Author : Simon Vuille
Publisher :
ISBN 13 :
Total Pages : 115 pages
Book Rating : 4.:/5 (129 download)
Book Synopsis A Quantitative Analysis of Cta Funds by : Simon Vuille
Download or read book A Quantitative Analysis of Cta Funds written by Simon Vuille and published by . This book was released on 2004 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our research studies various properties of commodity trading advisors (CTAs) from a quantitative point of view. Our investigation is based on a commercial database of 549 funds and focuses on the period 1990 to present. Firstly, CTAs' return distributions are analyzed and strong evidence of non-normality is found, stressing the need for portfolio allocation techniques which take into account higher-order moments.Secondly, relative persistence in return distribution parameters is studied. We find strong persistence for volatility, but fail to find significant persistence for average return or higher order moments.Thirdly, we review the major benchmarks available to the industry and build new benchmarks from our dataset. This allows us to infer the magnitude of various biases. We study homogeneity of 2 CTA subsets, namely trend-followers and non-trend-followers, and study the diversification possibilities in a CTA portfolio. In the second part of the study, we focus on linking CTAs returns with that of traditional assets. After showing that a buy and hold multi-factor linear model fails to explain CTAs returns, we point out the presence of option-like payoffs in CTAs return patterns. Trend-following CTAs exhibit straddle-like payoffs, while non-trend-followers' return patterns that are reminiscient of a long call option.Lastly, using simple trading algorithms based on moving averages, we propose a linear market model in which factors capture the dynamic nature of CTA managers' strategies. Our model leads to significant improvements over the classical model. Notably, we show that our model is able to closely replicate a broad index of CTAs for long out-of-sample periods.