Skip Ribbon Commands Skip to main content
Contact Us | Sitemap Welcome Sign In
Investor Resources  

Portfolio Analytics

We believe that successful investing requires a blend of quantitative and qualitative analysis.  Our commingled funds and separate accounts are constructed with investment rigor.  We utilize both internally and externally-developed tools and models that enable us to monitor client portfolios.  Principal among these tools are:

  • The Commonfund Allocation Planning Model® (APM)
  • RiskMetrics

Commonfund’s Allocation Planning Model (“APM”)
The Allocation Planning Model is a proprietary model developed and enhanced internally with assistance from external resources, including a number of leading academicians and other experts.  Our APM is a financial simulation tool that we utilize to assist our clients with the asset allocation decision.  With this tool, we help our clients understand the expected outcomes and potential risks of selected asset classes and the interrelationships of those asset classes.  This tool helps us think about how changing, adding, or removing an allocation to any given asset class will affect the risk/return profile of a portfolio on a forward-looking basis.

The APM is a forward-looking, yield curve-based model that simulates potential future economic scenarios and asset class returns within those economic scenarios.  The APM helps investors examine portfolio choice alternatives under different conditions of economic uncertainty on a forward-looking basis.  Spending/payout policies are important considerations in decision-making and are also incorporated into the APM.  By incorporating cash flows in the model in the form of spending, distributions, or grants, investors are able to understand the long-term ramifications of asset allocation, spending and cash flow decisions.

The APM is a model that is based on the term structure of interest rates.  We believe that the investment returns of the asset classes included in the model have been and will continue to be a function of the economic environment, in particular, changes in the yield curve.  Our model takes the current yield curve, uses Monte Carlo simulation to project 1,000 different yield curves for next year by changing economic factors that affect the curve, and projects returns for each of 20 asset classes in each of the “new” yield curve environments.  The projected returns are based on the regression of the historical relationship between these asset classes and the yield curve.  The model then takes each of the 1,000 “new” yield curves as the next starting point and repeats the process, building another 1,000 yield curves, and projecting returns in those environments.  In order to have the ability to focus on the long term, the model runs these simulations for 20 years into the future.  Fundamentally there are two core processes at work in the APM: defining the asset classes in terms of their historical relationship to the yield curve and projecting the returns of those asset classes in 1,000 different future economic scenarios for each year.

The APM has many advantages over mean variance optimization.  In addition to generating a distribution of potential outcomes and different economic scenarios (which cannot be accomplished with mean variance optimization), the APM’s term structure model has advanced features that distinguish it from most other forecasting models that use Monte Carlo simulation.  Ultimately, the power of a model that incorporates Monte Carlo simulation, unlike mean variance optimization, lies in the ability to produce a range of returns and generate meaningful statistical analysis from the distribution.  With historical-based inputs and/or user inputs, a mean variance optimization model can produce an efficient frontier along which reside optimal portfolios for a given expected return and standard deviation.  The APM, in contrast, considers asset allocation from the user’s perspective and then generates projected returns, standard deviations, distributions, and probabilities associated with that asset allocation.  With this type of analysis, the user is able to understand the likelihood of achieving goals rather than merely focusing on a median and standard deviation of an “optimal” portfolio produced by a mean variance optimization.

IMPORTANT: The projections or other information generated by the Allocation Planning Model™ regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investments and are not guarantees of future results. Results may vary with each use and over time. See APM Explanatory Notes at the end of this presentation.



Commonfund monitors and controls the volatility, concentration, and potential performance deviations from benchmark targets using market risk software. Commonfund is currently using MSCI’s RiskManager ™ and HedgePlatform™ software for market risk measurement and stress testing. RiskManager and HedgePlatform provide market exposures and sensitivities across a broad range of instruments including, Commodities, Equities, Fixed Income, FX, Mortgages and Structured Credit, using multiple Value at Risk (VaR) methodologies and flexible stress-testing.