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- 1. Why Distributions Matter By Peter Urbani 16 Jan 2012
- 2. Why Distributions Matter My ventures are not in one bottom trusted, Nor to one place; nor is my whole estate Upon the fortune of this present year; Therefore, my merchandise makes me not sad. Spoken by Antonio in Act I, Scene I, Merchant of Venice, William Shakespeare, circa 1650 Rembrandt’s Christ in the storm on the lake of Galilee – the cover illustration of Peter Bernstein’s excellent Against the God’s – The Remarkable Story of Risk
- 3. Diversification Diversification is the key principle upon which Modern Portfolio Theory (MPT) is built - although the concept of not putting all of one’s eggs into one basket dates all the way back to biblical times. Central to this is the concept of Correlation as measure of dependence between assets
- 4. Key Assumptions Correlation ( the standardised covariance between assets ) as the measure of dependence between assets Normally distributed returns. The assumption that asset returns are normally distributed about their means.
- 5. The Problem with Assumptions They make an ASS out of U and ME
- 6. Normality Testing
- 7. Normality Testing Hedge Funds ETF's Normal Normal Not-Normal Not-Normal
- 8. Assumed Normal Assumed Normal Assumed Normal Fund PDF (Normal) Normal VaR Normal CVaR -40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00%
- 9. If not Normal then What ? - Modified VaR ( Modified ) lower for small positive skew
- 10. Cornish Fisher - Modification VaR ( Modified ) higher for small negative skew
- 11. Cornish Fisher - Modification VaR ( Modified ) same as VaR ( Normal ) for no skew
- 12. Assumed Normal Assumed Normal Assumed Normal Fund PDF (Normal) Normal VaR Normal CVaR -40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00%
- 13. Assumed Modified Assumed Normal and Modified Distributions Assumed Normal Fund PDF (Normal) Assumed Modified Normal Fund PDF (Modified) Normal VaR Normal CVaR Modified VaR Modified CVaR - -40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00%
- 14. Cornish Fisher - Modification 1.3.2 Properties The qualitative properties of the Cornish-Fisher expansion are: If is a sequence of distributions converging to the standard normal distribution , the Edgeworth- and Cornish-Fisher approximations present better approximations (asymptotically for ) than the normal approximation itself. The approximated functions and are not necessarily monotone. has the ``wrong tail behavior'', i.e., the Cornish-Fisher approximation for for (or ). -quantiles becomes less and less reliable The Edgeworth- and Cornish-Fisher approximations do not necessarily improve (converge) for a fixed order of approximation, . and increasing For more on the qualitative properties of the Cornish-Fisher approximation see (Jaschke; 2001). It contains also an empirical analysis of the error of the Cornish-Fisher approximation to the 99%-VaR in real-world examples as well as its worst-case error on a certain class of one- and two-dimensional delta-gamma-normal models: http://fedc.wiwi.hu-berlin.de/xplore/tutorials/xfghtmlnode8.html
