A properly structured financial model can provide decision makers with a powerful planning tool that helps them identify the consequences of their decisions before they are put into practice. Introduction to Financial Models for Management and Planning, Second Edition enables professionals and students to learn how to develop and use computer-based models for financial planning. This volume provides critical tools for the financial toolbox, then shows how to use them tools to build successful models.

Discover how statistical information impacts decisions in today's business world as Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann's leading ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS, 9E connects concepts in each chapter to real-world practice. This edition delivers sound statistical methodology, a proven problem-scenario approach and meaningful applications that reflect the latest developments in business and statistics today. More than 350 new and proven real business examples, a wealth of practical cases and meaningful hands-on exercises highlight statistics in action. You gain practice using leading professional statistical software with exercises and appendices that walk you through using JMP (R) Student Edition 14 and Excel (R) 2016. WebAssign's online course management systems is available separately to further strengthen this business statistics approach and helps you maximize your course success.

The book unifies quantum theory and the general theory of relativity. As an unsolved problem for about 100 years and influencing so many fields, this is probably of some importance to the scientific community. Examples like Higgs field, limit to classical Dirac and Klein-Gordon or Schroedinger cases, quantized Schwarzschild, Kerr, Kerr-Newman objects, and the photon are considered for illustration. An interesting explanation for the asymmetry of matter and antimatter in the early universe was found while quantizing the Schwarzschild metric.

An introduction to how the mathematical tools from quantum field theory can be applied to economics and finance, providing a wide range of quantum mathematical techniques for designing financial instruments. The ideas of Lagrangians, Hamiltonians, state spaces, operators and Feynman path integrals are demonstrated to be the mathematical underpinning of quantum field theory, and which are employed to formulate a comprehensive mathematical theory of asset pricing as well as of interest rates, which are validated by empirical evidence. Numerical algorithms and simulations are applied to the study of asset pricing models as well as of nonlinear interest rates. A range of economic and financial topics are shown to have quantum mechanical formulations, including options, coupon bonds, nonlinear interest rates, risky bonds and the microeconomic action functional. This is an invaluable resource for experts in quantitative finance and in mathematics who have no specialist knowledge of quantum field theory.

Actuaries have access to a wealth of individual data in pension and insurance portfolios, but rarely use its full potential. This book will pave the way, from methods using aggregate counts to modern developments in survival analysis. Based on the fundamental concept of the hazard rate, Part I shows how and why to build statistical models, based on data at the level of the individual persons in a pension scheme or life insurance portfolio. Extensive use is made of the R statistics package. Smooth models, including regression and spline models in one and two dimensions, are covered in depth in Part II. Finally, Part III uses multiple-state models to extend survival models beyond the simple life/death setting, and includes a brief introduction to the modern counting process approach. Practising actuaries will find this book indispensable, and students will find it helpful when preparing for their professional examinations.

Random set theory is a fascinating branch of mathematics that amalgamates techniques from topology, convex geometry, and probability theory. Social scientists routinely conduct empirical work with data and modelling assumptions that reveal a set to which the parameter of interest belongs, but not its exact value. Random set theory provides a coherent mathematical framework to conduct identification analysis and statistical inference in this setting and has become a fundamental tool in econometrics and finance. This is the first book dedicated to the use of the theory in econometrics, written to be accessible for readers without a background in pure mathematics. Molchanov and Molinari define the basics of the theory and illustrate the mathematical concepts by their application in the analysis of econometric models. The book includes sets of exercises to accompany each chapter as well as examples to help readers apply the theory effectively.

Develop the analytical skills that are in high demand in businesses today with Camm/Cochran/Fry/Ohlmann's best-selling BUSINESS ANALYTICS, 4E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals illustrate data and results for each topic. Step-by-step instructions guide you through using Microsoft (R) Excel, Tableau, R, and JMP Pro software to perform even advanced analytics concepts. Practical, relevant problems at all levels of difficulty further help you apply what you've learned. This edition assists you in becoming proficient in topics beyond the traditional quantitative concepts, such as data visualization and data mining, which are increasingly important in today's analytical problem solving. MindTap digital learning resources with an interactive eBook, algorithmic practice problems with solutions and Exploring Analytics visualizations strengthen your understanding of key concepts.

Chris Albright's VBA FOR MODELERS, 4E, International Edition is an essential tool for helping students learn to use Visual Basic for Applications (VBA) as a means to automate common spreadsheet tasks, as well as to create sophisticated management science applications. VBA is the programming language for Microsoft (R) Office. VBA FOR MODELERS, 4E, International Edition contains two parts. The first part teaches students the essentials of VBA for Excel. The second part illustrates how a number of management science models can be automated with VBA. From a user's standpoint, these applications hide the details of the management science techniques and instead present a simple user interface for inputs and results.

