[PPT] - Chasing Your Tail Sandeep Patel Anil Suri Andrew Weisman March 28 PowerPoint Presentation

SLIDE 1

Chasing Your Tail

Sandeep Patel Anil Suri Andrew Weisman

March 28 2007 Presentation to the Q-Group

SLIDE 2

Structure

1. History of (Risk) World: Part I 2. Advances in Portfolio Construction Analytics

Meucci (2006):

Non-Normality, Extreme Co-Movements, Estimation Error, Drawdown Related Risk Measure, Extension of Black- Litterman to Non-Normal Market Views & Non-Normal Views.

Patel, Suri, Weisman (2007)

Shifting Marginal Distributions to Forward Views, Resampling Adjustment for Varying Length Histories, & Adjusting for Liquidity Using Barrier Option Model.

SLIDE 3

Structure (Con’t)

3. Data Dependence Issues

Life is Out of Sample

Post Bubble Drawdowns, Peso Problems, Small Sample Bias, Inaccurate Data

Poor Asset Allocation Advice

4. Normality (You Wish!)

Evidence and Observations on Non-Normality

Generally Not Normal, Lack of In-Sample and Out-of- Sample Correspondence

SLIDE 4

Structure (Con’t)

5. Sources of Significant Loss Potential:

Non-Normality of the Asset Menu
Illiquidity
Incentive Structures and Negative Convexity

6. Evidence of “Alpha Migration”

Econometric Analysis of Cycle Excess Return
Intuition for Alpha Migration

SLIDE 5

Structure (Con’t)

7. Alpha Migration & Tail Loss Potential

Simple Analytical Framework
Andy’s Laws
Periodic Efficiency & Tail Loss Potential

SLIDE 6

Preliminary Thoughts:

“Prediction is Very Difficult, Especially About the Future”

– Niels Bohr

“Everybody’s Got a Plan Till They Get Punched in the Face”

– Mike Tyson

SLIDE 7

Zeitgeist

Harry Markowitz, Portfolio Selection, 1952

Journal of Finance

Wins Prize 38 years later: Nobel committee

decides to diversify away idiosyncratic thought...shares prize with Merton Miller and Bill Sharpe.

Don't bet the ranch. Get more bang for your buck. Maximize output relative to input. Nothing ventured, nothing gained. Diversify instead of striving to make a killing. Don't put all your eggs in one basket; if it drops, you're in trouble. High volatility is like putting your head in the oven and your feet in the refrigerator.

SLIDE 8

The Universe Consists of:

Choices

– Risky Assets: Return, Volatility, Correlation

Beneficent Organizing Principle:

– Diversification

Promised Land

– Efficient Frontier

SLIDE 9

The Universe Populated by:

– Slavishly Conformist – Single Information Set – Rational Utility Maximizing – Portfolio Constructors

The Borg

SLIDE 10

Yada + Yada + Yada = Capital Asset Pricing Model

Strong Simplifying Assumptions (Efficiency) Yields

the Capital Asset Pricing Model:

– The Market Portfolio sits on the Efficient Frontier – All Investors Should Hold the Same Portfolio – Geared as a Function of Risk Tolerance – Idiosyncratic (Security Specific) Risk is Diversifiable – All About: Reward vs Systematic Risk – Basis for Passive Management

Mathematics of Portfolio Theory is the Theoretical

Precursor of Value-at-Risk (VaR)

SLIDE 11

The Tool Kit

Alpha
Beta
R-Squared
Correlation
Standard Deviation
Benchmarks
Tracking Error
Efficient Frontier
Scattergrams
Lots of Ratios:

– Sharpe, Treynor, Jensen,Up/Down Capture, Information...

Value at Risk
Stress Testing

SLIDE 12

Positional Data

Assume it’s available
Assume it’s map-able
Assume you have sufficient infrastructure
Assume you already understand the risk well

enough to design an appropriate set of stresses

Assume the portfolio will remain constant
Assume you’re King Leopold II of Belgium