- 15. Problems with Modified VaR – Not Monotone Modified VaR as a function of Skewness -10.00% Modified VaR @ CL 99.00% Modified VaR @ CL 95.00% Current Current -5.00% VaR ( Modified ) 0.00% 5.00% 10.00% 15.00% 20.00% -6 -4 -2 0 2 4 Skewness http://discussions.ft.com/alchemy/forums/edhec-risk-forum/hedge-fund-risk-management-models-for-the-return-distribution/ 6
- 16. Problems with Modified VaR – Bad Tail Behaviour 100.00% 18 Modif ied CDF 90.00% ↕ Normal CDF 80.00% Kurt 70.00% 60.00% S 50.00% 12 40.00% S 30.00% S 20.00% 10.00% 0.00% -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 6 Good Z Z 2500.00% Modified PDF Normal PDF 2000.00% ↔ 0 1500.00% Skew 1000.00% Z 500.00% -6 0.00% -30.0% -6 -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% -4 -2 0 2 4 6
- 17. Problems with Modified VaR – Bad Tail Behaviour 100.00% 18 Modified CDF 90.00% ↕ Normal CDF 80.00% Kurt 70.00% 60.00% S 50.00% 12 40.00% S 30.00% S 20.00% 10.00% 0.00% -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 6 Good Z Z 2500.00% Modified PDF Normal PDF 2000.00% ↔ 0 1500.00% Skew 1000.00% Z 500.00% -6 0.00% -30.0% -6 -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% -4 -2 0 2 4 6
- 18. How Prevalent is this problem ? VERY ETF's Hedge Funds OK OK WARNING: Degenerate Cornish Fisher. CDF w ill turn in tails WARNING: Degenerate Cornish Fisher. CDF w ill turn in tails WARNING: Degenerate Cornish Fisher. CDF w ill turn in body WARNING: Degenerate Cornish Fisher. CDF w ill turn in body
- 19. VBA code to check Cornish Fisher - Modification Public Function CFRegionWarning(ByVal Skew As Double, Kurt As Double) As String Dim a As Double Dim b As Double Dim c As Double Dim Q As Double Dim R As Double Dim Denom As Double Denom = 3 * Kurt - 4 * (Skew ^ 2) If Denom > 0 Then a = 12 * Skew / Denom b = (10 * Skew ^ 2 - 9 * Kurt + 72) / Denom c = -12 * Skew / Denom Q = (a ^ 2 - 3 * b) / 9 R = (2 * (a ^ 3) - 9 * a * b + 27 * c) / 54 If R ^ 2 > Q ^ 3 Then CFRegionWarning = "" 'Its in Well Behaved Region Else CFRegionWarning = "WARNING: Degenerate Cornish Fisher. CDF will turn in body (S)" End If Else CFRegionWarning = "WARNING: Degenerate Cornish Fisher. CDF will turn in tails (Z)" End If End Function
- 20. If not Normal or Modified then What ? Best Fitting Distributions ETF's Hedge Funds Gumbel (Max) 7.8% Gumbel (Max) 23.3% Gumbel (Min) 8.2% Gumbel (Min) 13.2% Johnson (Lognormal) 13.5% Johnson (Lognormal) 13.2% Johnson (Unbounded) 0.6% Johnson (Unbounded) 2.6% Mixture of Normals 20.1% Mixture of Normals 15.9% Modified Normal 35.8% Modified Normal 21.2% Normal 12.9% Normal 6.9% Uniform 1.0% Uniform 3.7%
- 21. Assumed Normal Assumed Normal Assumed Normal Fund PDF (Normal) Normal VaR Normal CVaR -40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00%
- 22. Assumed Modified Assumed Normal and Modified Distributions Assumed Normal Fund PDF (Normal) Assumed Modified Normal Fund PDF (Modified) Normal VaR Normal CVaR Modified VaR Modified CVaR - -40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00%
- 23. Best Fitting Best Fit and Assumed Normal and Modified Distributions Best Fit Fund PDF (Gumbel (Min)) Assumed Normal Fund PDF (Normal) Assumed Modified Normal Fund PDF (Modified) Best Fit VaR Normal VaR Best Fit CVaR Normal CVaR Modified VaR Modified CVaR - -40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00%
