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Statistical Inference In Financial And Insurance Mathematics With R
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Book Synopsis Statistical Inference in Financial and Insurance Mathematics with R by : Alexandre Brouste
Download or read book Statistical Inference in Financial and Insurance Mathematics with R written by Alexandre Brouste and published by Elsevier. This book was released on 2017-11-22 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables. Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described. In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text. Examines a range of statistical inference methods in the context of finance and insurance applications Presents the LAN (local asymptotic normality) property of likelihoods Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments
Book Synopsis Statistical Analysis of Financial Data in R by : René Carmona
Download or read book Statistical Analysis of Financial Data in R written by René Carmona and published by Springer Science & Business Media. This book was released on 2013-12-13 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This textbook fills this gap by addressing some of the most challenging issues facing financial engineers. It shows how sophisticated mathematics and modern statistical techniques can be used in the solutions of concrete financial problems. Concerns of risk management are addressed by the study of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Principal component analysis (PCA), smoothing, and regression techniques are applied to the construction of yield and forward curves. Time series analysis is applied to the study of temperature options and nonparametric estimation. Nonlinear filtering is applied to Monte Carlo simulations, option pricing and earnings prediction. This textbook is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. It is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the R computing environment. They illustrate problems occurring in the commodity, energy and weather markets, as well as the fixed income, equity and credit markets. The examples, experiments and problem sets are based on the library Rsafd developed for the purpose of the text. The book should help quantitative analysts learn and implement advanced statistical concepts. Also, it will be valuable for researchers wishing to gain experience with financial data, implement and test mathematical theories, and address practical issues that are often ignored or underestimated in academic curricula. This is the new, fully-revised edition to the book Statistical Analysis of Financial Data in S-Plus. René Carmona is the Paul M. Wythes '55 Professor of Engineering and Finance at Princeton University in the department of Operations Research and Financial Engineering, and Director of Graduate Studies of the Bendheim Center for Finance. His publications include over one hundred articles and eight books in probability and statistics. He was elected Fellow of the Institute of Mathematical Statistics in 1984, and of the Society for Industrial and Applied Mathematics in 2010. He is on the editorial board of several peer-reviewed journals and book series. Professor Carmona has developed computer programs for teaching statistics and research in signal analysis and financial engineering. He has worked for many years on energy, the commodity markets and more recently in environmental economics, and he is recognized as a leading researcher and expert in these areas.
Book Synopsis R Programming and Its Applications in Financial Mathematics by : Shuichi Ohsaki
Download or read book R Programming and Its Applications in Financial Mathematics written by Shuichi Ohsaki and published by CRC Press. This book was released on 2018-01-31 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to R programming and a summary of financial mathematics. It is not always easy for graduate students to grasp an overview of the theory of finance in an abstract form. For newcomers to the finance industry, it is not always obvious how to apply the abstract theory to the real financial data they encounter. Introducing finance theory alongside numerical applications makes it easier to grasp the subject. Popular programming languages like C++, which are used in many financial applications are meant for general-purpose requirements. They are good for implementing large-scale distributed systems for simultaneously valuing many financial contracts, but they are not as suitable for small-scale ad-hoc analysis or exploration of financial data. The R programming language overcomes this problem. R can be used for numerical applications including statistical analysis, time series analysis, numerical methods for pricing financial contracts, etc. This book provides an overview of financial mathematics with numerous examples numerically illustrated using the R programming language.
Book Synopsis Statistical Inference for Financial Engineering by : Masanobu Taniguchi
Download or read book Statistical Inference for Financial Engineering written by Masanobu Taniguchi and published by Springer Science & Business Media. This book was released on 2014-03-26 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
Book Synopsis Mathematical and Statistical Methods for Insurance and Finance by : Cira Perna
Download or read book Mathematical and Statistical Methods for Insurance and Finance written by Cira Perna and published by Springer Science & Business Media. This book was released on 2007-12-12 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: The interaction between mathematicians and statisticians reveals to be an effective approach to the analysis of insurance and financial problems, in particular in an operative perspective. The Maf2006 conference, held at the University of Salerno in 2006, had precisely this purpose and the collection published here gathers some of the papers presented at the conference and successively worked out to this aim. They cover a wide variety of subjects in insurance and financial fields.
Book Synopsis Statistical Analysis of Financial Data in S-Plus by : René Carmona
Download or read book Statistical Analysis of Financial Data in S-Plus written by René Carmona and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the introduction of tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves. The book is aimed at undergraduate students in financial engineering; master students in finance and MBA's, and to practitioners with financial data analysis concerns.