SLIDE 13

R A M P 0 5 -E F C 1 B 2 R a t in g (B B /- / B B 0 p ric e = 7 7 - 2 0 d e f a u lt s 7 5 9 0 1 0 5 1 2 0 1 3 5 Y ie ld 1 4 . 3 4 2 8 1 4 . 3 4 2 8 1 4 . 3 4 2 8 1 4 . 3 4 2 7 1 4 .3 4 2 2 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 9 6 2 . 0 0 9 6 2 .0 0 9 6 2 . 0 0 9 6 2 .0 0 9 6 2 .0 0 S p re a d 1 0 2 8 1 0 2 8 1 0 2 8 1 0 2 7 1 0 2 7 W A L 4 . 6 7 4 .6 7 4 . 6 7 4 .6 7 4 .6 7 7 0 P a ym e n t W in d o w A u g 0 5 t o O c t 1 3 A u g 0 5 t o O c t 1 3 A u g 0 5 t o O c t 1 3 A u g 0 5 to O c t 1 3 A u g 0 5 to O c t1 3 P r in c i p a l W i n d o w O c t 0 8 t o O c t 1 3 O c t0 8 t o O c t 1 3 O c t0 8 t o O c t 1 3 O c t 0 8 t o O c t 1 3 O c t 0 8 to O c t1 3 P r in c i p a l W r it e d o w n 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) M a t u rit y # m o s 9 9 9 9 9 9 9 9 9 9 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 3 , 1 8 2 ,4 4 2 . 1 1 (5 . 8 0 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 3 , 1 8 2 ,4 4 2 . 1 1 (5 . 8 0 % ) Y ie ld 1 5 . 7 4 5 7 1 5 . 7 4 5 3 1 5 . 6 9 9 1 5 . 3 5 3 8 1 4 .9 2 8 3 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 1 ,0 9 8 . 0 0 1 , 0 9 8 .0 0 1 , 0 9 3 . 0 0 1 , 0 5 9 .0 0 1 , 0 1 8 .0 0 S p re a d 1 1 6 9 1 1 6 9 1 1 6 5 1 1 3 0 1 0 8 7 W A L 3 . 6 9 3 .6 9 3 . 7 2 3 .9 6 4 .3 2 8 5 P a ym e n t W in d o w A u g 0 5 t o A u g 1 1 A u g 0 5 t o A u g 1 1 A u g 0 5 t o A u g 1 1 A u g 0 5 to J a n 1 2 A u g 0 5 to N o v 1 2 P r in c i p a l W i n d o w J u n 0 8 t o A u g 1 1 J u n 0 8 to A u g 1 1 J u n 0 8 t o A u g 1 1 J u n 0 8 t o J a n 1 2 J u n 0 8 t o N o v 1 2 P r in c i p a l W r it e d o w n 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) M a t u rit y # m o s 7 3 7 3 7 3 7 8 8 8 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 2 , 4 1 2 ,5 2 6 . 1 0 (5 . 7 3 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 2 , 4 1 2 ,5 2 6 . 1 0 (5 . 7 3 % ) Y ie ld 1 6 . 5 9 8 3 1 6 . 2 2 6 1 1 3 . 6 6 8 1 9 . 0 3 0 3 7 . 0 7 1 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 1 ,1 7 9 . 0 0 1 , 1 4 3 .0 0 9 0 1 . 0 0 4 6 2 .0 0 2 7 2 .0 0 S p re a d 1 2 5 5 1 2 1 8 9 6 1 4 9 8 3 0 2 W A L 3 . 2 7 3 .5 4 3 . 8 1 3 .5 7 3 . 5 1 0 0 P a ym e n t W in d o w A u g 0 5 t o N o v 1 1 A u g 0 5 t o F e b 1 3 A u g 0 5 t o M a r 1 4 A u g 0 5 to M a r 1 1 A u g 0 5 to S e p 1 0 P r in c i p a l W i n d o w J u n 0 8 t o N o v 1 1 J u n 0 8 to F e b 1 3 J u n 0 8 t o M a r1 4 J u n 0 8 t o A p r 0 9 J u n 0 8 t o J a n 0 9 P r in c i p a l W r it e d o w n 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 .0 0 % ) 1 , 7 5 4 , 1 7 8 . 3 4 (1 0 . 8 6 % ) 4 ,4 9 2 , 1 1 7 . 7 6 (2 7 . 8 2 % ) 5 ,2 4 0 , 2 1 5 . 8 3 (3 2 . 4 5 % ) M a t u rit y # m o s 7 6 9 1 1 0 4 6 8 6 2 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 5 , 0 3 8 , 9 2 7 . 3 5 ( 5 . 0 5 % ) 5 7 , 4 1 9 ,5 2 4 . 0 1 (5 . 2 7 % ) p re p a y s T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 5 , 0 3 8 , 9 2 7 . 3 5 ( 5 . 0 5 % ) 5 7 , 4 1 9 ,5 2 4 . 0 1 (5 . 2 7 % ) Y ie ld 1 6 . 9 9 6 1 1 2 . 5 0 5 4 8 . 7 9 0 3 5 . 0 2 2 9 4 .2 9 9 3 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 1 ,2 1 9 . 0 0 7 9 6 .0 0 4 4 0 . 0 0 7 4 .0 0 3 .0 0 S p re a d 1 2 9 6 8 4 7 4 7 5 9 8 2 6 W A L 2 . 8 5 2 .9 4 3 . 0 2 3 .0 1 2 .9 2 1 1 5 P a ym e n t W in d o w A u g 0 5 t o J u l1 7 A u g 0 5 t o A u g 1 0 A u g 0 5 t o M a r 1 0 A u g 0 5 to N o v 0 9 A u g 0 5 to J u l0 9 P r in c i p a l W i n d o w D e c 0 7 t o J u l1 7 D e c 0 7 t o J a n 0 9 J a n 0 8 t o O c t0 8 J a n 0 8 t o A u g 0 8 J a n 0 8 t o J a n 0 8 P r in c i p a l W r it e d o w n 6 9 2 , 7 9 0 . 6 6 (4 . 2 9 % ) 2 , 9 6 0 ,8 9 5 .8 2 (1 8 . 3 3 % ) 4 , 3 9 1 , 8 5 2 . 1 8 (2 7 . 2 0 % ) 5 ,6 9 3 , 5 6 7 . 7 6 (3 5 . 2 6 % ) 5 ,8 0 8 , 7 3 0 . 2 8 (3 5 . 9 7 % ) M a t u rit y # m o s 1 4 4 6 1 5 6 5 2 4 8 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 0 , 8 4 0 . 3 8 (3 . 2 2 % ) 3 8 , 3 3 0 , 6 8 1 . 5 5 (3 . 5 2 % ) 4 1 , 7 7 0 , 6 1 4 .5 1 ( 3 . 8 3 % ) 4 4 , 9 2 9 , 7 3 6 . 6 1 ( 4 . 1 2 % ) 4 6 , 2 8 1 ,1 3 8 . 8 5 (4 . 2 5 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 0 , 8 4 0 . 3 8 (3 . 2 2 % ) 3 8 , 3 3 0 , 6 8 1 . 5 5 (3 . 5 2 % ) 4 1 , 7 7 0 , 6 1 4 .5 1 ( 3 . 8 3 % ) 4 4 , 9 2 9 , 7 3 6 . 6 1 ( 4 . 1 2 % ) 4 6 , 2 8 1 ,1 3 8 . 8 5 (4 . 2 5 % ) Y ie ld 1 4 . 0 5 4 7 9 . 7 0 9 8 7 . 1 5 8 9 5 . 5 2 6 9 4 .7 9 6 1 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 9 4 5 5 3 1 .0 0 2 8 5 . 0 0 1 2 6 .0 0 5 4 .0 0 S p re a d 1 0 0 3 5 6 8 3 1 3 1 5 0 7 8 W A L 2 . 5 3 2 .5 6 2 . 5 2 2 .4 7 2 .4 1 1 3 0 P a ym e n t W in d o w A u g 0 5 t o D e c 0 9 A u g 0 5 t o A u g 0 9 A u g 0 5 t o A p r0 9 A u g 0 5 to D e c 0 8 A u g 0 5 to O c t0 8 P r in c i p a l W i n d o w S e p 0 7 t o J u n 0 8 S e p 0 7 t o F e b 0 8 S e p 0 7 t o D e c 0 7 S e p 0 7 to O c t 0 7 S e p 0 7 to S e p 0 7 P r in c i p a l W r it e d o w n 2 , 5 2 5 , 9 0 1 . 9 9 ( 1 5 . 6 4 % ) 4 , 0 0 3 ,3 7 1 .3 2 (2 4 . 7 9 % ) 4 , 7 0 6 , 1 9 3 . 4 3 (2 9 . 1 4 % ) 5 ,0 9 3 , 0 6 4 . 1 5 (3 1 . 5 4 % ) 5 ,2 2 6 , 3 4 8 . 3 9 (3 2 . 3 6 % ) M a t u rit y # m o s 5 3 4 9 4 5 4 1 3 9 T o t a l C o l la t L o s s (E n t it le d ) 2 8 ,5 1 9 , 4 2 3 . 0 5 (2 . 6 2 % ) 3 1 , 5 9 1 , 2 2 1 . 0 5 (2 . 9 0 % ) 3 3 , 5 3 9 , 3 4 6 .3 5 ( 3 . 0 8 % ) 3 4 , 6 8 0 , 1 4 0 . 4 5 ( 3 . 1 8 % ) 3 6 , 8 0 3 ,7 7 2 . 5 3 (3 . 3 8 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 2 8 ,5 1 9 , 4 2 3 . 0 5 (2 . 6 2 % ) 3 1 , 5 9 1 , 2 2 1 . 0 5 (2 . 9 0 % ) 3 3 , 5 3 9 , 3 4 6 .3 5 ( 3 . 0 8 % ) 3 4 , 6 8 0 , 1 4 0 . 4 5 ( 3 . 1 8 % ) 3 6 , 8 0 3 ,7 7 2 . 5 3 (3 . 3 8 % )