What do we mean by inequality comparisons? If the rich just get richer and the poor get poorer, the answer might seem easy. But what if the income distribution changes in a complicated way? Can we use mathematical or statistical techniques to simplify the comparison problem in a way that has economic meaning? What does it mean to measure inequality? Is it similar to National Income? Or a price index? Is it enough just to work out the Gini coefficient? Measuring Inequality tackles these questions and examines the underlying principles of inequality measurement and its relation to welfare economics, distributional analysis, and information theory. The book covers modern theoretical developments in inequality analysis, as well as showing how the way we think about inequality today has been shaped by classic contributions in economics and related disciplines. Formal results and detailed literature discussion are provided in two appendices. The principal points are illustrated in the main text, using examples from US and UK data, as well as other data sources, and associated web materials provide hands-on learning. Measuring Inequality is designed to appeal to both undergraduate and post-graduate students, and academic economists. Its emphasis on practical application means that it will also be useful to policy analysts and advisors.

'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times 'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric. We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: everyone is judged according to the same rules, and bias is eliminated. And yet, as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and incontestable, even when they're wrong. Most troubling, they reinforce discrimination. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort CVs, grant or deny loans, evaluate workers, target voters, and monitor our health. O'Neil calls on modellers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.

Die Monographie stellt eine prinzipielle Verallgemeinerung der herkoemmlichen Wahrscheinlichkeitstheorie vor. Diese erlaubt die Anwendung des Begriffs der Wahrscheinlichkeit auch in jenen Fallen, in denen die vorliegende Information nicht ausreicht, um jedes relevante Ereignis durch eine einzelne Zahl zu charakterisieren. Der mathematisch exakte Umgang mit Wahrscheinlichkeitsbewertungen erfordert eine systematische Erweiterung des Kanons der Begriffe und Methoden. Die Grundlagen hierfur werden im vorliegenden Band gelegt. Die Anwendungsmoeglichkeiten von Intervallwahrscheinlichkeit sind betrachtlich umfassender als die des herkoemmlichen Wahrscheinlichkeitsbegriffs, z.B. in den Bereichen Medizin, Technik, Versicherungswesen und kunstliche Intelligenz.

This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in the context of different market segments. Although no prior knowledge in this area is required, the reader is expected to have knowledge of probability theory as well as a general mathematical aptitude.Its simple presentation of complex algorithms required to solve modeling problems in increasingly sophisticated financial markets makes this book particularly valuable as a reference for graduate students and researchers interested in the field. Furthermore, it analyses the model estimation results in the context of the market and contrasts these with contemporary research publications. It is also suitable for use as a text for graduate level courses on stochastic modeling.

The use of credit scoring - the quantitative and statistical techniques to assess the credit risks involved in lending to consumers - has been one of the most successful if unsung applications of mathematics in business for the last fifty years. Now with lenders changing their objectives from minimising defaults to maximising profits, the saturation of the consumer credit market allowing borrowers to be more discriminating in their choice of which loans, mortgages and credit cards to use, and the Basel Accord banking regulations raising the profile of credit scoring within banks there are a number of challenges that require new models that use credit scores as inputs and extensions of the ideas in credit scoring. This book reviews the current methodology and measures used in credit scoring and then looks at the models that can be used to address these new challenges. The first chapter describes what a credit score is and how a scorecard is built which gives credit scores and models how the score is used in the lending decision. The second chapter describes the different ways the quality of a scorecard can be measured and points out how some of these measure the discrimination of the score, some the probability prediction of the score, and some the categorical predictions that are made using the score. The remaining three chapters address how to use risk and response scoring to model the new problems in consumer lending. Chapter three looks at models that assist in deciding how to vary the loan terms made to different potential borrowers depending on their individual characteristics. Risk based pricing is the most common approach being introduced. Chapter four describes how one can use Markov chains and survival analysis to model the dynamics of a borrower's repayment and ordering behaviour . These models allow one to make decisions that maximise the profitability of the borrower to the lender and can be considered as part of a customer relationship management strategy. The last chapter looks at how the new banking regulations in the Basel Accord apply to consumer lending. It develops models that show how they will change the operating decisions used in consumer lending and how their need for stress testing requires the development of new models to assess the credit risk of portfolios of consumer loans rather than a models of the credit risks of individual loans.

The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.

The substantially updated third edition of the popular Actuarial Mathematics for Life Contingent Risks is suitable for advanced undergraduate and graduate students of actuarial science, for trainee actuaries preparing for professional actuarial examinations, and for life insurance practitioners who wish to increase or update their technical knowledge. The authors provide intuitive explanations alongside mathematical theory, equipping readers to understand the material in sufficient depth to apply it in real-world situations and to adapt their results in a changing insurance environment. Topics include modern actuarial paradigms, such as multiple state models, cash-flow projection methods and option theory, all of which are required for managing the increasingly complex range of contemporary long-term insurance products. Numerous exam-style questions allow readers to prepare for traditional professional actuarial exams, and extensive use of Excel ensures that readers are ready for modern, Excel-based exams and for the actuarial work environment. The Solutions Manual (ISBN 9781108747615), available for separate purchase, provides detailed solutions to the text's exercises.