Book Synopsis R Programming for Actuarial Science by : Peter McQuire
Download or read book R Programming for Actuarial Science written by Peter McQuire and published by John Wiley & Sons. This book was released on 2023-10-16 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: R Programming for Actuarial Science Professional resource providing an introduction to R coding for actuarial and financial mathematics applications, with real-life examples R Programming for Actuarial Science provides a grounding in R programming applied to the mathematical and statistical methods that are of relevance for actuarial work. In R Programming for Actuarial Science, readers will find: Basic theory for each chapter to complement other actuarial textbooks which provide foundational theory in depth. Topics covered include compound interest, statistical inference, asset-liability matching, time series, loss distributions, contingencies, mortality models, and option pricing plus many more typically covered in university courses. More than 400 coding examples and exercises, most with solutions, to enable students to gain a better understanding of underlying mathematical and statistical principles. An overall basic to intermediate level of coverage in respect of numerous actuarial applications, and real-life examples included with every topic. Providing a highly useful combination of practical discussion and basic theory, R Programming for Actuarial Science is an essential reference for BSc/MSc students in actuarial science, trainee actuaries studying privately, and qualified actuaries with little programming experience, along with undergraduate students studying finance, business, and economics.
Book Synopsis Statistics and Data Analysis for Financial Engineering by : David Ruppert
Download or read book Statistics and Data Analysis for Financial Engineering written by David Ruppert and published by Springer. This book was released on 2015-04-21 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.
Book Synopsis Computational Actuarial Science with R by : Arthur Charpentier
Download or read book Computational Actuarial Science with R written by Arthur Charpentier and published by CRC Press. This book was released on 2014-08-26 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).
Book Synopsis A Course in Statistics with R by : Prabhanjan N. Tattar
Download or read book A Course in Statistics with R written by Prabhanjan N. Tattar and published by John Wiley & Sons. This book was released on 2016-03-15 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets
Book Synopsis Optimal Statistical Inference in Financial Engineering by : Masanobu Taniguchi
Download or read book Optimal Statistical Inference in Financial Engineering written by Masanobu Taniguchi and published by CRC Press. This book was released on 2007-11-26 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering literature, even though this idea is fundamental to the field. Balancing statistical theory with data analysis, Optimal Statistical Inference in Financial Engineering examines how stochastic models can effectively des
Book Synopsis Introduction to Insurance Mathematics by : Annamaria Olivieri
Download or read book Introduction to Insurance Mathematics written by Annamaria Olivieri and published by Springer. This book was released on 2015-09-30 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition expands the first chapters, which focus on the approach to risk management issues discussed in the first edition, to offer readers a better understanding of the risk management process and the relevant quantitative phases. In the following chapters the book examines life insurance, non-life insurance and pension plans, presenting the technical and financial aspects of risk transfers and insurance without the use of complex mathematical tools. The book is written in a comprehensible style making it easily accessible to advanced undergraduate and graduate students in Economics, Business and Finance, as well as undergraduate students in Mathematics who intend starting on an actuarial qualification path. With the systematic inclusion of practical topics, professionals will find this text useful when working in insurance and pension related areas, where investments, risk analysis and financial reporting play a major role.
Book Synopsis An Introduction to Analysis of Financial Data with R by : Ruey S. Tsay
Download or read book An Introduction to Analysis of Financial Data with R written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2014-08-21 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.
Book Synopsis Examples in Parametric Inference with R by : Ulhas Jayram Dixit
Download or read book Examples in Parametric Inference with R written by Ulhas Jayram Dixit and published by Springer. This book was released on 2016-05-20 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.
Book Synopsis Financial Data Analytics with Machine Learning, Optimization and Statistics by : Yongzhao Chen
Download or read book Financial Data Analytics with Machine Learning, Optimization and Statistics written by Yongzhao Chen and published by John Wiley & Sons. This book was released on 2024-11-19 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.
Book Synopsis Probability and Statistical Inference Using R by : Sunil K. Mathur
Download or read book Probability and Statistical Inference Using R written by Sunil K. Mathur and published by Academic Press. This book was released on 2013-11-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a solid, well-thought and well-balanced foundation in probability and statistical inference, enabling students to gain fundamental understanding of the subject. The material is presented in a way that invokes the thinking process in students, and helps them find solutions to problems posed to them. Mathematics is required for understanding probability, and probability is needed to understand statistical inference. Fisher presented a way to introduce methodologies and lay foundations in statistics, while Neyman showed how mathematics should be used in statistics to prove things mathematically and brought mathematical orientation in statistics. Topics covered in the book are fairly conventional along with some recent topics in probability and statistical inference presented in a rigorous mathematical and conceptual way. The aim of the book is to use principles of probability theory using tools of measure theory to build strong theoretical statistical inference. The topics have a wealth of material, and many topics are given greater in-depth coverage. The conceptual structure of topics is self-contained. Methodology is illustrated using real-life applications. Provides proofs for almost all the theorems and statements Material is backed by well-researched references Features more than 500 problems and more than 300 worked examples Includes R code for most of the worked examples Numerous figures and tables are used to help in understanding the concepts
Book Synopsis Computational Finance by : Argimiro Arratia
Download or read book Computational Finance written by Argimiro Arratia and published by Springer Science & Business Media. This book was released on 2014-05-08 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described.
