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Statistics and Data Analysis for Financial Engineering

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Paperback / softback
27 December 2012
$199.42
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">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 <em>Statistics and Finance: An Introduction</em>, 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. </div>
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.</div>
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">Some exposure to finance is helpful.</div>

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    Product Details
    ISBN-13
    9781461427490
    Author
    David Ruppert
    Publisher
    Springer Nature B.V.
    Published
    27 December 2012
    Format
    Paperback / softback

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    $199.42

    Statistics and Data Analysis for Financial Engineering

    $199.42

    Description

    <div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">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 <em>Statistics and Finance: An Introduction</em>, 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. </div>
    <div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.</div>
    <div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">Some exposure to finance is helpful.</div>

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