## Statistics and Data Analysis for Financial EngineeringFinancial 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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### Contents

1 | |

5 | |

3 Fixed Income Securities | 17 |

4 Exploratory Data Analysis | 41 |

5 Modeling Univariate Distributions | 79 |

6 Resampling | 131 |

7 Multivariate Statistical Models | 149 |

8 Copulas | 175 |

Troubleshooting | 341 |

Advanced Topics | 369 |

15 Cointegration | 413 |

16 The Capital Asset Pricing Model | 423 |

17 Factor Models and Principal Components | 443 |

18 GARCH Models | 477 |

19 Risk Management | 505 |

20 Bayesian Data Analysis and MCMC | 531 |

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ACF plots analysis ARIMA assume autocorrelation bandwidth Bayesian bootstrap called CAPM changes coefficient cointegration components compute confidence interval copula covariance matrix curve data set defined degrees of freedom differencing equal example expected return factor model Figure forecast function GARCH Gaussian inflation rate interest rate kurtosis Ljung-Box Test log returns log-likelihood maximum likelihood MCMC multivariate nonlinear normal distribution normal plot null hypothesis outliers p-value package parametric estimates polynomial posterior prediction predictor variables prior probability Problem Quantiles T h random variable regression model resamples residuals risk risky assets sample mean Sample Quantiles scale parameter scatterplot Section Sharpe’s ratio simulated skewness spline Springer Science+Business Media standard deviation standard error stationary Statistics Suppose t-distribution tail tangency portfolio transformation variance vector volatility white noise WinBUGS yield to maturity zero