The performance of option strategies based on basic volatility forecasting

Abstract

The commodities and capital markets, including derivatives, are always followed by some amount of risk. One of the derivatives are options and the selection process of option strategies represents one of the most unexplored trading instruments. The aim of my research is to explore the performance of option strategies based on basic volatility forecasting and to provide the best combinations to get profit with a reasonable risk.

This research tries to understand and model factors that impact the option strategies selection. Those factors are the price of the underlying asset, the maturity and the volatility. I used the Stata software and the MatLab software to create algorithms that are implementing my emperical analaysis, my forecastings, my option strategies selection and my tests of performance. An important part of the work is the return computation of the classical option strategies based on the S&P 500, the EURO-USD exchange rate and the Yuan to USD exchange rate. I used a wide range of tests to examine the normality, the stationarity, the seasonality and the white noise of the data. Another significant part of my work is about volatility forecasting. Two main methods have been used to do it: standard deviation and GARCH family models.

The results of the all different approaches I used show that the residuals of the EURO-USD and Yuan to USD exchange rates follow a white noise. This is a characteristic of efficient market and in order to have more success with my investment process in the future, we need to seek for non-efficient markets. It has also showed the incredible outperformance of the combination of my forecasting process and the Butterfly strategy, especially when it is applied to the S&P 500.