Not logged in.

Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Learning to Manage the Risk that Matters
Organization Unit
Authors
  • Paolo Pace
Supervisors
  • Erich Walter Farkas
  • Patrick Matei Lucescu
Language
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Number of Pages 87
Date September 2021
Abstract Text The objective of the thesis is to investigate a one-paramenter family of risk measures called Conditional Drawdown at Risk (CDaR) and its application to Equal Risk Budgeting Portfolios and Machine Learning techniques for Asset Allocation. We first introduce and develop a theoretical framework for the Conditional Drawdown at Risk. The next step will be thorough study of the properties of Equal Risk Contribution and their drawbacks, that will lead to the formulation of an optimization problem based on the CDaR. The Hierarchical Clustering approach applied to portfolio allocation has been of interest for many years has it seemed to tackle what was commonly known as the Markowitz' curse. More recent studies implement techniques to improve the performance of this strategy by computing beforehand the optimal number of clusters and adopting different linkage methods. The use of a tail risk measure, like CDaR, instead of the volatility, defined as the standard deviation, will be tested in this context too. We produce a series of strategies based both Equal Risk Contribution and Hierarchical Clustering algorithms that we will backtest against traditional portfolios, namely Minimum Variance, Maximum Sharpe Ratio and Equally Weighted. The statistical significance of the risk adjusted performance of the proposed strategies will need to be evaluated in order to understand if outperfomances were produced by chance. Finally we will try to reconcile our finding within the modern portfolio theory framework.
Export BibTeX