Inhoudsopgave:
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\u003cdiv style=\"MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal\"\u003eFinancial 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 \u003cem\u003eStatistics and Finance: An Introduction\u003c/em\u003e, 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. \u003c/div\u003e \u003cdiv style=\"MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal\"\u003eThe prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.\u003c/div\u003e \u003cdiv style=\"MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal\"\u003eSome exposure to finance is helpful.\u003c/div\u003e |
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