I walk slowly, but I never walk backwards. Have you moved forward with your risk awareness?

The internet is a huge and seemingly all-knowledgeable place sometimes. When I went looking for a quote using the word "slowly," I not only found the the above by American President Abraham Lincoln, but also a whole 45 minutes-worth of other interesting reading from a huge breadth of people and eras, including this famous and rather apt line from Charles MacKay:

"Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, and one by one."

There was a consistency in many of the quotes, including these two, underlining the recognition that learning and acceptance can be a slow process which panic and ignorance can quickly offset, but that it is one that we must all stay committed to.

What topic is it that brings me to this introduction? That it is now one year since we saw the huge downward movements in the markets, I want to summarise and comment on what I have seen in terms of change in portfolio risk management over those last 12 months.

Risk Model Providers
Many of the risk models that people were using took some criticism in October/November last year as their models were considered to be slow to react. This in turn lead to the emergence of shorter horizon models (R-squared, Barra, Northfield, etc.) and practitioners were encouraged not to replace their current models, but to complement them with the additional analysis that could now be generated. This parallel analysis can give confidence in markets where the horizons are shifting.

Modelling Techniques
Stress-testing is another complementary analysis technique that got plenty of air-time (not least in this blog) as people have looked to forecast the impact on portfolios of certain market changing events, both historical (e.g., Internet bubble, Rouble crisis) and modeled (e.g., Oil to $200). There has also been work on the incorporation of "fat-tails" into the forecasting models and how they can directly affect the outcome of tests such as the commonly-used 10 day 99% VaR limit. Monte Carlo techniques for analysis of the whole distribution now give us further measure such as Expected Tail Loss and CVaR.

Attribution
When AUM has fallen by >40% it can come across as a little disingenuous to point out that a portfolio outperformed its benchmark by a few points and attribution was probably not the tool at the forefront of people's minds. The subsequent rally experienced since April has however brought it back to centre stage, and the ability to combine risk attribution with the more customary allocation-based methodologies (e.g., Brinson) for both contrast and measurement continues to win approval.

Third Party Commentary
There has been much conjecture following on from last year regarding what, if any, new regulation will be implemented. The criticism of VaR, or at least the over-reliance on it as a single risk measure, was just one high profile area of discussion. The need to improve the granularity, frequency, and depth of reporting is another. The Tower Group recently published a report on risk analysis budgeting in the industry (a summary of which can be heard here) highlighting the expectation of all participants that an increase is on the cards. They report that they expect budgets of IT spend as a whole to remain fairly flat but that the allocation of those budgets towards the understanding of risk will rise markedly.

Summary
So what have we been seeing with our clients? The drop in the market from September last year brought a reduction in AUM which, not surprisingly, was seen in reduced fees and therefore a reluctance to commit to new risk spending. There has been a large amount of general interest picking up in the new models and new implementation techniques. We have seen some clients embrace new analysis and reporting for their business units combining several of the above points. But most are undecided and unwilling to commit to change, perhaps balancing the recognition that things have to change with demanding investment into an area seen as a necessary evil rather than the necessary toolset that I believe risk analysis to be.

What are you doing? Which direction do you believe improvement in risk awareness will come from? Are you moving forward? In the financial landscape of the moment there seem to be two major players, and as I started with a quote from an American about always looking to progress, I will finish with one from a Chinese man, Confucius:

"It does not matter how slowly you go as long as you do not stop."

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FactSet's Industry Spotlight webseries kicks off with Emerging Markets 2009

FactSet's monthly webseries features expert speakers from a variety of industries. Each month, we will explore a different topic impacting the markets, with live commentary and insight from standout thought leaders in areas of interest, including discussions on emerging markets, healthcare, the changing economy, and more.

The series kicks off September 16 with "Emerging Markets: A 2009 Update." Led by MSCI Barra's Frank Nielsen, this presentation highlights the evolution and characteristics of emerging markets over the past 20 years. Nielsen will revisit key issues related to Emerging Markets, including the evolution of Emerging Markets over the last two decades and examine the various drivers of risk and return for these markets during that period. Neilsen will also discuss the performance and risk of Emerging Market investments over the last two years.

Register for this or other webcasts at www.factset.com/spotlight.

Frank Nielsen is Executive Director and Head of Applied Research for the Americas at MSCI Barra. His main responsibilities include managing and enhancing developed and emerging market equity indices for the Americas region and conducting applied research on clients' investment and risk management processes leveraging the MSCI Barra index and risk analytics. Since joining Barra in 1993, Mr Nielsen has held various positions in product management, enterprise risk management, and equity research. Prior to joining Barra, Mr Nielsen worked for Hypo-Vereinsbank in Germany as a security and credit analyst. Mr Nielsen has an MBA from the University Hamburg in Germany and is a CFA charterholder.

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What a difference a year makes

Here we are in September 2009, awaiting a whole raft of articles to be published, TV documentaries to be aired, and legislation to be recommended, all of it focused on and in response to what happened across the financial markets 12 months ago. I therefore feel excused from having to comment upon it myself.

What I would like to highlight is the impact that the last 12 months has had on the some of the statistics that we rely on when it comes to measuring risk, and show how even some very simple models have changed hugely through incorporating the new data. I also want to highlight how an incomplete presentation of these statistics can have huge implications on our understanding.


One of the more (in)famous quotes of last year is from David Viniar, Goldman’s chief financial officer, who said, "We were seeing things that were 25-standard deviation moves, several days in a row." I do not intend to add further to the large amount of predominantly critical commentary focused on these particular words, but I did think that they provide a basic framework to work from. Now while David was no doubt referring to the short-horizon movements of a particular asset that Goldman held , for simplification I will consider monthly movements of a general index, the S&P 500. The principle is exactly the same, but by doing this I sidestep both the issues of identifying the asset, as well as the well documented issues of using daily data.

The two datasets used in comparison are the 60 monthly returns up to August 2008 (i.e., the five years prior to last year's crash) and the 60 monthly returns up to August 2009. I have selected 60 months, as this is the horizon used in most long term risk models, and if we look at these returns against a normal background we get the following chart:

While there is some obvious kurtosis, skew is minimal and the normal assumption does not seem extreme. Assuming the normal distribution, the descriptive statistics for these distributions are

The reduction in the average return reflects not only the recent downturn but also excludes the postive market run through 2004. The big change though is in the standard deviation of those returns, the value almost doubling.

Paraphrasing David Viniar, we see that the realised return of September 2008, a month that saw the S&P500 fall 9.08%, was a 3.5 standard deviation event as of August 2008, but only a 2 standard deviation event were it to happen in September 2009. These numbers are much more digestible than the 25 deviations he was talking about, but do we really appreciate the difference between 3.5 and 2 deviations?

If we accept the normal distribution for our model, then the September 2008 return was a once in 345 year event from the point of view of someone last year, while using all the data that we have today shows that it would be expected to occure every 3.5 years, a difference of a hundredfold. In actual fact the S&P500 Index has delivered return of less that -9.0% on three occasions over the last 12 months, suggesting that this multiple is even too low!

In summary, statistics are calculated using the data available and report descriptive values. Depending on how those values are framed when they are reported can have a huge impact on risk understanding. Shouldering the burden of improving the understading of risk, we must all take care and resist the urge to throw out even simple statistics without any accompanying education.

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