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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Manipulating the Payoff Function

Given the recent market volatility and cash constraints that many asset managers face, financial engineers are looking for ways to make options cheaper (and more customized). For example, you may want to simply change the contract parameters, such as the time to maturity or the strike price. More elaborate schemes may involve changing the reference index to a high dividend yielding stock, which puts a break on the upward movement of a call option, or referencing a basket of (uncorrelated) indices, which will reduce the potential payoff through a reduction in volatility.

One way to make options cheaper is by changing the payoff function. Changing the payoff function can be done, among other ways, by linear segmentation, such as the introduction of a second strike. Using a simplistic example, if an investor believes the market will go up by ~10% for a particular security, a product could be created by simply going long a call at $163.39 (K₁ ) which is at-the-money, and short a call at $179.73 (K₂ ), thus resulting in a segmented payoff function (or bull call spread), where and are the premiums, respectively. The short call is used to subsidise the premium of the long call.




After running this bull call spread through FactSet’s Monte Carlo VaR, I can see that the loss is capped at the difference between the premiums (in this example, $2). Also interesting to note is that when I close the short position, I can see that the distribution has a maximum loss of $4 (this is the post-trade distribution), which is the premium of the long call, and there is a higher chance of making a gain.





You don't need an engineer to create the above; just enter into two different contracts at two different strikes. From an engineering perspective, the payoff can be segmented any number of ways to match your desired payoff, which will ultimately be based on your view of the market. You can even integrate partial call spreads if you want to take part in an up market, but your conviction is not strong (the upside is positively sloped and not capped).

Later posts will involve manipulating the payoff function and other techniques to make options cheaper.

Guest blogger Mike Joel is a FactSet Portfolio Analytics specialist in London.

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