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The files contained in this repository were used for implementing the three models in my master thesis. My master thesis title is "Comparison of different portfolio optimization problems with different risk measures".

Jupyter Notebook 65.84% TeX 34.16%
markowitz-model risk optimization porfolio portfolio-optimization risk-measures value-at-risk average-value-at-risk entropic-value-at-risk variance

masterthesis's Introduction

Description

This folder contains all the codes that I used in my master thesis.

It contains two R-markdown files and a Python file. The R-markdown files being implemented in R-studio and the Python file being implemented in Jupyter notebook.

The Rmarkdown files are for implementation of the Markowitz model with and without risk-free asset and Integrated management formulation model with and without a risk-free asset.

Markowitz_Research_Me.Rmd

The theory of the Markowitz model doesn't need any specific package for its implementation and so we can find all the concepts for the Markowitz model in R by just using the theory itself.

Integrated_Management_Formulation_Model.Rmd

For the Integrated Management Formulation model, this is a linear model and so we use the already developed package called "linprog" in R which is suitable for handling linear problems. In this package, we use the function called "solveLP" for solving linear programs(https://www.rdocumentation.org/packages/linprog/versions/0.9-2/topics/solveLP).

Optimization with Entropic Value-at-Risk.ipynb

The Jupyter notebook is for implementation of the Entropic Value-at-Risk based model. The Entropic Value-at-Risk model is a non-linear optimization problem depending on more than one variable. The choice to use Python for this model is because there are nice packages in Python to handle non-linear optimization problems which depend on more than one variables. Such packeges include "scipy.optimize.minimize" as in the following site (https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html). This package is good for our optimization purposes since we want to minimze the EV@R and so the programming suits our objective.

Results and Discussions of the models

The results and discussions of the models are asmcontained in Chapter 6 of the thesis. Not all the results from the models are discussed but the most important results in the thesis are included.

CSV files

The CSV files in the repository are automatically created and saved by the programs themselves. So no need to worry!

HAPPY CODING!!!!!

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