This book introduces statistical application in finance, with methods of evaluating option contracts, analyzing financial time series, choosing portfolios and managing risks. The 4th edition offers new chapters on long memory models, copulae and CDO valuation.

Now in its fifth edition, this book offers a detailed yet concise introduction to the growing field of statistical applications in finance. The reader will learn the basic methods for evaluating option contracts, analyzing financial time series, selecting portfolios and managing risks based on realistic assumptions about market behavior. The focus is both on the fundamentals of mathematical finance and financial time series analysis, and on applications to specific problems concerning financial markets, thus making the book the ideal basis for lectures, seminars and crash courses on the topic. All numerical calculations are transparent and reproducible using quantlets.

For this new edition the book has been updated and extensively revised and now includes several new aspects such as neural networks, deep learning, and crypto-currencies. Both R and Matlab code, together with the data, can be downloaded from the book's product page and the Quantlet platform.

The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allow readers to reproduce the tables, pictures and calculations inside this Springer book.

** This book provides an excellent introduction to the tools from probability and statistics necessary to analyze financial data. Clearly written and accessible, it will be very useful to students and practitioners alike.

Yacine Ait-Sahalia, Otto Hack 1903 Professor of Finance and Economics, Princeton University

Offers an essential introduction to the growing field of statistical applications in finance Addresses option pricing, analysis of financial time series, portfolio selection and risk management, and various financial applications Includes chapters on neural networks and deep learning, and crypto-currencies Using statistical software, readers can learn by doing and directly apply the methods

Autorentext
Jürgen Franke is a Professor of Applied Mathematical Statistics at Technische Universität Kaiserslautern, Germany, and is affiliated as advisor to the Fraunhofer Institute for Industrial Mathematics, Kaiserslautern. His research focuses on nonlinear time series, nonparametric statistics and machine learning with applications in time series and risk analysis for finance and industry. Wolfgang Karl Härdle is a Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität Berlin, Germany, and director of the IRTG 1792 "High Dimensional Non-stationary Time Series." He teaches quantitative finance and semi-parametric statistics. His research focuses on dynamic factor models, multivariate statistics in finance, and computational statistics. He is an elected member of the ISI (International Statistical Institute) and advisor to the Guanghua School of Management, Peking University, China. Christian Matthias Hafner is a Professor of Econometrics at the Université Catholique de Louvain and President of the Louvain School of Statistics, Biostatistics and Actuarial Sciences. His work is mainly concerned with applied non- and semiparametric statistics, time series analysis, volatility models, and financial econometrics.

Inhalt
Preface to the Fith Edition.- Part I Option Pricing.- Derivatives.- Introduction to Option Management.- Basic Concepts of Probability Theory.- Stochastic Processes in Discrete Time.- Stochastic Integrals and Differential Equations.- BlackScholes Option Pricing Model.- Binomial Model for European Options.- American Options.- Exotic Options.- Interest Rates and Interest Rate Derivatives.- Part II Statistical Models of Financial Time Series.- Introduction: Definitions and Concepts.- ARIMA Time Series Models.- Time Series with Stochastic Volatility.- Long Memory Time Series.- Non-Parametric and Flexible Time Series Estimators.- Part III Selected Financial Applications.- Value at Risk and Backtesting.- Copulae and Value at Risk.- Statistics of Extreme Risks.- Neural Networks and Deep Learning.- Volatility Risk of Option Portfolios.- Nonparametric Estimators for the Probability of Default.- Credit Risk Management and Credit Derivatives.- Financial econometrics ofCrypto-currencies.- A Technical Appendix.- Index.- Symbols and Notations.