- 24. Goodness of Fit Cummulative Probability Distributions P-P Plot (Showing Goodness of Fit for Fund) 100% 90% 80% 70% 20% Best Fit Fund CDF (Gumbel (Min)) 15% Assumed Normal Fund CDF (Normal) 10% Empirical CDF Fund 0% -25% 50% 40% -5% 30% -10% 20% -15% 10% -20% -30.00% -20.00% -10.00% Fund Best Fit (Gumbel (Min)) Fund Assumed (Normal) 5% 60% 0% -40.00% Series1 0.00% 10.00% 20.00% 30.00% 40.00% -25% -20% -15% -10% -5% 0% 5% 10% 15%
- 25. Relationship Between Assets
- 26. Relationship between Fund A and B 10.00% 8.00% 6.00% FUND A 4.00% Linear Regression 2.00% 0.00% -2.00% -4.00% -6.00% -4.00% -2.00% 0.00% 2.00% FUND B 4.00% 6.00% 8.00% 10.00%
- 27. Assumed Bi-Variate Normal Copula 10.00% 8.00% 6.00% Assumed Bi-variate Normal Copula Lines FUND A 4.00% 2.00% Linear Regression 0.00% -2.00% -4.00% -6.00% -4.00% -2.00% 0.00% 2.00% FUND B 4.00% 6.00% 8.00% 10.00%
- 28. Best Fit Bi-Variate Copula 10.00% Best Fit Bi-variate Copula Lines 8.00% Best Fit Regression 6.00% Linear Regression FUND A 4.00% 2.00% 0.00% -2.00% -4.00% -6.00% -4.00% -2.00% 0.00% 2.00% FUND B 4.00% 6.00% 8.00% 10.00%
- 29. Example – Johnson Lognormal - Normal 50.00% Best Fit Bi-variate Copula Lines 40.00% Best Fit Regression 30.00% Linear Regression FUND C 20.00% 10.00% 0.00% -10.00% -20.00% -8.00% -6.00% -4.00% -2.00% 0.00% 2.00% FUND D 4.00% 6.00% 8.00% 10.00%
- 30. Example – Normal - Normal 15.00% Best Fit Bi-variate Copula Lines 10.00% Best Fit Regression Linear Regression FUND E 5.00% 0.00% -5.00% -10.00% -15.00% -6.00% -4.00% -2.00% 0.00% 2.00% FUND F 4.00% 6.00% 8.00%
- 31. Example – Modified Normal - Normal 8.00% Best Fit Bi-variate Copula Lines 6.00% Best Fit Regression Linear Regression FUND G 4.00% 2.00% 0.00% -2.00% -4.00% -15.00% -10.00% -5.00% 0.00% FUND H 5.00% 10.00% 15.00%
- 32. Example – Mix Normals – Modified Normal 30.00% Best Fit Bi-variate Copula Lines 20.00% Best Fit Regression 10.00% Linear Regression FUND I 0.00% -10.00% -20.00% -30.00% -40.00% -20.00% -15.00% -10.00% -5.00% 0.00% 5.00% FUND J 10.00% 15.00% 20.00% 25.00% 30.00%
- 33. Example – Mod Normal – Mod Normal 40.00% Best Fit Bi-variate Copula Lines 30.00% Best Fit Regression 20.00% Linear Regression FUND K 10.00% 0.00% -10.00% -20.00% -30.00% -40.00% -50.00% -40.00% -30.00% -20.00% -10.00% 0.00% FUND L 10.00% 20.00% 30.00% 40.00% 50.00%
- 34. Relationship Between Assets 2011 Asset Class Correlations 40.00% 30.00% Total Returns ( % ) 20.00% 10.00% 0.00% -10.00% -20.00% -30.00% -40.00% -1.00 -0.50 0.00 Correlation to S&P500 0.50 1.00
- 35. Relationship Between Assets 2011 Asset Class Correlations 20.00% 15.00% Total Returns ( % ) 10.00% US Government Bonds Gold US Total Bond Market 5.00% Global Bond Index Cash 0.00% US Dollar -5.00% US High Yield Bonds US Bonds CTA's International Government Bonds Volatility US Real Estate Emerging Markets Bonds US Equities Oil Hedge Funds World Equities -10.00% -15.00% Emerging Market Equities -20.00% -1.00 -0.50 0.00 0.50 1.00 Correlation to S&P500 Correlations at multi-decade highs
- 36. Best Fit and Pearson Correl Pairs with CAGR > 7% Base Fund Lowest Correlation via Best Fit Lowest Correlation via Pearson Correl
- 37. 30.00% 30.00% Best Fit Pair CORN - OLO Pearson Pair CORN - USO Best Fit Bi-variate Copula Lines 20.00% Best Fit Bi-variate Copula Lines 20.00% Best Fit Regression 10.00% Best Fit Regression 10.00% Linear Regression Corn Fund 0.00% Corn Fund 0.00% Linear Regression -10.00% -10.00% -20.00% -20.00% -30.00% -30.00% -40.00% -25.00% -40.00% -20.00% -15.00% -10.00% -5.00% 0.00% DB Crude Oil Long ETN 5.00% 10.00% 15.00% 20.00% -25.00% -20.00% -15.00% -10.00% -5.00% 0.00% 5.00% United States Oil Fund 10.00% 15.00% 20.00% 25.00%
