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Asset Allocation

Asset Allocation. Why Dynamic Asset Allocation is winning the race? Arnaud de Servigny. A review of the current Asset Allocation paradigm . Modern Portfolio Theory R.I.P.!.

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Asset Allocation

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  1. Asset Allocation WhyDynamicAsset Allocation iswinning the race? Arnaud de Servigny

  2. A review of the currentAsset Allocation paradigm. Modern Portfolio Theory R.I.P.!

  3. On The Efficient Frontier, there is a high degree of certainty and therefore a high degree of concentration!

  4. The efficient frontierusually relies on a limitednumber of support assets

  5. There is verylittleyear on yearstability of the Efficient Frontier

  6. Out-of-sample, the efficient frontierusually drifts to much more modest & sub-optimal performance

  7. Over the past 20 years there has been a trend of growing out-of-sampleunder performance of the MPT

  8. The Market portfolio (CAPM) & the MPT portfolios are equally not doing well

  9. Whydoesn’titwork? There is no variance stability of main asset classes! 1 The critical values for the 90%, 95% and 99% level tests are respectively below 1.22, 1.36 and 1.63, for an rejection (21”0 of no break.

  10. A test to see if we face a fat tail problem or a regime switching problem?

  11. « Through the cycle » correlation and covariance do not exist!

  12. Let us try to look at the world differently!

  13. RegimeSwitchingmodels in a nutshell

  14. Out-of-sample performance of a 3 asset Regime Switching model

  15. An “all weather” mean-variance optimisation fails to capture regime changes and therefore fat tails & the dynamics of fat tails Regime 2 Growth “Through the Cycle “ Average Fat tailed “through the cycle” distribution Regime 1 Recession Time

  16. An Unknown complex multi-regime environment can be approximated by a regularly re-estimated Mean-Variance framework updated with latest information (EWMA estimation) EWMA Estimation of monthly mean / covariance matrix Monthly mean variance process Regime 2 Growth Regime 1 Recession Time Unit of time delta ( t) Unit of time delta ( t) Unit of time delta ( t) Unit of time delta ( t) Unit of time delta ( t) Unit of time delta ( t)

  17. A Case Study • A full out-of-sampleAnalysis • In one case 4 asset classes (CashTB,IG Bond,HY Bond,US Equity ), in the other 9 (CashTB,Gov Bond,IG Bond,HY Bond,DevEquity,EM Equity,Commodities,Real Estate,HedgeFunds) • Thetargetedvolatilitylevel in the portfolio is 8% • 4 methodologies: • Equallyweighted • Mean-Variancereestimatedeveryyearbasedon a 10-year slidingwindow • Mean-Variancereestimatedeveryyearusing a memorydecayestimationmethod (EWMA) • Mean-Variancereestimatedeverymonthusing a memorydecayestimationtechnique (EWMA)

  18. Whobettercopeswith the 8% target vol out-of-sample?

  19. What is the truelevel of asset diversification in eachapproach?

  20. TailRisk

  21. The Return Dynamics

  22. A clearer picture!!!

  23. What is the degree of stability in the portfolios?

  24. Market Prices: Long-term results Data as of 20.06.2012

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