Thanks I appreciate that.
As to why I arrived at such a different outcome from the referenced research paper, I’ve already hinted at a few reasons in my earlier posts, however in summary I’d suggest the following key issues …
· If you wish to examine the relationship between characteristics or risk factors and subsequent returns in commodity futures markets it’s essential that you sample this across a diverse a group of commodities as possible rather than testing a portfolio which is limited to USD denominated markets that include commodities that are almost replicas of each other when it comes to their return profile.
· 26 years of daily data may sound like a lot …. but 45 years produces quite a different outcome
· It’s important that all commodities within the testing portfolio can potentially have an equal impact on the strategy’s profitability or otherwise and so weighting allocations should be based on each market’s recent volatility rather than nominal dollar values.
· As it’s unclear exactly which methodology the authors used to construct a continuous price series for each market, it’s possible that they have not accounted for the price gaps when rolling from one contract to another. I think it’s unlikely given the authors’ combined experience in researching commodity futures, however if so, then that would certainly help to account for the large discrepancy between our results.
· The paper doesn’t disclose individual market contributions to the overall portfolio results. This is concerning as it’s possible that some of the “non-tradeable” markets such as Pork Bellies or Electricity may be the major contributors to the strategy’s profitability in the same way that UK Natural Gas dominated my own results.
As an aside I’m certainly not suggesting that the use of the skew of a market’s historical returns should not be employed within strategy development, however using it as the sole ranking measure in a rotational strategy designed to trade commodities in my opinion has no value. That said, I’d certainly be interested to hear from others as to their thoughts on the use of this relatively simple statistical measure within their strategy design process.
Meantime I’m also going to email the paper’s authors to see if they would like to offer any further thoughts as a result of the above findings.
Great stuff.
I saw that original paper and I had skewness on my list of things to look at.
There might still be something in adding it with a low weight - I'm a big believer in trading things that make sense and add diversification even if their SR is close to zero. The other thing is it might work better within asset classes - like a lot of relative value trading. So for example within ags, within energy, within metals.
My gut feeling is that skew is more interesting in financial futures, but until someone (probably you or I) checks this we won't know for sure. Here you'd definitely want to go within asset classes. Or you'd be persistently long stocks / short bonds. We know that trade hasn't worked.
Correct me if I'm wrong but are all your results using daily returns to measure skew, albeit with different lookbacks? I wonder if using weekly / monthly returns would change things. I'm worried that bad data/ microstructure does weird things to daily returns. Of course we'd end up with estimates that are more random because of fewer data points for a given lookback.
For this reason it might be better to use a more robust statistical estimate of skewness; eg a simple one is the 5% point from the returns distribution divided by the median estimate of returns minus the 95% point divided by the median.
Or it might make sense to use the more robust estimate on daily returns to remove the effect of outliers, although I am still worried that there is a weird effect which will bias their skew which even the robust estimate won't take out.
GAT