- 38. 12.00% 12.00% Best Fit Pair VQT - UCC 10.00% Best Fit Bi-variate Copula Lines 10.00% Pearson Pair VQT - EEH Best Fit Bi-variate Copula Lines Best Fit Regression Best Fit Regression 8.00% 8.00% Linear Regression Linear Regression 6.00% Barclays ETN+ S&P VEQTOR ETN Barclays ETN+ S&P VEQTOR ETN 6.00% 4.00% 2.00% 0.00% 4.00% 2.00% 0.00% -2.00% -2.00% -4.00% -4.00% -6.00% -15.00% -6.00% -10.00% -5.00% 0.00% 5.00% 10.00% 15.00% Ultra Consumer Services 20.00% 25.00% 30.00% 35.00% -20.00% -15.00% -10.00% -5.00% 0.00% SPECTRUM Lg Cap U.S. Sector ETN 5.00% 10.00% 15.00%
- 39. 25.00% 25.00% Best Fit Pair DLBL - HYG Pearson Pair DLBL - JNK Best Fit Bi-variate Copula Lines 20.00% Best Fit Bi-variate Copula Lines 20.00% Best Fit Regression 15.00% Linear Regression US Treasury Long Bond Bull ETN US Treasury Long Bond Bull ETN 15.00% Best Fit Regression 10.00% 5.00% Linear Regression 10.00% 5.00% 0.00% 0.00% -5.00% -5.00% -10.00% -8.00% -10.00% -6.00% -4.00% -2.00% 0.00% 2.00% 4.00% iBoxx $ HY Corp Bond Fund 6.00% 8.00% 10.00% 12.00% -10.00% -5.00% 0.00% 5.00% SPDR Barclays Capital High Yield Bond ETF 10.00% 15.00%
- 40. 40.00% 20.00% Best Fit Pair WREI - FTY Pearson Pair WREI - RWO Best Fit Bi-variate Copula Lines 30.00% Best Fit Bi-variate Copula Lines 15.00% Best Fit Regression 10.00% Linear Regression 10.00% Wilshire US REIT ETF Wilshire US REIT ETF 20.00% Best Fit Regression 0.00% Linear Regression 5.00% 0.00% -10.00% -5.00% -20.00% -10.00% -30.00% -15.00% -15.00% -10.00% -5.00% 0.00% 5.00% FTSE NAREIT Real Estate 50 Index Fund 10.00% 15.00% -20.00% -15.00% -10.00% -5.00% 0.00% 5.00% SPDR DJ Wilshire Global Real Estate ETF 10.00% 15.00% 20.00%
- 41. Other Measures of Dependence • Spearman’s Rank Order Correlation • Lin’s Concordance measure • Copula methods • Distance measures • Mutual Information and other entropy based measures
- 42. Conclusions • Distributions do differ from Normal at least 15 – 20% of the time and up to 30 – 40% of the time depending on the data set being used – Test them • The Cornish Fisher modification is not strictly monotone and should probably not be used at confidence levels above 95% • The Cornish Fisher modification has poor tail behaviour almost half of the time – CHECK • Correlation is a limited and linear measure of dependence only • Non-Linear Copula based methods offer significant promise in helping to find better diversification and pairs trading opportunities
- 43. Pietro (‘Peter’) Urbani (45) • Chief Investment Officer (CIO) – Infiniti Capital $3bn Fund of Hedge Funds Group Head of Quantitative Research – Infiniti Capital • CEO – KnowRisk Consulting – Asset Consulting • Head of Investment Strategy – Fairheads Asset Managers HNW Trust and Investment Boutique Head of Research – Fairheads Asset Managers • Head of Portfolio Management – Nexus Securities • Senior Portfolio Manager – Commercial Union – Superfund • Equities Dealer – Junior Portfolio Manager – Mathison & Hollidge Stockbrokers http://nz.linkedin.com/in/peterurbani

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