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Publication Date: November 17, 2010 | ISBN-10: 1441977864 | ISBN-13: 978-1441977861 | Edition: 2011 Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful.

Quantitative Equity Portfolio Management Publication Date: May 11, 2007 | ISBN-10: 1584885580 | ISBN-13: 978-1584885580 | Edition: 1 Quantitative equity portfolio management combines theories and advanced techniques from several disciplines, including financial economics, accounting, mathematics, and operational research. While many texts are devoted to these disciplines, few deal with quantitative equity investing in a systematic and mathematical framework that is suitable for quantitative investment students. Providing a solid foundation in the subject, Quantitative Equity Portfolio Management: Modern Techniques and Applications presents a self-contained overview and a detailed mathematical treatment of various topics. From the theoretical basis of behavior finance to recently developed techniques, the authors review quantitative investment strategies and factors that are commonly used in practice, including value, momentum, and quality, accompanied by their academic origins. They present advanced techniques and applications in return forecasting models, risk management, portfolio construction, and portfolio implementation that include examples such as optimal multi-factor models, contextual and nonlinear models, factor timing techniques, portfolio turnover control, Monte Carlo valuation of firm values, and optimal trading. In many cases, the text frames related problems in mathematical terms and illustrates the mathematical concepts and solutions with numerical and empirical examples. Ideal for students in computational and quantitative finance programs, Quantitative Equity Portfolio Management serves as a guide to combat many common modeling issues and provides a rich understanding of portfolio management using mathematical analysis.

A Primer on Scientific Programming with Python Apress.The.Definitive.Guide.to.Jython.Python.for.the.Java.Platform.Feb.2010 NumpyforBeginner Packt.NumPy Cookbook.2012 Prentice.Hall.Core.Python.Applications.Programming.3rd.Edition.Mar.2012 SciPy and NumPy Python in a nutshell 2nd