1. Quantitative methods in Hedge Fund of Fund construction By Peter Urbani, CIO Infiniti-Capital

2. Weaknesses of models used to analyse Hedge Funds “ Models currently used to analyze hedge funds generally display a number of major weaknesses: The models do not pay sufficient attention to the asymmetry of hedge fund returns (hedge funds returns are not normally distributed). VaR type models therefore do not measure risk accurately. The models do not correct for the presence of widespread auto-correlation causing significant understatement of volatility of hedge fund returns. Benchmarks used are not often significant resulting in spurious comparisons. The models do not consider the impact of asymmetry on dependence measures such as correlation. The models do not consider the persistence of any alpha. The models generally seek to condense all of the relevant detail into one single standardized comparative number that is frequently meaningless. The weaknesses in existing models mean that the unique characteristics of hedge funds and risks are not captured.” Satyajit Das Author of Traders Guns and Money – p28, Wilmott Magazine August 2007

4. Hedge Funds v.s. Hedged Funds A Perfectly ‘Hedged’ fund Fund Returns -ve Equity Returns +ve

6. ‘ Perfect ’ vs. MSCI Daily TR Gross World Free USD, for 31-Jan-93 to 31-Mar-07 Note Asymmetric payoff

7. Avg HF vs. MSCI Daily TR Gross World Free USD, for 31-Jan-93 to 31-Mar-07 Note Asymmetric payoff

8. Less than 12% of Hedge Funds ‘Normally’ distributed Based on analysis of 5400 Hedge Fund distributions

10. (i)Liquidity a Source of Alpha ? Relationship between liquidty and Returns Our research indicates that longer lock-ups are compensated for by additional alpha of 300 – 400bp per annum

12. Infiniti SFA Risk score Amaranth First Warning signal 31 May 2005 Second Warning signal 30 April 2006 Outright Sell signal 31 May 2006

13. Significant deviation as distribution type changes in April / May 2005 Infiniti ‘Best Fit’ Value at Risk (VaR) Amaranth

14. Analysis of Classic Correlation (top Right Quadrant) and Modified Correlation (bottom Left Quadrant) of sample Portfolio 0.486 0.428 0.548 0.313 0.238 0.589 0.601 0.470 0.387 0.476 0.553 0.695 0.306 0.249 Fund 1 vs Fund 2 0.629 Fund 1 vs Fund 3 0.651 Fund 1 vs Fund 4 0.629 Fund 1 vs Fund 5 0.633 Fund 2 vs Fund 3 0.537 Fund 2 vs Fund 4 Fund 2 vs Fund 5 Fund 3 vs Fund 4 Fund 3 vs Fund 5 Fund 4 vs Fund 5 0.357 0.522 Portfolios 95% Normal VaR = -0.77% Portfolios 95% Modified VaR = -0.82% Fund 1 Fund 2 Fund 3 Fund 4 Fund 5 Fund 1 1 0.629 0.651 0.357 0.633 Fund 2 1 0.537 0.486 0.428 Fund 3 1 0.548 0.313 Fund 4 1 0.238 Fund 5 1 0.589 0.601 0.470 0.387 0.476 0.553 0.695 0.522 0.306 0.249

15. Linear Analysis of sample Portfolio 0.486 0.428 0.548 0.313 0.238 Fund 1 vs Fund 2 0.629 Fund 1 vs Fund 3 0.651 Fund 1 vs Fund 4 0.629 Fund 1 vs Fund 5 0.633 Fund 2 vs Fund 3 0.537 Fund 2 vs Fund 4 Fund 2 vs Fund 5 Fund 3 vs Fund 4 Fund 3 vs Fund 5 Fund 4 vs Fund 5 0.357 Portfolios 95% Normal VaR = -0.77% Pearson Correlation Fund Name Mean StDev Fund 1 0.84% 0.89% Fund 2 0.80% 0.86% Fund 3 1.04% 1.78% Fund 4 1.33% 2.26% Fund 5 0.64% 1.01% Sample Portfolio 0.93% 1.03% VaR cVaR -0.62% -0.99% -0.62% -0.98% -1.89% -2.63% -2.39% -3.34% -1.03% -1.45% -0.77% -1.21% Normal/Gaussian Descriptives and VaRs Mean Contributor StDev Contributor nVaR Contributor 18.18% 13.15% -0.06% 17.17% 11.32% -0.03% 22.32% 28.56% -0.28% 28.60% 35.20% -0.33% 13.72% 11.78% -0.07% 100.00% 100.00% -0.77% Fund Name Fund 1 Fund 2 Fund 3 Fund 4 Fund 5 Sample Portfolio Attribution of Portfolio Descriptives Normal “ Type” Diversifier Diversifier High Return High Return Diversifier

