Modeling Liquidity and Income in Modern Portfolios Todd E Petzel, CIO Offit Capital Advisors

1. Modeling Liquidity and Income in Modern Portfolios Todd E Petzel, CIO Offit Capital Advisors QWAFAFEW New York March 24, 2009

2. Outline of Presentation • Traditional Models and Assumptions • Incorporating Liquidity and Income Theoretically • Practical Issues and Approaches

3. Traditional Approaches • Linear or non-linear optimization in return space • Monte Carlo simulation in return space • “Total Return” spending rules • Major implicit assumption: portfolio adjustments are frictionless and costless

4. Optimizers have multiple problems • Thousands of data points; one history • Corner solutions are the norm • “Solutions” more likely to reflect constraints than truth

5. Monte Carlo is supposed to cure these issues • Thousands of simulations, but based on same history • Distribution of outcomes versus a single expected characterization

6. Monte Carlo approach still has severe issues • Covariance assumptions are subject to abrupt changes • Path dependency is fairly rudimentary • Still backward looking

7. December 31, 2008

8. June 30, 2008

9. Total Return Spending Rules • The exception rather than the rule 40 years ago • Assumes sufficient liquidity to create payments from portfolio and to rebalance • Ignores actual operations side of enterprise and covenants

10. Private Equity Simulation Rules • First cousin to Monte Carlo portfolio analysis • Used to plan transition to “long-term” portfolio containing illiquid partnerships • Usual conclusion: Over allocate to illiquid partnerships in order to reach goal • Keep money in equities while waiting for calls

11. Major Unstated Assumptions • Bull markets provide early distributions and funding sources for following calls • There will always be enough liquid securities to sell when capital calls appear • Simulations based on a decidedly bull market history

12. Reality in 2008 • PE obligations slowed down, but still remain dollar liabilities to the investor • Intended source of funding hammered by bear market • Liquid securities have been sold down to meet regular spending and capital calls • Major institutions borrow to pay bills

13. Where do Liquidity and Income Fit In? • In the traditional approaches there is no difference between liquid and illiquid investments, or between income and total return • Recommendations for illiquid private investments are usually only bounded by initial constraints

14. How to Improve the Models • Don’t maximize wealth, maximize utility • U = f(W, L, I) [Wealth, Liquidity, Income] • Downward sloping marginal utility of all factors • Upward sloping transactions costs associated with less income or liquidity • Higher opportunity costs of more income and liquidity

15. Conceptually This Isn’t Too Difficult • Problems arise in execution • Do organizations understand their marginal utilities of liquidity and income in good times? • Very similar problem to estimating the marginal utility of storage between times of full inventories and shortages.

16. Practical Approaches • Throw away your total return spending rule • Integrate the operational budget and investment processes • Understand how much cash you’ll need in the near term

17. Practical Approaches II • Split the portfolio into two components: • Sleep well at night money • Long-term portfolio • Try to cover cash needs with income producing assets • If that is not possible, decrease illiquid assets to lower impact of asset sales

18. Practical Approaches III • Forecast future capital calls • Set aside “sleep well at night money” for these liabilities extending some period • Do not over allocate to partnerships to try to build up positions quickly

19. Conclusions • Inability to properly model income or liquidity benefits skewed portfolio construction toward higher risks • Too many institutions are revisiting these topics now after suffering permanent losses • Ad hoc rules are better than inadequate models