The Modern Risk Report

SLIDE 14

R A M P 0 5 -E F C 1 B 2 R a t in g (B B /- / B B 0 p ric e = 7 7 - 2 0 d e f a u lt s 7 5 9 0 1 0 5 1 2 0 1 3 5 Y ie ld 1 4 . 3 4 2 8 1 4 . 3 4 2 8 1 4 . 3 4 2 8 1 4 . 3 4 2 7 1 4 .3 4 2 2 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 9 6 2 . 0 0 9 6 2 .0 0 9 6 2 . 0 0 9 6 2 .0 0 9 6 2 .0 0 S p re a d 1 0 2 8 1 0 2 8 1 0 2 8 1 0 2 7 1 0 2 7 W A L 4 . 6 7 4 .6 7 4 . 6 7 4 .6 7 4 .6 7 7 0 P a ym e n t W in d o w A u g 0 5 t o O c t 1 3 A u g 0 5 t o O c t 1 3 A u g 0 5 t o O c t 1 3 A u g 0 5 to O c t 1 3 A u g 0 5 to O c t1 3 P r in c i p a l W i n d o w O c t 0 8 t o O c t 1 3 O c t0 8 t o O c t 1 3 O c t0 8 t o O c t 1 3 O c t 0 8 t o O c t 1 3 O c t 0 8 to O c t1 3 P r in c i p a l W r it e d o w n 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) M a t u rit y # m o s 9 9 9 9 9 9 9 9 9 9 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 3 , 1 8 2 ,4 4 2 . 1 1 (5 . 8 0 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 3 , 1 8 2 ,4 4 2 . 1 1 (5 . 8 0 % ) Y ie ld 1 5 . 7 4 5 7 1 5 . 7 4 5 3 1 5 . 6 9 9 1 5 . 3 5 3 8 1 4 .9 2 8 3 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 1 ,0 9 8 . 0 0 1 , 0 9 8 .0 0 1 , 0 9 3 . 0 0 1 , 0 5 9 .0 0 1 , 0 1 8 .0 0 S p re a d 1 1 6 9 1 1 6 9 1 1 6 5 1 1 3 0 1 0 8 7 W A L 3 . 6 9 3 .6 9 3 . 7 2 3 .9 6 4 .3 2 8 5 P a ym e n t W in d o w A u g 0 5 t o A u g 1 1 A u g 0 5 t o A u g 1 1 A u g 0 5 t o A u g 1 1 A u g 0 5 to J a n 1 2 A u g 0 5 to N o v 1 2 P r in c i p a l W i n d o w J u n 0 8 t o A u g 1 1 J u n 0 8 to A u g 1 1 J u n 0 8 t o A u g 1 1 J u n 0 8 t o J a n 1 2 J u n 0 8 t o N o v 1 2 P r in c i p a l W r it e d o w n 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) 0 .0 0 ( 0 . 0 0 % ) M a t u rit y # m o s 7 3 7 3 7 3 7 8 8 8 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 2 , 4 1 2 ,5 2 6 . 1 0 (5 . 7 3 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 6 , 1 6 2 , 1 7 0 . 7 6 ( 5 . 1 5 % ) 6 2 , 4 1 2 ,5 2 6 . 1 0 (5 . 7 3 % ) Y ie ld 1 6 . 5 9 8 3 1 6 . 2 2 6 1 1 3 . 6 6 8 1 9 . 0 3 0 3 7 . 0 7 1 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 1 ,1 7 9 . 0 0 1 , 1 4 3 .0 0 9 0 1 . 0 0 4 6 2 .0 0 2 7 2 .0 0 S p re a d 1 2 5 5 1 2 1 8 9 6 1 4 9 8 3 0 2 W A L 3 . 2 7 3 .5 4 3 . 8 1 3 .5 7 3 . 5 1 0 0 P a ym e n t W in d o w A u g 0 5 t o N o v 1 1 A u g 0 5 t o F e b 1 3 A u g 0 5 t o M a r 1 4 A u g 0 5 to M a r 1 1 A u g 0 5 to S e p 1 0 P r in c i p a l W i n d o w J u n 0 8 t o N o v 1 1 J u n 0 8 to F e b 1 3 J u n 0 8 t o M a r1 4 J u n 0 8 t o A p r 0 9 J u n 0 8 t o J a n 0 9 P r in c i p a l W r it e d o w n 0 . 0 0 (0 .0 0 % ) 0 . 0 0 (0 .0 0 % ) 1 , 7 5 4 , 1 7 8 . 3 4 (1 0 . 8 6 % ) 4 ,4 9 2 , 1 1 7 . 7 6 (2 7 . 8 2 % ) 5 ,2 4 0 , 2 1 5 . 8 3 (3 2 . 4 5 % ) M a t u rit y # m o s 7 6 9 1 1 0 4 6 8 6 2 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 5 , 0 3 8 , 9 2 7 . 3 5 ( 5 . 0 5 % ) 5 7 , 4 1 9 ,5 2 4 . 0 1 (5 . 2 7 % ) p re p a y s T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 1 , 3 5 6 . 