16. Non-Linear Analysis of sample Portfolio Fund 1 vs Fund 2 Fund 1 vs Fund 3 Fund 1 vs Fund 4 Fund 1 vs Fund 5 Fund 2 vs Fund 3 Fund 2 vs Fund 4 Fund 2 vs Fund 5 Fund 3 vs Fund 4 Fund 3 vs Fund 5 Fund 4 vs Fund 5 Portfolios 95% Modified VaR = -0.82% Modified Correlation 0.589 0.601 0.470 0.387 0.476 0.553 0.695 0.306 0.249 0.522 Fund Name “ Mod SD” Skew Kurtosis Fund 1 0.84% 0.75% 0.458 6.619 Fund 2 0.80% 0.95% -0.685 0.634 Fund 3 1.04% 1.68% 0.150 2.425 Fund 4 1.33% 2.00% 0.549 1.408 Fund 5 0.64% 1.26% -4.041 21.616 Sample Portfolio 0.93% 1.06% -0.254 1.160 VaR cVaR Modified/Cornish Fisher Descriptives and VaRs Mean Attribution of Portfolio Descriptives Mean Contributor “ Mod SD” Contributor mVaR Contributor 18.18% -0.06% 17.17% -0.06% 22.32% -0.27% 28.60% -0.32% 13.72% -0.11% 100.00% 100.00% -0.82% Fund Name Fund 1 Fund 2 Fund 3 Fund 4 Fund 5 Sample Portfolio 13.32% 12.54% 26.95% 33.48% 13.72% Skew Contributor Kurt Contributor 17.90% 15.59% 39.94% 9.56% -10.79% 25.72% -6.34% 33.45% 59.28% 15.67% 100.00% 100.00% Diversifier Diversifier High Return High Return Diversifier Normal “ Type” Attempts to address the non-linear dependence of hedge funds by coming up with an analogue or ‘modified’ correlation matrix using the additional co-skewness and co-kurtosis matrices. This allows for a better understanding of the underlying risk factors within the portfolio -0.38% -1.52% -0.77% -1.27% -1.73% -3.05% -1.96% -2.90% -1.44% -2.75% -0.82% -1.49%

17. Comparison of Normal and Modified Distributions Fatter Tails Negatively Skewed Normal Modified 95% VaR -0.77% -0.82% 99% VaR -1.48% -1.93%

18. Putting it all together – The Infiniti Capital Analytics Suite (IAS)

19. Import database of Funds

20. Fund Database

21. Filter by Infiniti Qualified (QFL) and Invested List

22. Filter further

23. Filter further by Fund AUM exclude funds with less than $20m

24. Filter further by Fund AUM exclude funds with less than $20m

25. Ensure all funds have up to date history

26. Load filtered list into Simulated Annealing Optimiser

27. Set weight constraints

28. Cooling schedule for Annealing and no of iterations - Defaults

29. Fee Information - Defaults

30. Drag and Drop standard check-limits or build custom limits

31. Default objective function is Infiniti SFA Total Score

32. What is SFA Score ? – Ranking system for Risk, Return and Persistence

33. Risk, Return and Persistence scores made up of multiple factors

34. Can also use any other objective function

35. Here objective function is maximise CAGR and minimise Drawdowns

36. Run Portfolio improvement routine for 10,000 iterations

37. Generates in-sample Returns of 12.65% with volatility of 2.22%

38. Change Benchmark to CSFB Tremont

39. Show Benchmark Returns and remove fees if investable

40. Verify all Check-limit constraints satisfied

41. Out of Sample performance

42. Change Chart to SFA Total Score or any other statistic

43. Verify SFA Score matches optimised value

44. Can be used to build portfolios with any shape distribution

45. DISCLAIMER: This presentation is by Infiniti Capital AG, the Investment Manager of The Infiniti Capital Trust and its portfolio’s. Application for shares can only be made on the basis of the current Prospectuses. The Funds are unregulated collective investment schemes in the UK and Switzerland and their promotion by authorised persons in the UK is restricted by the Financial Services and Markets Act 2000. The price of shares and the income from them can go down as well as up and the value of an investment can fluctuate in response to changes in exchange rates. The following information is intended for institutional investors who are accredited investors and qualified purchasers under the securities laws. Investment in the Fund involves special considerations and risks. There can be no assurance that the Fund’s investment objectives will be achieved. An investment in the Fund is only suitable for sophisticated investors who fully understand and are capable of assuming the risk of an investment in the Fund. Multi Manager Multi Strategy Fund of Funds