Abstract
Using a unique dataset of daily returns of 89 programmes of Commodity Trading Advisors (CTAs), we investigate the distributional properties of CTA strategies including trend following, fundamental and contrarian strategies. We find that daily data exhibits strong features of fat-tail, volatility clustering, and long memory in volatility. This is different from previous studies which are often based on monthly data. Our study contributes to the literature of stylized facts of financial markets, it also provides insights to practitioners because the information from monthly data might be misleading.
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