Using option-implied information in portfolio selection and risk management

Abstract

The objective of this dissertation is two-fold. The first objective is to examine whether one can use implied information (implied volatility and implied correlations) from the options market to improve the out-of-sample performance of an all-stock optimized portfolio. Portfolio performance is measured using three metrics, namely, returns, volatility, and the Sharpe Ratio. The second objective is to examine the risk metrics of the portfolios to analyze whether a portfolio created using option-implied information is better at predicting risk than one using a conventional sample covariance matrix. This is done by calculating the portfolios VaR using a variety of methodologies. Empirically, this dissertation finds that the use of option-implied volatility when estimating the covariance matrix was able to increase the Sharpe Ratio of both constrained and unconstrained portfolios. There was no improvement to performance when option-implied correlation was added to the optimization process, thus the primary mechanism for improving performance was the ability to predict asset volatility. The risk management aspect of the dissertation provides two interesting findings. It finds that the use of a covariance matrix using option implied information is better at estimating hit rates than the sample covariance matrix. Also, there is evidence that the use of option implied information in the portfolio selection process reduces tail risk.

Date of Award	27 Apr 2021
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Pedro Barroso (Supervisor)