Learn how to calculate, analyze and plot simple and continuously compounded returns in R.

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2

Random variables and probability distributions

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Learn how to work with probability distributions in R in the context of return and value-at-risk calculations.

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3

Bivariate distributions

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Explore bivariate probability distributions in R.

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4

Simulating time series data

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Learn how to use R to simulate autoregressive and moving average processes.

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5

Analyzing stock returns

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Learn how to analyze stock returns with the R packages PerformanceAnalytics, zoo and tseries.

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6

Constant expected return model

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Estimate parameters of the constant expected return (CER) model, compute standard errors and confidence intervals and test various hypotheses about the parameters and assumptions of the model. Perform bootstrapping of CER model estimates.

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7

Introduction to portfolio theory

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Compute portfolios that consist of Boeing and Microsoft, T-bills and Boeing, T-bills and Microsoft and T-bills and combinations of Boeing and Microsoft. Use R functions to compute the global minimum variance portfolio and the tangency portfolio.

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8

Computing efficient portfolios using matrix algebra

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Using the monthly closing price data on four Northwest stocks, you will estimate expected returns, variances and covariances to be used as inputs to the Markowitz algorithm. You will compute the global minimum variance portfolio, efficient portfolios, and the tangency portfolio for short-sales allowed and for short-sales not allowed.