Evaluation of Commodity Trading Advisors using fixed and variable and benchmark models

Abstract

This paper examines the performance of Commodity Trading Advisors (CTAs) using fixed and variable benchmarking models. In order to avoid the troublesome passive and active commodity and managed futures benchmarks (indices) when examining the performance of CTAs, we innovate by using data envelopment analysis (DEA). Because this alternative class has non-linear returns due to long/short positions, and derivatives (i.e., dynamic trading strategies), DEA can alleviate the problems usually associated with these indices. The effectiveness of using benchmarking models in a DEA setting will provide investors with an alternative technique in assessing the performance and identifying efficient CTAs.

Performances of socially responsible investment and environmentally friendly funds

“…We apply dynamic mean-variance model using shortage function of Briec and Kerstens (2009). Previous studies such as Funari (2001, 2005), Bauer et al (2005), Daraio and Simar (2006), Gregoriou and Chen (2006), and Lin and Chen (2008) analyze mutual fund performance but there remain several problems to be solved. Comparing to previous studies, therefore, we contribute to the literature on the financial evaluation of funds in three ways: 1) our analysis considered performances in the risk-adjusted sense, 2) we measured efficiency using only applicable funds, not benchmarks, 3) it can define each fund's "projection" on the efficient production frontier to not only locate ill-performing (inefficient) funds but also to determine the degree and causes of their inefficiencies, and 4) application in SRI and EF funds.…”

Assessing the performance of alternative investments using non-parametric efficiency measurement approaches: Is it convincing?

“…Gregoriou (2006a) presented a case study on the performance of CTAs using the same DEA models as Gregoriou et al (2005b), while Gregoriou (2006b) analyzed the largest US mutual funds using a CCR model and CRS cross and super efficiency models. Gregoriou and Chen (2006) extended previous performance evaluation studies by applying fixed and variable input-oriented benchmark models under VRS. Eling (2006) went into the problem of input-output and model specification in DEA and illustrated the findings using empirical data on hedge funds by running 10 different DEA models which differed in their input-output specification and the RTS assumptions.…”

Managed Futures for Long‐Term Investors: A DEA Ranking Analysis

“…The Gregoriou and Chen (2006) DEA model uses these inputs: (i) the monthly average standard deviation; (ii) the semi‐deviation that takes into consideration only data from months with negative rate of return; (iii) the percentage of negative monthly returns; and (iv) the number of months it takes the CTA to recover from the largest maximum drawdown. The outputs are: (i) the compounded return; and (ii) the percentage of positive monthly returns.…”