The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practicing analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data."

This book provides a comprehensive and unified treatment of finite sample statistics and econometrics, a field that has evolved in the last five decades. Within this framework, this is the first book which discusses the basic analytical tools of finite sample econometrics, and explores their applications to models covered in a first year graduate course in econometrics, including repression functions, dynamic models, forecasting, simultaneous equations models, panel data models, and censored models. Both linear and nonlinear models, as well as models with normal and non-normal errors, are studied. Finite sample results are extremely useful for applied researchers doing proper econometric analysis with small or moderately large sample data. Finite sample econometrics also provides the results for very large (asymptotic) samples. This book provides simple and intuitive presentations of difficult concepts, unified and heuristic developments of methods, and applications to various econometric models. It provides a new perspective on teaching and research in econometrics, statistics, and other applied subjects.

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.

This definitive textbook provides a solid introduction to discrete and continuous stochastic processes, tackling a complex field in a way that instils a deep understanding of the relevant mathematical principles, and develops an intuitive grasp of the way these principles can be applied to modelling real-world systems. It includes a careful review of elementary probability and detailed coverage of Poisson, Gaussian and Markov processes with richly varied queuing applications. The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching and enriched by over 300 exercises, this is an exceptional resource for anyone looking to develop their understanding of stochastic processes.

Developed over 20 years of teaching academic courses, the Handbook of Financial Risk Management can be divided into two main parts: risk management in the financial sector; and a discussion of the mathematical and statistical tools used in risk management. This comprehensive text offers readers the chance to develop a sound understanding of financial products and the mathematical models that drive them, exploring in detail where the risks are and how to manage them. Key Features: Written by an author with both theoretical and applied experience Ideal resource for students pursuing a master's degree in finance who want to learn risk management Comprehensive coverage of the key topics in financial risk management Contains 114 exercises, with solutions provided online at www.crcpress.com/9781138501874

The small sample properties of estimators and tests are frequently too complex to be useful or are unknown. Much econometric theory is therefore developed for very large or asymptotic samples where it is assumed that the behaviour of estimators and tests will adequately represent their properties in small samples. Refined asymptotic methods adopt an intermediate position by providing improved approximations to small sample behaviour using asymptotic expansions. Dedicated to the memory of Michael Magdalinos, whose work is a major contribution to this area, this book contains chapters directly concerned with refined asymptotic methods. In addition, there are chapters focusing on new asymptotic results; the exploration through simulation of the small sample behaviour of estimators and tests in panel data models; and improvements in methodology. With contributions from leading econometricians, this collection will be essential reading for researchers and graduate students concerned with the use of asymptotic methods in econometric analysis.

How can organizations ensure that they can get best value for money in their procurement decisions? How can they stimulate innovations from their dedicated suppliers? With contributions from leading academics and professionals, this 2006 handbook offers expert guidance on the fundamental aspects of successful procurement design and management in firms, public administrations, and international institutions. The issues addressed include the management of dynamic procurement; the handling of procurement risk; the architecture of purchasing systems; the structure of incentives in procurement contracts; methods to increase suppliers' participation in procurement contests and e-procurement platforms; how to minimize the risk of collusion and of corruption; pricing and reputation mechanisms in e-procurement platforms; and how procurement can enhance innovation. Inspired by frontier research, it provides practical recommendations to managers, engineers and lawyers engaged in private and public procurement design.

This textbook contains and explains essential mathematical formulas within an economic context. A broad range of aids and supportive examples will help readers to understand the formulas and their practical applications. This mathematical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice. The topics presented include, but are not limited to: mathematical signs and symbols, logic, arithmetic, algebra, linear algebra, combinatorics, financial mathematics, optimisation of linear models, functions, differential calculus, integral calculus, elasticities, economic functions, and the Peren theorem. Given its scope, the book offers an indispensable reference guide and is a must-read for undergraduate and graduate students, as well as managers, scholars, and lecturers in business, politics, and economics.

This is a concise and elementary introduction to stochastic control and mathematical modelling. This book is designed for researchers in stochastic control theory studying its application in mathematical economics and those in economics who are interested in mathematical theory in control. It is also a good guide for graduate students studying applied mathematics, mathematical economics, and non-linear PDE theory. Contents include the basics of analysis and probability, the theory of stochastic differential equations, variational problems, problems in optimal consumption and in optimal stopping, optimal pollution control, and solving the Hamilton-Jacobi-Bellman (HJB) equation with boundary conditions. Major mathematical prerequisites are contained in the preliminary chapters or in the appendix so that readers can proceed without referring to other materials.