He was also once president of the Royal Statistical Society and Treasurer of the British Academy where he was a fellow. He is the author of "Basic Mathematics," "Mathematical Analysis for Economists," "Mathematical Economics" and "Macroeconomic Theory."

This textbook addresses postgraduate students in applied mathematics, probability, and statistics, as well as computer scientists, biologists, physicists and economists, who are seeking a rigorous introduction to applied stochastic processes. Pursuing a pedagogic approach, the content follows a path of increasing complexity, from the simplest random sequences to the advanced stochastic processes. Illustrations are provided from many applied fields, together with connections to ergodic theory, information theory, reliability and insurance. The main content is also complemented by a wealth of examples and exercises with solutions.

Tackling the cybersecurity challenge is a matter of survival for society at large. Cyber attacks are rapidly increasing in sophistication and magnitude-and in their destructive potential. New threats emerge regularly, the last few years having seen a ransomware boom and distributed denial-of-service attacks leveraging the Internet of Things. For organisations, the use of cybersecurity risk management is essential in order to manage these threats. Yet current frameworks have drawbacks which can lead to the suboptimal allocation of cybersecurity resources. Cyber insurance has been touted as part of the solution - based on the idea that insurers can incentivize companies to improve their cybersecurity by offering premium discounts - but cyber insurance levels remain limited. This is because companies have difficulty determining which cyber insurance products to purchase, and insurance companies struggle to accurately assess cyber risk and thus develop cyber insurance products. To deal with these challenges, this volume presents new models for cybersecurity risk management, partly based on the use of cyber insurance. It contains: A set of mathematical models for cybersecurity risk management, including (i) a model to assist companies in determining their optimal budget allocation between security products and cyber insurance and (ii) a model to assist insurers in designing cyber insurance products. The models use adversarial risk analysis to account for the behavior of threat actors (as well as the behavior of companies and insurers). To inform these models, we draw on psychological and behavioural economics studies of decision-making by individuals regarding cybersecurity and cyber insurance. We also draw on organizational decision-making studies involving cybersecurity and cyber insurance. Its theoretical and methodological findings will appeal to researchers across a wide range of cybersecurity-related disciplines including risk and decision analysis, analytics, technology management, actuarial sciences, behavioural sciences, and economics. The practical findings will help cybersecurity professionals and insurers enhance cybersecurity and cyber insurance, thus benefiting society as a whole. This book grew out of a two-year European Union-funded project under Horizons 2020, called CYBECO (Supporting Cyber Insurance from a Behavioral Choice Perspective).

The goal of Portfolio Rebalancing is to provide mathematical and empirical analysis of the effects of portfolio rebalancing on portfolio returns and risks. The mathematical analysis answers the question of when and why fixed-weight portfolios might outperform buy-and-hold portfolios based on volatilities and returns. The empirical analysis, aided by mathematical insights, will examine the effects of portfolio rebalancing in capital markets for asset allocation portfolios and portfolios of stocks, bonds, and commodities.

