<P><B>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Nature of Financial Data </P><OL><P></P></B><P>Financial Time Series </P><P>Autocorrelations </P><P>Stationarity </P><P>Time Scales and Data Aggregation </P><P>Financial Assets and Markets </P><P>Markets and Regulatory Agencies </P><P>Interest </P><P>Returns on Assets </P><P>Stock Prices; Fair Market Value </P><P>Splits, Dividends, and Return of Capital </P><P>Indexes and "the Market" </P><P>Derivative Assets </P><P>Short Positions </P><P>Portfolios of Assets: Diversification and Hedging </P><P>Frequency Distributions of Returns </P><P>Location and Scale </P><P>Skewness </P><P>Kurtosis </P><P>Multivariate Data </P><P>The Normal Distribution </P><P>Q-Q Plots </P><P>Outliers </P><P>Other Statistical Measures </P><P>Volatility </P><P>The Time Series of Returns </P><P>Measuring Volatility: Historical and Implied </P><P>Volatility Indexes: The VIX </P><P>The Curve of Implied Volatility </P><P>Risk Assessment and Management </P><P>Market Dynamics </P><P>Stylized Facts about Financial Data </P><P>Notes and Further Reading </P><P>Exercises and Questions for Review </P><P>Appendix A: Accessing and Analyzing Financial Data in R </P><P>A R Basics </P><P>A Data Repositories and Inputting Data into R </P><P>A Time Series and Financial Data in R </P><P>A Data Cleansing </P><P>Notes, Comments, and Further Reading on R </P><P>Exercises in R </P><P></P><B><P></P></OL><P>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Exploratory Financial Data Analysis </P><OL><P></P></B><P>Data Reduction </P><P>Simple Summary Statistics </P><P>Centering and Standardizing Data </P><P>Simple Summary Statistics for Multivariate Data </P><P>Transformations </P><P>Identifying Outlying Observations </P><P>The Empirical Cumulative Distribution Function </P><P>Nonparametric Probability Density Estimation </P><P>Binned Data </P><P>Kernel Density Estimator </P><P>Multivariate Kernel Density Estimator </P><P>Graphical Methods in Exploratory Analysis </P><P>Time Series Plots </P><P>Histograms </P><P>Boxplots </P><P>Density Plots </P><P>Bivariate Data </P><P>Q-Q Plots </P><P>Graphics in R </P><P>Notes and Further Reading </P><P>Exercises </P><P></P><B><P></P></OL><P>3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Probability Distributions in Models of Observable Events </P><OL><P></P></B><P>Random Variables and Probability Distributions </P><P>Discrete Random Variables </P><P>Continuous Random Variables </P><P>Multivariate Distributions </P><P>Measures of Association in Multivariate Distributions </P><P>Copulas </P><P>Transformations of Multivariate Random Variables </P><P>Distributions of Order Statistics </P><P>Asymptotic Distributions; The Central Limit Theorem </P><P>The Tails of Probability Distributions </P><P>Sequences of Random Variables; Stochastic Processes </P><P>Diffusion of Stock Prices and Pricing of Options </P><P>Some Useful Probability Distributions </P><P>Discrete Distributions </P><P>Continuous Distributions </P><P>Multivariate Distributions </P><P>General Families of Distributions Useful in Modeling </P><P>Constructing Multivariate Distributions </P><P>Modeling of Data-Generating Processes </P><P>R Functions for Probability Distributions </P><P>Simulating Observations of a Random Variable </P><P>Uniform Random Numbers </P><P>Generating Nonuniform Random Numbers </P><P>Simulating Data in R </P><P>Notes and Further Reading </P><P>Exercises&nbsp;</P></OL><P><STRONG>4.</STRONG>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; St<B>atistical Models and Methods of Inference </P><OL></B><P>Models </P><P>Fitting Statistical Models </P><P>Measuring and Partitioning Observed Variation </P><P>Linear Models </P><P>Nonlinear Variance-Stabilizing Transformations </P><P>Parametric and Nonparametric Models </P><P>Bayesian Models </P><P>Models for Time Series </P><P>Criteria and Methods for Statistical Modeling </P><P>Estimators and Their Properties </P><P>Methods of Statistical Modeling </P><P>Optimization in Statistical Modeling; Least Squares and Other Applications</P><P>The General Optimization Problem </P><P>Least Squares </P><P>Maximum Likelihood </P><P>R Functions for Optimization </P><P>Statistical Inference </P><P>Confidence Intervals </P><P>Testing Statistical Hypotheses </P><P>Prediction </P><P>Inference in Bayesian Models </P><P>Resampling Methods; The Bootstrap </P><P>Robust Statistical Methods </P><P>Estimation of the Tail Index </P><P>Estimation of VaR and Expected Shortfall </P><P>Models of Relationships among Variables </P><P>Principal Components </P><P>Regression Models </P><P>Linear Regression Models </P><P>Linear Regression Models: The Regressors </P><P>Linear Regression Models: Individual Observations and Residuals</P><P>Linear Regression Models: An Example </P><P>Nonlinear Models </P><P>Specifying Models in R </P><P>Assessing the Adequacy of Models </P><P>Goodness-of-Fit Tests; Tests for Normality </P><P>Cross Validation </P><P>Model Selection and Model Complexity </P><P>Notes and Further Reading </P><P>Exercises </P><P></P><B><P></P></OL><P>5.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Discrete Time Series Models and Analysis </P><OL><P></P></OL></B><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Basic Linear Operations </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Backshift Operator </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Difference Operator </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Integration Operator </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Summation of an Infinite Geometric Series </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Linear Difference Equations </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Trends and Detrending </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Cycles and Seasonal Adjustment </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Analysis of Discrete Time Series Models </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Stationarity </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Sample Autocovariance and Autocorrelation Functions; Estimators</P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Statistical Inference in Stationary Time Series </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Autoregressive and Moving Average Models </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Moving Average Models; MA(q) </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Autoregressive Models; AR(p) </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Partial Autocorrelation Function (PACF) </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ARMA and ARIMA Models </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Simulation of ARMA and ARIMA Models </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Statistical Inference in ARMA and ARIMA Models </P><P>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Selection of Orders in ARIMA Models </P><P>&nbsp;&nbsp

Description based on CIP data; resource not viewed.