I note that this issue contains a paper on financial corruption and its link with return volatility. This broad area is one of great interest and can be summarised as to how to recognise a transaction/price that does not seem consistent with normal financial activity. This is clearly a very difficult problem, and one which we hope to publish more papers on in the future.

We are also publishing two papers on broad issues to do with downside/asymmetric returns — one looks at downside risk from an options pricing perspective, while the other considers asymmetric risk metrics. It always seems hard for newcomers in finance to understand why there are so few implementations using downside risk portfolio construction tools. The reason is that very few such tools can handle the large volume of stocks and speed necessary for optimisation/portfolio construction in high-frequency trading. This use of conventional mean variance techniques to build portfolios in hedge funds is widespread and has rather revived the popularity of optimisation in finance. We await the construction of a portfolio technique that allows us to do the same thing for asymmetric data.

One version that claims to be able to do this is based on the use of stable distributions. Without overwhelming the reader with technical details, essentially if you want to build portfolios using stable distributions, you need to make incredibly strong assumptions about the data. In particular, you need to assume that all assets have moments that exist up to some order less than two, and it is the same for every asset and hence every portfolio. Thus you are assuming that all assets do not have finite variances. This seems a fairly ridiculous assumption to have to make, just to take on board the ability to model skewness. If you want to assume finite variances in a stable framework, you are back with normality, and mean variance analysis re-emerges by default.

It is considerations such as the above that make me think we will still be doing mean variance optimisation for large portfolios in 20 years time. I will be delighted if I am proved wrong.