7 3 (3 . 2 2 % ) 4 2 , 1 2 1 , 6 2 8 . 0 7 (3 . 8 6 % ) 4 9 , 1 4 1 , 8 9 9 .4 2 ( 4 . 5 1 % ) 5 5 , 0 3 8 , 9 2 7 . 3 5 ( 5 . 0 5 % ) 5 7 , 4 1 9 ,5 2 4 . 0 1 (5 . 2 7 % ) Y ie ld 1 6 . 9 9 6 1 1 2 . 5 0 5 4 8 . 7 9 0 3 5 . 0 2 2 9 4 .2 9 9 3 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 1 ,2 1 9 . 0 0 7 9 6 .0 0 4 4 0 . 0 0 7 4 .0 0 3 .0 0 S p re a d 1 2 9 6 8 4 7 4 7 5 9 8 2 6 W A L 2 . 8 5 2 .9 4 3 . 0 2 3 .0 1 2 .9 2 1 1 5 P a ym e n t W in d o w A u g 0 5 t o J u l1 7 A u g 0 5 t o A u g 1 0 A u g 0 5 t o M a r 1 0 A u g 0 5 to N o v 0 9 A u g 0 5 to J u l0 9 P r in c i p a l W i n d o w D e c 0 7 t o J u l1 7 D e c 0 7 t o J a n 0 9 J a n 0 8 t o O c t0 8 J a n 0 8 t o A u g 0 8 J a n 0 8 t o J a n 0 8 P r in c i p a l W r it e d o w n 6 9 2 , 7 9 0 . 6 6 (4 . 2 9 % ) 2 , 9 6 0 ,8 9 5 .8 2 (1 8 . 3 3 % ) 4 , 3 9 1 , 8 5 2 . 1 8 (2 7 . 2 0 % ) 5 ,6 9 3 , 5 6 7 . 7 6 (3 5 . 2 6 % ) 5 ,8 0 8 , 7 3 0 . 2 8 (3 5 . 9 7 % ) M a t u rit y # m o s 1 4 4 6 1 5 6 5 2 4 8 T o t a l C o l la t L o s s (E n t it le d ) 3 5 ,1 0 0 , 8 4 0 . 3 8 (3 . 2 2 % ) 3 8 , 3 3 0 , 6 8 1 . 5 5 (3 . 5 2 % ) 4 1 , 7 7 0 , 6 1 4 .5 1 ( 3 . 8 3 % ) 4 4 , 9 2 9 , 7 3 6 . 6 1 ( 4 . 1 2 % ) 4 6 , 2 8 1 ,1 3 8 . 8 5 (4 . 2 5 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 3 5 ,1 0 0 , 8 4 0 . 3 8 (3 . 2 2 % ) 3 8 , 3 3 0 , 6 8 1 . 5 5 (3 . 5 2 % ) 4 1 , 7 7 0 , 6 1 4 .5 1 ( 3 . 8 3 % ) 4 4 , 9 2 9 , 7 3 6 . 6 1 ( 4 . 1 2 % ) 4 6 , 2 8 1 ,1 3 8 . 8 5 (4 . 2 5 % ) Y ie ld 1 4 . 0 5 4 7 9 . 7 0 9 8 7 . 1 5 8 9 5 . 5 2 6 9 4 .7 9 6 1 M k t V a lu e 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 M k t V a lu e w / A c c ru e d 1 2 , 5 3 5 ,6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 . 2 5 1 2 ,5 3 5 , 6 6 1 . 2 5 1 2 , 5 3 5 , 6 6 1 .2 5 1 2 , 5 3 5 , 6 6 1 .2 5 D is c M a rg in 9 4 5 5 3 1 .0 0 2 8 5 . 0 0 1 2 6 .0 0 5 4 .0 0 S p re a d 1 0 0 3 5 6 8 3 1 3 1 5 0 7 8 W A L 2 . 5 3 2 .5 6 2 . 5 2 2 .4 7 2 .4 1 1 3 0 P a ym e n t W in d o w A u g 0 5 t o D e c 0 9 A u g 0 5 t o A u g 0 9 A u g 0 5 t o A p r0 9 A u g 0 5 to D e c 0 8 A u g 0 5 to O c t0 8 P r in c i p a l W i n d o w S e p 0 7 t o J u n 0 8 S e p 0 7 t o F e b 0 8 S e p 0 7 t o D e c 0 7 S e p 0 7 to O c t 0 7 S e p 0 7 to S e p 0 7 P r in c i p a l W r it e d o w n 2 , 5 2 5 , 9 0 1 . 9 9 ( 1 5 . 6 4 % ) 4 , 0 0 3 ,3 7 1 .3 2 (2 4 . 7 9 % ) 4 , 7 0 6 , 1 9 3 . 4 3 (2 9 . 1 4 % ) 5 ,0 9 3 , 0 6 4 . 1 5 (3 1 . 5 4 % ) 5 ,2 2 6 , 3 4 8 . 3 9 (3 2 . 3 6 % ) M a t u rit y # m o s 5 3 4 9 4 5 4 1 3 9 T o t a l C o l la t L o s s (E n t it le d ) 2 8 ,5 1 9 , 4 2 3 . 0 5 (2 . 6 2 % ) 3 1 , 5 9 1 , 2 2 1 . 0 5 (2 . 9 0 % ) 3 3 , 5 3 9 , 3 4 6 .3 5 ( 3 . 0 8 % ) 3 4 , 6 8 0 , 1 4 0 . 4 5 ( 3 . 1 8 % ) 3 6 , 8 0 3 ,7 7 2 . 5 3 (3 . 3 8 % ) T o t a l C o l la t L o s s (F o re c a s t e d ) 2 8 ,5 1 9 , 4 2 3 . 0 5 (2 . 6 2 % ) 3 1 , 5 9 1 , 2 2 1 . 0 5 (2 . 9 0 % ) 3 3 , 5 3 9 , 3 4 6 .3 5 ( 3 . 0 8 % ) 3 4 , 6 8 0 , 1 4 0 . 4 5 ( 3 . 1 8 % ) 3 6 , 8 0 3 ,7 7 2 . 5 3 (3 . 3 8 % )

