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Statistics and Data Analysis for Financial Engineering

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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 di 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.


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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 di 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.

30 review for Statistics and Data Analysis for Financial Engineering

  1. 4 out of 5

    Shubham S.

    Great text for someone with prior background in Probability Theory and Calculus. A crisp, to-the-point text book for financial econometrics.

  2. 5 out of 5

    David

    This book broadly covers many statistical techniques applied in finance. Most likely the audience for this book is for students in Quant Finance programs learning some statistics techniques. I guess it succeeds in that purpose. But for researchers, statisticians, or practitioners there is not enough depth. I rated this book 2 stars rather than 1 star, because I do believe Ruppert did a good job introducing difficult topics, for example the Bayesian methods. But generally, this book and his other This book broadly covers many statistical techniques applied in finance. Most likely the audience for this book is for students in Quant Finance programs learning some statistics techniques. I guess it succeeds in that purpose. But for researchers, statisticians, or practitioners there is not enough depth. I rated this book 2 stars rather than 1 star, because I do believe Ruppert did a good job introducing difficult topics, for example the Bayesian methods. But generally, this book and his other undergraduate version "Statistics and Finance" are not worth reading unless you are completely new to a topic. That being said, some of Ruppert's other books are quite nice, for example "Semiparametric Regression".

  3. 5 out of 5

    Evgeniy

  4. 5 out of 5

    Stela

  5. 5 out of 5

    Veljko Krunic

  6. 5 out of 5

    Gunhee Lee

  7. 4 out of 5

    Felipe Osorio

  8. 4 out of 5

    Milos

  9. 4 out of 5

    ADK

  10. 4 out of 5

    Thanh Binh

  11. 4 out of 5

    Keven Bluteau

  12. 4 out of 5

    Brian Peterson

  13. 4 out of 5

    Jovany Agathe

  14. 4 out of 5

    Franta

  15. 5 out of 5

    Trung Dang

  16. 4 out of 5

    Claude

  17. 4 out of 5

    Lucas Idargo

  18. 5 out of 5

    Andrew

  19. 4 out of 5

    Ashkan Ziabakhshdeylami

  20. 5 out of 5

    Gene

  21. 5 out of 5

    John

  22. 5 out of 5

    William Scott

  23. 5 out of 5

    Melissa

  24. 5 out of 5

    Alex

  25. 4 out of 5

    Vicky Zhou

  26. 5 out of 5

    Serin

  27. 5 out of 5

    Michael Levine

  28. 4 out of 5

    Emmeri

  29. 4 out of 5

    Rishabh Soni

  30. 5 out of 5

    Eric Nichols

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