Statistics for Finance
Statistics for Finance

Author: Erik Lindström

Publisher: CRC Press

Published: 2018-09-03

Total Pages: 384

ISBN-13: 1315362554

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Statistics for Finance develops students’ professional skills in statistics with applications in finance. Developed from the authors’ courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, Itō’s formula, the Black–Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more. This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students’ financial reasoning skills.

A Course on Statistics for Finance
A Course on Statistics for Finance

Author: Stanley L. Sclove

Publisher: CRC Press

Published: 2012-12-06

Total Pages: 281

ISBN-13: 1439892547

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Taking a data-driven approach, A Course on Statistics for Finance presents statistical methods for financial investment analysis. The author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance. The book begins with a review of basic statistics, including descriptive statistics, kinds of variables, and types of data sets. It then discusses regression analysis in general terms and in terms of financial investment models, such as the capital asset pricing model and the Fama/French model. It also describes mean-variance portfolio analysis and concludes with a focus on time series analysis. Providing the connection between elementary statistics courses and quantitative finance courses, this text helps both existing and future quants improve their data analysis skills and better understand the modeling process.

Probability and Statistics for Finance
Probability and Statistics for Finance

Author: Svetlozar T. Rachev

Publisher: John Wiley & Sons

Published: 2010-07-30

Total Pages: 676

ISBN-13: 0470906324

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A comprehensive look at how probability and statistics is applied to the investment process Finance has become increasingly more quantitative, drawing on techniques in probability and statistics that many finance practitioners have not had exposure to before. In order to keep up, you need a firm understanding of this discipline. Probability and Statistics for Finance addresses this issue by showing you how to apply quantitative methods to portfolios, and in all matter of your practices, in a clear, concise manner. Informative and accessible, this guide starts off with the basics and builds to an intermediate level of mastery. • Outlines an array of topics in probability and statistics and how to apply them in the world of finance • Includes detailed discussions of descriptive statistics, basic probability theory, inductive statistics, and multivariate analysis • Offers real-world illustrations of the issues addressed throughout the text The authors cover a wide range of topics in this book, which can be used by all finance professionals as well as students aspiring to enter the field of finance.

Statistics and Data Analysis for Financial Engineering
Statistics and Data Analysis for Financial Engineering

Author: David Ruppert

Publisher: Springer

Published: 2015-04-21

Total Pages: 736

ISBN-13: 1493926144

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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.

Statistics of Financial Markets
Statistics of Financial Markets

Author: Szymon Borak

Publisher: Springer Science & Business Media

Published: 2013-01-11

Total Pages: 266

ISBN-13: 3642339298

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Practice makes perfect. Therefore the best method of mastering models is working with them. This book contains a large collection of exercises and solutions which will help explain the statistics of financial markets. These practical examples are carefully presented and provide computational solutions to specific problems, all of which are calculated using R and Matlab. This study additionally looks at the concept of corresponding Quantlets, the name given to these program codes and which follow the name scheme SFSxyz123. The book is divided into three main parts, in which option pricing, time series analysis and advanced quantitative statistical techniques in finance is thoroughly discussed. The authors have overall successfully created the ideal balance between theoretical presentation and practical challenges.

Financial Statistics and Data Analytics
Financial Statistics and Data Analytics

Author: Shuangzhe Li

Publisher: MDPI

Published: 2021-03-02

Total Pages: 232

ISBN-13: 3039439758

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Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Statistical Analysis of Financial Data in S-Plus
Statistical Analysis of Financial Data in S-Plus

Author: René Carmona

Publisher: Springer Science & Business Media

Published: 2006-04-18

Total Pages: 456

ISBN-13: 0387218246

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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.

Statistics of Financial Markets
Statistics of Financial Markets

Author: Jürgen Franke

Publisher: Springer Science & Business Media

Published: 2004

Total Pages: 454

ISBN-13: 9783540216759

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Extreme Value Theory (EVT), GARCH MODELS, Hypothesis Testing, Fitting Probability Distributions to Risk Factors and Portfolios.

Financial Statistics and Mathematical Finance
Financial Statistics and Mathematical Finance

Author: Ansgar Steland

Publisher: John Wiley & Sons

Published: 2012-06-21

Total Pages: 355

ISBN-13: 1118316568

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Mathematical finance has grown into a huge area of research which requires a lot of care and a large number of sophisticated mathematical tools. Mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, it considers various aspects of the application of statistical methods in finance and illustrates some of the many ways that statistical tools are used in financial applications. Financial Statistics and Mathematical Finance: Provides an introduction to the basics of financial statistics and mathematical finance. Explains the use and importance of statistical methods in econometrics and financial engineering. Illustrates the importance of derivatives and calculus to aid understanding in methods and results. Looks at advanced topics such as martingale theory, stochastic processes and stochastic integration. Features examples throughout to illustrate applications in mathematical and statistical finance. Is supported by an accompanying website featuring R code and data sets. Financial Statistics and Mathematical Finance introduces the financial methodology and the relevant mathematical tools in a style that is both mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, both graduate students and researchers in statistics, finance, econometrics and business administration will benefit from this book.

Mathematics and Statistics for Financial Risk Management
Mathematics and Statistics for Financial Risk Management

Author: Michael B. Miller

Publisher: John Wiley & Sons

Published: 2013-12-31

Total Pages: 341

ISBN-13: 1118750292

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Mathematics and Statistics for Financial Risk Management is a practical guide to modern financial risk management for both practitioners and academics. Now in its second edition with more topics, more sample problems and more real world examples, this popular guide to financial risk management introduces readers to practical quantitative techniques for analyzing and managing financial risk. In a concise and easy-to-read style, each chapter introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problems and application sections demonstrate how these techniques can be applied to actual risk management problems. Exercises at the end of each chapter and the accompanying solutions at the end of the book allow readers to practice the techniques they are learning and monitor their progress. A companion Web site includes interactive Excel spreadsheet examples and templates. Mathematics and Statistics for Financial Risk Management is an indispensable reference for today’s financial risk professional.