The quantitative modeling of complex systems of interacting risks is a fairly recent development in the financial and insurance industries. Over the past decades, there has been tremendous innovation and development in the actuarial field. In addition to undertaking mortality and longevity risks in traditional life and annuity products, insurers face unprecedented financial risks since the introduction of equity-linking insurance in 1960s. As the industry moves into the new territory of managing many intertwined financial and insurance risks, non-traditional problems and challenges arise, presenting great opportunities for technology development. Today's computational power and technology make it possible for the life insurance industry to develop highly sophisticated models, which were impossible just a decade ago. Nonetheless, as more industrial practices and regulations move towards dependence on stochastic models, the demand for computational power continues to grow. While the industry continues to rely heavily on hardware innovations, trying to make brute force methods faster and more palatable, we are approaching a crossroads about how to proceed. An Introduction to Computational Risk Management of Equity-Linked Insurance provides a resource for students and entry-level professionals to understand the fundamentals of industrial modeling practice, but also to give a glimpse of software methodologies for modeling and computational efficiency. Features Provides a comprehensive and self-contained introduction to quantitative risk management of equity-linked insurance with exercises and programming samples Includes a collection of mathematical formulations of risk management problems presenting opportunities and challenges to applied mathematicians Summarizes state-of-arts computational techniques for risk management professionals Bridges the gap between the latest developments in finance and actuarial literature and the practice of risk management for investment-combined life insurance Gives a comprehensive review of both Monte Carlo simulation methods and non-simulation numerical methods Runhuan Feng is an Associate Professor of Mathematics and the Director of Actuarial Science at the University of Illinois at Urbana-Champaign. He is a Fellow of the Society of Actuaries and a Chartered Enterprise Risk Analyst. He is a Helen Corley Petit Professorial Scholar and the State Farm Companies Foundation Scholar in Actuarial Science. Runhuan received a Ph.D. degree in Actuarial Science from the University of Waterloo, Canada. Prior to joining Illinois, he held a tenure-track position at the University of Wisconsin-Milwaukee, where he was named a Research Fellow. Runhuan received numerous grants and research contracts from the Actuarial Foundation and the Society of Actuaries in the past. He has published a series of papers on top-tier actuarial and applied probability journals on stochastic analytic approaches in risk theory and quantitative risk management of equity-linked insurance. Over the recent years, he has dedicated his efforts to developing computational methods for managing market innovations in areas of investment combined insurance and retirement planning.

Since the publication of the first edition over 30 years ago, the literature related to Pareto distributions has flourished to encompass computer-based inference methods. Pareto Distributions, Second Edition provides broad, up-to-date coverage of the Pareto model and its extensions. This edition expands several chapters to accommodate recent results and reflect the increased use of more computer-intensive inference procedures. New to the Second Edition New material on multivariate inequality Recent ways of handling the problems of inference for Pareto models and their generalizations and extensions New discussions of bivariate and multivariate income and survival models This book continues to provide researchers with a useful resource for understanding the statistical aspects of Pareto and Pareto-like distributions. It covers income models and properties of Pareto distributions, measures of inequality for studying income distributions, inference procedures for Pareto distributions, and various multivariate Pareto distributions existing in the literature.

Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R. This book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable. Web ResourceMore detailed examples are available on a supplementary website. The site also contains JAGS, R, and Stata codes to estimate the models along with syntax to reproduce the results.

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes, the book explains univariate extreme value mixture modeling, threshold selection in extreme value analysis, and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas, develops time series of extremes with applications from climatology, describes max-autoregressive and moving maxima models for extremes, and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies, such as composite likelihood, Bayesian inference, and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling, including financial investments, insurance and financial risk management, weather and climate disasters, clinical trials, and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology, hydrology, finance, insurance, sports, and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists.

Economic evaluation has become an essential component of clinical trial design to show that new treatments and technologies offer value to payers in various healthcare systems. Although many books exist that address the theoretical or practical aspects of cost-effectiveness analysis, this book differentiates itself from the competition by detailing how to apply health economic evaluation techniques in a clinical trial context, from both academic and pharmaceutical/commercial perspectives. It also includes a special chapter for clinical trials in Cancer. Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement is not just about performing cost-effectiveness analyses. It also emphasizes the strategic importance of economic evaluation and offers guidance and advice on the complex factors at play before, during, and after an economic evaluation. Filled with detailed examples, the book bridges the gap between applications of economic evaluation in industry (mainly pharmaceutical) and what students may learn in university courses. It provides readers with access to SAS and STATA code. In addition, Windows-based software for sample size and value of information analysis is available free of charge-making it a valuable resource for students considering a career in this field or for those who simply wish to know more about applying economic evaluation techniques. The book includes coverage of trial design, case report form design, quality of life measures, sample sizes, submissions to regulatory authorities for reimbursement, Markov models, cohort models, and decision trees. Examples and case studies are provided at the end of each chapter. Presenting first-hand insights into how economic evaluations are performed from a drug development perspective, the book supplies readers with the foundation required to succeed in an environment where clinical trials and cost-effectiveness of new treatments are central. It also includes thought-provoking exercises for use in classroom and seminar discussions.