The Modern Risk Report

SLIDE 15

Advances In Portfolio Construction Analytics

Meucci (2005, 2006)

Non-Normality

Marginals Estimated by Kernels

Tail Correlation

t-Copula

Estimation Error

Resampling

Drawdown

Relevant Risk Measures: CVaR

Extension of Black-Litterman to Non-Normal Market

Views & Non-Normal Views

New Frontier Advisors, FinAnalytica, Axioma and others

SLIDE 16

Advances In Hedge Fund Portfolio Construction Analytics

Patel, Suri, Weisman (2007)

Absence of Priors

Kullback-Leibler

Unequal History

Masking Technology

Illiquidity

Barrier Option Model

CVaR, CDaR
Resampling
Kernels, Copulas

SLIDE 17

Shifting to Forward-Looking Views

The Expected Excess Return of a Strategy

– Fluctuates as Macro-Economic Environment Changes – Diminishes as Competition Increases

Adapt Risk-Neutralized Distribution Techniques Developed for Pricing Options
Facilitates Blending Alternative Prospective Distributions of Returns and Volatility

SLIDE 18

Simulated Returns for Managers with Unequal Track Records

Panel A: Observed Manager Returns History for 84 months

SLIDE 19

Simulated Returns for Managers with Unequal Track Records

Panel B: Observed Manager Returns History for 24 months

SLIDE 20

Non-Normality of Hedge Funds and Sample Size Issues

Fitted Index Returns:

– Non-Normal & Difficult to Parameterize

Observed Normality a Poor Guide

– Single Manager Example – Index Example of Multiple Tests of Normality – Broad Cross-Section of Individual Funds

SLIDE 21

The Evidence

Normal(0.022755, 0.032643)

2 4 6 8 10 12 14

0.08
0.06
0.04
0.02

0.00 0.02 0.04 0.06 0.08 0.10 0.12

< > 5.0% 5.0% 90.0%

0.0309

0.0764

Logistic(0.038308, 0.020054)

2 4 6 8 10 12 14 16

0.15
0.10
0.05

0.00 0.05 0.10

< > 5.0% 90.0%

0.0207

0.0974

Logistic(0.033078, 0.016981)

2 4 6 8 10 12 14 16 18

0.20
0.15
0.10
0.05

0.00 0.05 0.10

< > 5.0% 90.0%

0.0169

0.0831

Logistic(0.029606, 0.018484)

2 4 6 8 10 12 14 16

0.20
0.15
0.10
0.05

0.00 0.05 0.10

< > 5.0% 90.0%

0.0248

0.0840

BetaGeneral(6.6694, 2.4694, -0.087093, 0.054571) Values in Thousandths

5 10 15 20 25

62.0
31.5
1.0

29.5 60.0

< 5.0% 5.0% 90.0%

20.0885

44.2317 Logistic(0.021537, 0.013287) Values in Thousandths

2 4 6 8 10 12 14 16 18 20

80
60
40
20

20 40 60 80

< > 5.0% 5.0% 90.0%

17.5852

60.6597

Normal(0.026851, 0.042281)

