The standard resource for statisticians and applied researchers. Accessible to the wide range of researchers who use statistical modelling techniques.

While mainstream financial theories and applications assume that asset returns are normally distributed, overwhelming empirical evidence shows otherwise. Yet many professionals don’t appreciate the highly statistical models that take this empirical evidence into consideration. Fat-Tailed and Skewed Asset Return Distributions examines this dilemma and offers readers a less technical look at how portfolio selection, risk management, and option pricing modeling should and can be undertaken when the assumption of a non-normal distribution for asset returns is violated. Topics covered in this comprehensive book include an extensive discussion of probability distributions, estimating probability distributions, portfolio selection, alternative risk measures, and much more. Fat-Tailed and Skewed Asset Return Distributions provides a bridge between the highly technical theory of statistical distributional analysis, stochastic processes, and econometrics of financial returns and real-world risk management and investments.

This book reviews the state-of-the-art advances in skew-elliptical distributions and provides many new developments in a single volume, collecting theoretical results and applications previously scattered throughout the literature. The main goal of this research area is to develop flexible parametric classes of distributions beyond the classical no

In commemoration of the bicentennial of the birth of the “lady who gave the rose diagram to us”, this special contributed book pays a statistical tribute to Florence Nightingale. This book presents recent phenomenal developments, both in rigorous theory as well as in emerging methods, for applications in directional statistics, in 25 chapters with contributions from 65 renowned researchers from 25 countries. With the advent of modern techniques in statistical paradigms and statistical machine learning, directional statistics has become an indispensable tool. Ranging from data on circles to that on the spheres, tori and cylinders, this book includes solutions to problems on exploratory data analysis, probability distributions on manifolds, maximum entropy, directional regression analysis, spatio-directional time series, optimal inference, simulation, statistical machine learning with big data, and more, with their innovative applications to emerging real-life problems in astro-statistics, bioinformatics, crystallography, optimal transport, statistical process control, and so on.

Mixture models are a powerful tool for analyzing complex and heterogeneous datasets across many scientific fields, from finance to genomics. Mixture Models: Parametric, Semiparametric, and New Directions provides an up-to-date introduction to these models, their recent developments, and their implementation using R. It fills a gap in the literature by covering not only the basics of finite mixture models, but also recent developments such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling. Features Comprehensive overview of the methods and applications of mixture models Key topics include hypothesis testing, model selection, estimation methods, and Bayesian approaches Recent developments, such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling Examples and case studies from such fields as astronomy, biology, genomics, economics, finance, medicine, engineering, and sociology Integrated R code for many of the models, with code and data available in the R Package MixSemiRob Mixture Models: Parametric, Semiparametric, and New Directions is a valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a supplementary text for courses on data mining, semiparametric modeling, and high-dimensional data analysis.

This book is designed in making statisticians, researchers, and programmers aware of the awesome new product now available in SAS called Enterprise Miner. The book will also make readers get familiar with the neural network forecasting methodology in statistics. One of the goals to this book is making the powerful new SAS module called Enterprise Miner easy for you to use with step-by-step instructions in creating a Enterprise Miner process flow diagram in preparation to data-mining analysis and neural network forecast modeling. Topics discussed in this book An overview to traditional regression modeling. An overview to neural network modeling. Numerical examples of various neural network designs and optimization techniques. An overview to the powerful SAS product called Enterprise Miner. An overview to the SAS neural network modeling procedure called PROC NEURAL. Designing a SAS Enterprise Miner process flow diagram to perform neural network forecast modeling and traditional regression modeling with an explanation to the various configuration settings to the Enterprise Miner nodes used in the analysis. Comparing neural network forecast modeling estimates with traditional modeling estimates based on various examples from SAS manuals and literature with an added overview to the various modeling designs and a brief explanation to the SAS modeling procedures, option statements, and corresponding SAS output listings.

A thoroughly revised and updated edition of a textbook for graduate students in finance, with new coverage of global financial institutions. This thoroughly revised and updated edition of a widely used textbook for graduate students in finance now provides expanded coverage of global financial institutions, with detailed comparisons of U.S. systems with non-U.S. systems. A focus on the actual practices of financial institutions prepares students for real-world problems. After an introduction to financial markets and market participants, including asset management firms, credit rating agencies, and investment banking firms, the book covers risks and asset pricing, with a new overview of risk; the structure of interest rates and interest rate and credit risks; the fundamentals of primary and secondary markets; government debt markets, with new material on non-U.S. sovereign debt markets; corporate funding markets, with new coverage of small and medium enterprises and entrepreneurial ventures; residential and commercial real estate markets; collective investment vehicles, in a chapter new to this edition; and financial derivatives, including financial futures and options, interest rate derivatives, foreign exchange derivatives, and credit risk transfer vehicles such as credit default swaps. Each chapter begins with learning objectives and ends with bullet point takeaways and questions.