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This course is part of these tracks:

Kris Boudt
Kris Boudt

Professor of Finance and Econometrics at VUB and VUA

Kris Boudt is a Professor of Finance and Econometrics at Vrije Universiteit Brussel and Amsterdam. Since 2011, he is the research partner at Finvex, a leading financial investment designer. Kris Boudt is an expert in portfolio analysis and has contributed to the development of several smart beta equity indices and has published his research in the Journal of Portfolio Management, Journal of Financial Econometrics and the Review of Finance, among others. He has a passion for developing financial econometrics tools in R and is a coauthor of the highfrequency, PeerPerformance and PortfolioAnalytics R packages.

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  • Lore Dirick

    Lore Dirick

  • Josiah Parry

    Josiah Parry

Course Description

A golden rule in investing is to always test the portfolio strategy on historical data, and, once you are trading the strategy, to constantly monitor its performance. In this course, you will learn this by critically analyzing portfolio returns using the package PerformanceAnalytics. The course also shows how to estimate the portfolio weights that optimally balance risk and return. This is a data-driven course that combines portfolio theory with the practice in R, illustrated on real-life examples of equity portfolios and asset allocation problems. If you'd like to continue exploring the data after you've finished this course, the data used in the first three chapters can be obtained using the tseries-package. The code to get them can be found here. The data used in chapter 4 can be downloaded here.

  1. 1

    The building blocks


    Asset returns and portfolio weights; those are the building blocks of a portfolio return. This chapter is about computing those portfolio weights and returns in R.

  2. Analyzing performance

    The history of portfolio returns reveals valuable information about how much the investor can expect to gain or lose. This chapter introduces the R functionality to analyze the investment performance based on a statisical analysis of the portfolio returns. It includes graphical analysis and the calculation of performance statistics expressing average return, risk and risk-adjusted return over rolling estimation samples.

  3. Performance drivers

    In addition to studying portfolio performance based on the observed portfolio return series, it is relevant to find out how individual (expected) returns, volatilities and correlations interact to determine the total portfolio performance.

  4. Optimizing the portfolio

    We have up to now considered the portfolio weights as given. In this chapter you learn how to determine in R the portfolio weights that are optimal in terms of achieving a target return with minimum variance, while satisfying constraints on the portfolio weights.