2 4 6 8 10 12 14

0.10
0.05

0.00 0.05 0.10 0.15 0.20

< > 5.0% 5.0% 90.0%

0.0427

0.0964

Convert Arb Distressed Event Driven Multi Strat Risk Arb Event Driven Fixed Income Arb Hedge Fund Index

SLIDE 22

The Evidence

Difficult to fit to a Distribution
Risk = Standard Deviation = Big Mistake
Fitting Distributions to Historical Data

Frequently Underestimates Loss Potential

Sector A_D Test Value Convert Arb 0.5 0.15 - 0.25 Distressed 1.6 < 0.005 Event Driven 1.9 < 0.005 Event Driven MS 1.9 < 0.005 Fixed Income Arb 1.0 0.005 - 0.01 Risk Arb 0.7 0.05 - 0.1 Hedge Fund Index 0.8 0.025 - 0.05 Probability Value (p)

SLIDE 23

Alpha? That’s Funny…

Logistic(0.0127575, 0.0030517)

Values x 10^2 Values in Thousandths

0.0 0.2 0.4 0.6 0.8 1.0 1.2

15
10
5

5 10 15 20 25 30

< > 5.0% 5.0% 90.0% 3.7720 21.7431

Mean 1.2 Mode 1.3 Median 1.3 St Dev 0.6 Skewness

1.1

Kurtosis 5.4 Summary Statistics

SLIDE 24

…You Didn’t Look Skewish

ExtValue(-0.044408, 0.15131)

5 10 15 20 25

0.6
0.5
0.4
0.3
0.2
0.1

0.0 0.1

< > 5.0% 90.0%

0.2104

0.4050

Mean 0.2 Mode 1.3 Median 1.3

Std. Dev

6.5 Skewness

7.2

Kurtosis 55.5 Summary Statistics

SLIDE 25

January 1991 – June 1998 p-values of Tests of Normality

1 2 3 4 5 6 7 8 Strategies Lilliefors Cramer von Mises Anderson- Darling Pearson( Chi-square) Shapiro- Wilk Shapiro- Francia Jarque- Bera Kolmogoro v-Smirnov 1 HFRI Emerging Markets 0.33 0.04 0.28 0.19 0.83 0.91 0.50

2

HFRI Equity Hedge 0.34 0.03 0.24 0.03 0.11 0.35 0.03

3

HFRI Macro 0.08 0.03 0.17 0.04 0.57 0.75 0.50

4

HFRI Distressed Securities 0.01 0.05 0.03 0.46 0.04 0.22 0.01

5

HFRI Merger Arbitrage 0.00 0.16 0.00 0.32 0.02 0.21 0.04

6

HFRI Event-Driven 0.50 0.05 0.46 0.74 0.72 0.79 0.14

7

HFRI Convertible Arbitrage 0.04 0.22 0.00 0.11 0.00 0.09 0.01

8

HFRI Equity Market Neutral 0.50 0.29 0.25 0.15 0.51 0.81 0.23

9

HFRI Fixed Income Arbitrage 0.05 0.26 0.05 0.20 0.12 0.36 0.05

10

HFRI Fund Weighted Composite 0.06 0.06 0.03 0.21 0.14 0.41 0.50

Normality Tests: p-value with Non-Normal Distributions

SLIDE 26

January 1991 – December 2006 p-values of Tests of Normality

1 2 3 4 5 6 7 8 Strategies Lilliefors Cramer von Mises Anderson- Darling Pearson( Chi-square) Shapiro- Wilk Shapiro- Francia Jarque- Bera Kolmogoro v-Smirnov 1 HFRI Emerging Markets 0.50 0.00 0.58 0.24 0.64 0.96 0.49

2

HFRI Equity Hedge 0.50 0.00 0.23 0.41 0.08 0.32 0.02

3

HFRI Macro 0.02 0.00 0.00 0.01 0.02 0.22 0.05

4

HFRI Distressed Securities 0.02 0.00 0.01 0.04 0.01 0.12 0.00

5

HFRI Merger Arbitrage 0.03 0.02 0.03 0.22 0.05 0.26 0.02

6

HFRI Event-Driven 0.12 0.00 0.06 0.08 0.04 0.24 0.01

7

HFRI Convertible Arbitrage 0.04 0.03 0.00 0.05 0.01 0.19 0.04

8

HFRI Equity Market Neutral 0.24 0.04 0.12 0.43 0.29 0.73 0.14

9

HFRI Fixed Income Arbitrage 0.00 0.06

0.00
0.03

0.00

10

HFRI Fund Weighted Composite 0.29 0.00 0.23 0.04 0.77 0.63 0.50

Normality Tests: p-value with Non-Normal Distributions

SLIDE 27

Normality Tests

0% 20% 40% 60% 80% 100% 120% 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

Data Pts required to be in population % with Non-Normal Distribution

Lilliefors Cramer von Mises Anderson-Darling Pearson( Chi-square) Shapiro-W ilk Shapiro-Francia Jarque-Bera Kolmogorov-Sm irnov

SLIDE 28

Sources of Tail Loss Potential

Non-normality of Underlying Asset Returns
Illiquidity: More Than Meets the

Econometricians Eye

Incentive Structures in Shorting Options
Regulatory Risk – Split-Strike, PIPEs,

Random Shorting, Death Spiral Converts

SLIDE 29

Extreme Events in Commodity Price Changes

SLIDE 30

=

t

RNAV Reported NAV at time t =

t

TNAV True (or liquidation value) NAV at time t = RNAV TNAV = δ Proportional Valuation Lag, where 1 ≤ ≤ δ = Δt = −

t t

TNAV RNAV Over-valuation at time t. = L Barrier value for

t

Δ (Where L

t ≥

Δ a payout occurs.) Smoothing Algorithm: =

t

RNAV ) (

1 1 − −

− +

t t t

RNAV TNAV RNAV δ

Smooth Idea

SLIDE 31

Building the Model

Utilize Basic Option Modeling Assumptions:

– Geometric Brownian Motion. – Risk Neutral Valuation. – Option Value Equal to Discounted Payoff.

