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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An Alternative Perspective on Risk Management

Much of this blog discusses portfolio risk from the perspective of exposures to market factors and measurements like VaR and tracking error. Though much is debated about their methodology and applicability, most portfolio managers monitor these risk measurements on a regular basis and use them as they see fit. I think most would agree that risk is not something we can encompass into a single number; in risk management, more is better.

Most stock scoring models I encounter are variations of a common construct: combine fundamental factors with momentum factors to generate a multi-factor score. In the continual search for alpha with low risk, a practitioner may want to consider an accounting and corporate governance factor. This often overlooked factor can have the double-impact of raising alpha while lowering portfolio risk.

In this study, I use Audit Integrity’s Accounting and Governance Risk (AGR) Score. In brief, the AGR score measures the accounting and corporate governance profile for North American and Western European stocks: companies with low scores have a higher risk of potentially fraudulent or misleading activity. This type of measurement is traditionally not measured in commercial risk models or is not easily calculated by an analyst.

In a factor test of the Russell 3000, from 12/31/2000 to 6/30/2009, the AGR factor had a statistically significant 12-month information coefficient (IC) of 0.0578. When filtered down to the bottom size quintile, the IC jumps to a significant 0.088. You could look at the AGR data across other cross sections – sectors, valuation bins, etc – and find that the score’s efficacy persists.

The potential for higher alpha portfolios is highlighted in the simulations I run below. Simulations A and B are optimized portfolios* where A uses a short term multi-factor score and B uses the Accounting and Governance Risk Score as the stock scoring measurement.
The results show simulation B having a higher alpha, lower beta, and higher overall IR. Also note the standard deviation of portfolio returns is lower when using the AGR score (i.e., less portfolio risk).

Another practical application of incorporating the AGR score is to see how the AGR can affect a multi-factor stock scoring model. The table below shows the results of portfolios an optimized portfolio using a multi-factor stock score model without (C) and with (D) the AGR incorporated in the score.

Here, despite the higher overall portfolio risk (stdev of portfolio returns), the IR is higher for the multi-factor model that includes the AGR component.

A final example I will walk through is how you can use the AGR as a stop-loss mechanism. The portfolios below were constructed using a trade-rules based simulation†. Portfolio F uses a stop-loss mechanism that sells out of positions that have fallen below acceptable AGR standards.

By using the AGR as a stop-loss mechanism, we are able to turn this negative alpha portfolio (E) into a slightly positive alpha portfolio (F). By keeping a watch on positions with respect to their accounting and risk governance rating, we are able to improve portfolio performance and reduce risk.

The simulations show there are a variety of ways to incorporate this factor into the management of the portfolio. You can gain additional insight by running performance attribution across the AGR groups to see how “very aggressive” companies contribute to your portfolio’s return. In short, adding this alternative risk measurement factor to your analysis can both diversify your stock scoring models and subsequently enhance portfolio returns.

Guest blogger Sammy Choo is Vice President of Quantitative Analytics at FactSet.

*Portfolios A, B, C and D are optimized portfolios with the following constraints:
Asset Min: Max(0,Bench Weight-.5%)
Asset Max: 1.5 x Bench Weight
Sectors: +/- 2% Bench Weight
Expected Returns: Short Term Alpha Score or AGR Score

†Portfolios E and F have the same parameters as A-D, except a rules-based engine is used and stock ranks are Short Term Alphas or AGR Score.

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A strengthening dollar will keep the smart money home

It was back in April that I wrote about how different national strategies in response to the financial crisis and recession might lead to increasing currency risk and advocated further investigation in to the potential benefits of introducing a currency hedging strategy. Four months on, we have seen a rally in equity markets of over 20%, the VIX has dropped by more than 30% to below 25, and forecast variance in developed markets has more than halved. There is a slowdown in the rate of increase in unemployment, people are talking about better than expected sales, and there seems to be a general belief that we are at the beginning of the end, as it were.


So how has all of this movement affected the distribution of risk in global equity portfolios? Consider the contribution of the three major areas of global risk and how they contribute to the overall risk of the MSCI World Index from the perspective of a USD-denominated investor. I have used the R-Squared Global Risk Model for this analysis; results are similar when generated using Barra and Northfield.

We can see from the chart above that the last 12 months have seen Currency Risk contribution increase far beyond that generated through Sector Risk, i.e., it is now more important to get your portfolios currency exposure right than to worry about your sector allocations.

Now consider the movements in the currency markets: the U.S. dollar has deteriorated ~7% against a trade-weighted basket of major currencies (source: JPMorgan & Co) with the Canadian Dollar (+13.4%) and sterling GBP (+7.6%) being the relative gainers. These are not small movements, and considering the above chart, the potential for a large correction is very real.

I therefore built a Stress Test looking at a 5% recovery in the Dollar and compared the impact on the S&P500 to the MSCI World. With the U.S. leading the world in consumption, it is not too surprising to see that the recovery of the dollar would be bad for equity markets in general, but it is interesting to see that the impact on the S&P500 is less than the MSCI World overall.

The overweighting of the S&P500 in Information Technology would have a negative relative effect with the strong Dollar affecting the returns of large exporters such as IBM, Apple, Microsoft, etc. The relative underweighting of the S&P 500 in Financial stocks (e.g., 14.9% vs FTSE 100 >21%) where through globalisation most have a large exposure to the U.S. (e.g., HSBC and Barclays), would generate a positive return, as would the Materials sector where the low exposure to commodities (all priced in USD) is a boon (i.e., no exposure to BHP Billiton, Rio Tinto, etc.)

Therefore, if the models are telling us that equity risk is predominantly a currency call and a strengthening in the dollar negatively impacts the S&P less than the rest of the world, then if I were a U.S. investor, there'd be no place like home.

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Webcast: MSCI Barra's Frank Nielsen introduces the GEM2 model

In the webcast below, MSCI Barra's Frank Nielsen details two case studies that show how the model can be used to analyze the risk and return of a fundamentally managed international portfolio.

One important aspect of GEM2 holdings-based risk analysis is the granularity available in historical analysis. This detail enables you to explain an investment process over time and guides intuition of your portfolio’s risk/return based on market events.

View the webcast for a full description and demonstration of the GEM2 model.

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