There has been a lot of talk in the press following the credit crisis, Bear Stearns and Soc Gen about risk management and its role in our industry. Last month saw articles by Alan Greenspan and Nout Wellink talking about the need for banks and regulators to be more "forward-looking"; advocating more use of simulation rather than relying on historical calculation. This sounds ideal but there are some intrinsic issues here.

Although it has taken some time (arguably changes should have happened in 1998 following the collapse of LTCM) the financial world is rightly discussing how the industry should be managing its risks. The problem is however much deeper than changing focus from an historical to a more "forward-looking" approach.

Currently most banks, hedge and institutional funds use VaR as the benchmark analytic for assessing risk exposure. Herein lies the first issue. VaR is based on a normal Gaussian distribution which does not reflect the reality of how the financial markets actually work. You may have heard of the phrase "fat-tails" - meaning that markets appear to display kurtosis risks and that events that should occur one in a million (or higher) years under that model actually happen all too frequently.

The second issue is that all this is based upon historical data - and again the markets have a nasty habit of deviating from past behaviour. In the LTCM crisis even the models built by the greatest minds in finance (Scholes and Merton) were not able to predict the intricate correlation of disparate markets following the Russian bond default.

Thirdly is the problem of technology. All of the above require massive processing power that only the largest organisations can really afford. Most institutions systems grow organically from the departmental level - and thus weaving together all of that data to give you an enterprise-wide view of your risk exposures is by no means an easy thing.

So can we solve this? Yes I think so - but not in the near future.

If we are to replace VaR as the most widely used analytic, you first have to find a replacement, have it accepted over time by practitioners and build systems to actually do it. There is no acceptance of alternatives at the moment - the most compelling one that I have seen is Benoit Mandelbrot's Fractal Theory of the markets (a view shared by the maverick option trader and author Nassim Taleb). If we are to move towards prediction then you also need an accepted way of (mathematically) predicting the future market movements. Many banks have been trying to incorporate neural networks into their infrastructure in a bid to understand the biological complexity of the markets - but we have not solved this yet nor I think are ever likely to. Simulation is a great concept; knowing what and how to simulate future events is the problem. Lastly more focus (or rather money) needs to be put into Risk Management departments and systems that in some areas have been neglected in recent years and the current debate will surely ensure this happens. Once the good times start to roll again however, you can be certain that this will again take a back seat.

So to answer the question of who should be doing this, yes all market participants will need to change once we find the answers. But it is the banks (the sell-side) who need it the most; the buy-side is by its very nature a much more cautious beast. What is needed most to my mind is actually agreement by all parties about what we need to do and where we need to go to. My advice to you is to await the Corrigan report (due in July) to see what the industry will do to face this challenge after the recent credit crisis.