Model as a Barrier Option:

– Barrier Exceeded When Reported Portfolio Value Greater Than True (Liquidation) Value by More Than a Specified Percent. – Barrier Payout Equals = (Percentage Overstatement + Potential Liquidation Penalty) – Option Price Equal Discounted Value of Payout. – Express Option Cost on an Annualized-Percent-of-Portfolio basis.

SLIDE 32

Smoothing of Illiquid Portfolios: Viewed as a Barrier Option

Reported NAV Vs Actual NAV mean = .15, sigma = .30, lag valuation = .15

0.4
0.3
0.2
0.1

0.1 0.2 0.3 0.4 1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5 2 7 2 9 3 1 3 3 3 5 3 7 3 9 4 1 4 3 4 5 4 7 4 9 5 1 5 3 5 5 5 7 5 9 Period Percent Difference

500

1,000 1,500 2,000 2,500 3,000 Value of $1,000 Invested Diff Market Manager

L20

t

Δ C

SLIDE 33

L (Threshold) = 5.0% 6% 8% 10% 12% 14% 16% 18% 0.1 13% 17% 20% 22% 23% 25% 26% 0.2 5% 11% 15% 18% 21% 22% 23% δ ( Smoothing parameter) 0.3 1% 5% 10% 13% 17% 19% 21% 0.4 0% 1% 3% 7% 11% 14% 17% 0.5 0% 0% 0% 2% 4% 8% 11% Note: C (Penality parameter is fixed at 20%) TNAV Volatility

SLIDE 34

L (Threshold) = 7.5% 6% 8% 10% 12% 14% 16% 18% 0.1 9% 14% 18% 21% 23% 25% 26% 0.2 1% 5% 9% 14% 17% 20% 22% δ ( Smoothing parameter) 0.3 0% 1% 3% 6% 10% 13% 16% 0.4 0% 0% 0% 1% 3% 6% 8% 0.5 0% 0% 0% 0% 0% 1% 2% Note: C (Penality parameter is fixed at 20%) TNAV Volatility

SLIDE 35

L (Threshold) = 10.0%% 6% 8% 10% 12% 14% 16% 18% 0.1 5% 11% 15% 19% 21% 24% 26% 0.2 0% 1% 5% 8% 12% 16% 19% δ ( Smoothing parameter) 0.3 0% 0% 0% 1% 4% 7% 10% 0.4 0% 0% 0% 0% 0% 1% 2% 0.5 0% 0% 0% 0% 0% 0% 0% Note: C (Penality parameter is fixed at 20%) TNAV Volatility

SLIDE 36

The Experiment

Simulate “Alpha-Transfer” in a Two Trader/Two Style World:

Trader Joe

– Long Higher Probability Bets:

More Frequent Wins
Smaller Periodic

Payouts

Larger Periodic Losses
Short Options
Trader Vic

– Long Lower Probability Bets:

Less Frequent Wins
Larger Periodic Payouts
Smaller Periodic

Losses

Long Options

SLIDE 37

Case 1

Joe sells Out of the Money Options to Vic
Struck 2 Std Dev Out of the Money
Options Valued at Black-Scholes + 10%
Underlying Process: GBM
Options Purchased at Start of a Month and

Expire at the End of Every Month

Joe Takes in 35 BP’s of Premium
Vic Spends 35 BP’s of Capital on Premium
Risk Free Rate: 5%

SLIDE 38

Case 1: Joe takes Alpha from Vic

Vic (Long Option) Joe (Short Option)

α

Option Premium

SLIDE 39

Alpha Transferred to Short Option

Distribution for + Alpha SO/N17

0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400 1.600 1.800

Mean=5.670704E-02

0.8
0.575
0.35
0.125

0.1

0.8
0.575
0.35
0.125

0.1

5% 90%

.1483

.0963

Mean=5.670704E-02

Short Monthly Return Histogram: Long Monthly Return Histogram:

Short Profile:
High Mean: 5.67%
Higher Mode: 9.63%
Truncated Profit: 9.63%
Average Net Present Value of

Incentive Fees Over Ten Year Period: 166.90

Long Profile:
Low Mean: 4.59%
Lower Mode: 0.80%
Truncated Loss
Average Net Present Value of

Incentive Fees Over Ten Year Period: 97.52

Distribution for +Alpha LO/N35

0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400

Mean=4.589801E-02

0.35 0.7 1.05 1.4 0.35 0.7 1.05 1.4

90% 5%

.008 .2377

Mean=4.589801E-02

SLIDE 40

Case 2

Joe sells Out of the Money Options to Vic
Struck 2 Std Dev Out of the Money
Options Valued at Black-Scholes
Underlying Process: GBM
Options Purchased at Start of Month and Expire

at the End of Every Month

Joe Takes in 35 BP’s of Premium
Vic Spends 35 BP’s of Capital on Premium
Risk Free Rate: 5%

SLIDE 41

Case 2: No Alpha Exchange

Vic (Long Option) Joe (Short Option)

Option Premium

α

SLIDE 42

No Alpha Transfer

Distribution for 0 Alpha SO/N17

0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400 1.600

Mean=5.227088E-02

1
0.7
0.4
0.1

0.2

1
0.7
0.4
0.1

0.2 5% 90% 5%

.1744

.0963 Mean=5.227088E-02

Short Monthly Return Histogram: Long Monthly Return Histogram:

