The idea of writing this bookarosein 2000when the ?rst author wasassigned to teach the required course STATS 240 (Statistical Methods in Finance) in the new M. S. program in ?nancial mathematics at Stanford, which is an interdisciplinary program that aims to provide a master’s-level education in applied mathematics, statistics, computing, ?nance, and economics. Students in the programhad di?erent backgroundsin statistics. Some had only taken a basic course in statistical inference, while others had taken a broad spectrum of M. S. - and Ph. D. -level statistics courses. On the other hand, all of them had already taken required core courses in investment theory and derivative pricing, and STATS 240 was supposed to link the theory and pricing formulas to real-world data and pricing or investment strategies. Besides students in theprogram,thecoursealso attractedmanystudentsfromother departments in the university, further increasing the heterogeneity of students, as many of them had a strong background in mathematical and statistical modeling from the mathematical, physical, and engineering sciences but no previous experience in ?nance. To address the diversity in background but common strong interest in the subject and in a potential career as a “quant” in the ?nancialindustry,thecoursematerialwascarefullychosennotonlytopresent basic statistical methods of importance to quantitative ?nance but also to summarize domain knowledge in ?nance and show how it can be combined with statistical modeling in ?nancial analysis and decision making. The course material evolved over the years, especially after the second author helped as the head TA during the years 2004 and 2005.

Statistical Models and Methods for Financial Markets (Springer Texts in Statistics)

Business & Money

Xing, Haipeng

Lai, Tze Leung

Economics

Springer

The Analysis of Time Series: An Introduction with R (Chapman & Hall/CRC Texts in Statistical Science)

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. It also presents many examples and implementations of time series models and methods to reflect advances in the field. Highlights of the seventh edition: <ul>
<li>A new chapter on univariate volatility models</li> <li>A revised chapter on linear time series models</li> <li>A new section on multivariate volatility models</li> <li>A new section on regime switching models</li> <li>Many new worked examples, with R code integrated into the text</li> </ul> The book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance.

The Analysis of Time Series (Chapman & Hall/CRC Texts in Statistical Science)

Science & Math

Chatfield, Chris

Mathematics

Chapman and Hall/CRC

Following the recent financial crisis, risk management in financial institutions, particularly in banks, has attracted widespread attention and discussion. Novel modeling approaches and courses to educate future professionals in industry, government, and academia are of timely relevance. This book introduces an innovative concept and methodology developed by the authors: active risk management. It is suitable for graduate students in mathematical finance/financial engineering, economics, and statistics as well as for practitioners in the fields of finance and insurance. The book’s website features the data sets used in the examples along with various exercises.

Data Science and Risk Analytics in Finance and Insurance (Chapman and Hall/CRC Financial Mathematics Series)

Reference

CRC-Press

The authors here present statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. They provide basic statistical background as well as in-depth applications.

Lai, Tze Leung Leung

Change-point Methodology and Applications (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

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