Quick start | Usage of the package | Preliminary theory | Datasets | Fitting | Decomposition | Forecasting | Next steps | References

Fast Design of High-Order Portfolios | Quick Start | What is a High-Order Portfolio? | Signal model | Modern portfolio theory | From mean-variance portfolio to high-order portfolio | High-order portfolios | Using the Package highOrderPortfolios | Estimate the high-order sample moments | Fit a multivariate skew \(t\) distribution | MVSK portfolio | MVSK tilting portfolio | References

Table of Contents | Installation | Quick Start | Usage of the package | Datasets | Fitting a Gaussian AR(1) model | Fitting a Student’s \(t\) AR(1) model | Fitting a Student’s \(t\) VAR model | Imputation of missing values from Gaussian AR(1) model | Imputation of missing values from Student’s t AR(1) model | Removing outliers | Comparison with other packages | Algorithms | Parameter estimation | Imputation | References

Mean Vector and Covariance Matrix Estimation under Heavy Tails | Installation | Quick Start | Numerical Comparison with Existing Packages | Extension to Skewed Distributions | Algorithms | References

Markowitz Portfolio | Risk Parity Portfolio | Package riskParityPortfolio

​ | Introduction | Warm-Up: Markowitz Portfolio | Risk Parity Portfolio | Conclusions

Fast Design of Risk Parity Portfolios | Quick Start | What is a Risk Parity Portfolio? | Signal model | Modern Portfolio Theory | From “dollar” to risk diversification | Risk parity portfolio | Solving the Risk Parity Portfolio (RPP) | Naive diagonal formulation | Vanilla convex formulation | General nonconvex formulation | Using the Package riskParityPortfolio | Modern Risk Parity Portfolio | RPP with additional expected return term | RPP with additional variance term | RPP with general linear constraints | A pratical example using FAANG price data | Comparison with other Packages | Appendix I - Risk concentration formulations | Appendix II - Numerical algorithms for the risk parity portfolio | Algorithms for the vanilla risk parity formulation | Successive convex approximation algorithm for the modern risk parity formulation | Appendix III - Computational time | References

Table of Contents | Package Snapshot | Quick Start | Installation | Loading Data | Basic structure of datasets | Obtaining more data | Expanding the datasets | Defining Portfolios | Backtesting and Plotting | Backtesting your portfolios | Result format | Shaping your results | Plotting your results | Advanced Usage | Transaction costs | Incorporating benchmarks | Parameter tuning in portfolio functions | Progress bar | Parallel backtesting | Initialization for each backtest | Tracing where execution errors happen | Backtesting over files: usage for grading students | Leaderboard of portfolios with user-defined ranking | Example of a script file to be submitted by a student | Appendix | Performance criteria | References

Design of Portfolio of Stocks to Track an Index | Comparison with other packages | Usage of the package | Explanation of the algorithms | spIndexTrack(): Sparse portfolio construction | References

Comparison with other packages | Usage of the package | Explanation of the algorithms | References

Comparison with other packages | Usage of the package | Computation of sparse eigenvectors of a given matrix | Covariance matrix estimation with sparse eigenvectors | Complex-valued inputs | Explanation of the algorithms | spEigen(): Sparse eigenvectors from a given covariance matrix | spEigenCov(): Covariance matrix estimation with sparse eigenvectors | References