Short Profile:
High Mode: 9.63%
Mediocre Mean: 5.23%
Truncated Profit
Average Net Present Value of

Incentive Fees Over Ten Year Period: 156.10

Long Profile:
Mediocre Mean: 5.04%
Lower Mode: 0.80%
Truncated Loss
Average Net Present Value of

Incentive Fees Over Ten Year Period: 126.93

Distribution for 0 Alpha LO/N35

0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900

Mean=0.0504102 0.625 1.25 1.875 2.5 0.625 1.25 1.875 2.5 90% 5% .008 .2646 Mean=0.0504102

SLIDE 43

Case 3

Joe sells Out of the Money Options to Vic
Struck 2 Std Dev Out of the Money
Options Valued at Black-Scholes -10%
Underlying Process: GBM
Options Purchased at Start of Month and Expire

at the End of Every Month

Joe Takes in 35 BP’s of Premium
Vic Spends 35 BP’s of Capital on Premium
Risk Free Rate: 5%

SLIDE 44

Case 3: Vic takes Alpha from Joe

Vic (Long Option) Joe (Short Option)

α

Option Premium

SLIDE 45

Alpha Transferred to Long Option

Distribution for - Alpha SO/N17

0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400 1.600

Mean=4.690464E-02

1
0.7
0.4
0.1

0.2

1
0.7
0.4
0.1

0.2

5% 90% 5%

.2075

.0963

Mean=4.690464E-02

Short Monthly Return Histogram:

Distribution for - Alpha LO/N35

0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000

Mean=5.527177E-02

0.625 1.25 1.875 2.5 0.625 1.25 1.875 2.5

90% 5%

.0135 .2644

Mean=5.527177E-02

Long Monthly Return Histogram:

Short Profile:
High Mode: 9.63%
Low Mean: 4.69%
Truncated Max
Average Net Present Value of

Incentive Fees Over Ten Year Period: 146.44

Long Profile:
Low Mode: 1.35%
High Mean: 5.53%
Truncated Loss
Average Net Present Value of

Incentive Fees Over Ten Year Period: 143.98

SLIDE 46

Performance Comparison

Alpha

+ Alpha 0 Alpha Short to Long Alpha Transfer

Mean: 4.7% Mean: 5.7% Mean: 5.2% Mean: 4.7% Mode: 9.6% Mode: 9.6% Mode: 9.6% Mode: 9.6% Fee: 146.4 Fee: 166.9 Fee: 156.1 Fee: 146.4 Mean: 4.6% Mean: 5.5% Mean: 5.0% Mean: 5.5% Mode: 0.8% Mode: 1.4% Mode: 0.8% Mode: 1.4% Fee: 97.5 Fee: 144.0 Fee: 127.0 Fee: 144.0

Short Option Long Option

SLIDE 47

Option Alpha Trivia

Short Optionality Results in a Significant

Increase in Per Unit Cost of Alpha

Short Option Strategies Have Modal Rates
f Return That are Largely Insensitive to

Alpha

Feasible Cases in Which Negative Alpha

Strategies are Preferable to a Manager

SLIDE 48

Alpha Migration

Markets are Periodically Efficient
Develop a Factor Model to Explain Hedge

Fund Index Return

– Fung and Hsieh (2001, 2002), Agarwal and Naik (2000,2004), Jaeger and Wagner (2005)

Dynamic Behavior of alpha
24-month Rolling Regressions
Plot Rolling 12-month alpha

SLIDE 49

Rolling 24 Month Alphas: Feb 96 –Jun 06

SLIDE 50

SLIDE 51

Converts Distressed Emerging Markets Fixed Income Managed Futures Global Macro Long/Short Equity Market Neutral Risk Arb Total Index Alpha

0.22 0.20 0.37 0.63 0.64 0.78 0.63 0.47 0.21 0.87

Lag1

0.0 0.0 0.0 0.0 0.0 0.4

Dow Jones AIG Commodity Index

0.6

Fama French Momentum Factor

0.0 0.6 0.0 0.0

ML All Convertibles, Ex Mandatory, Inv. Grade

0.0 0.0 2.9

ML Global Bond Index ex-US

0.7

ML Global Emg. Mkts. Sovreign Plus Bond Index

0.0 0.0

ML USD Emerging Mkts Sovreign Plus Bond Index

0.0 0.1 0.0

MSCI Emerging Markets Index

0.0 2.6

Russell 1000

0.0 0.0

Russell 1000 Value - Russell 1000 Growth

4.1 1.1

Russell 2000 - Russell 1000

0.0 0.0 0.0

Russell 2000 Value - Russell 2000 Growth

0.0

US 30 Day Treasury Bills

1.4 0.7 0.0

US Dollar Index

0.7 0.1 0.1

US High Yield Master Index

0.0 0.0 0.1

US Trade Weighted Dollar Index

2.3

WTI Crude Oil

0.4

CBOE BuyWrite Monthly Index

0.1 0.0

Fung Hsieh PTFS - Bonds

0.8 0.3

Fung Hsieh PTFS - FX

0.0 0.9

Fung Hsieh PTFS - Stocks

2.4 0.7 0.0

ML Option Volatility Estimate Index

0.2

S&P 500 Deep-out-of-the-money Calls

0.6

S&P 500 Deep-out-of-the-money Puts

0.0 0.0

S&P 500 Out-of-the-money Calls

0.1 0.1 0.0

S&P 500 Out-of-the-money Puts

0.0 0.0 0.7

Swiss Partners Group Futures Index

0.0 0.1 0.8

Number of Factors: long only

3 2 4 1 3 3 7 2 5 5

Number of Factors: with optionality

3 3 1 4 3 2 1 1 2 2

R square

63% 70% 79% 43% 44% 33% 85% 31% 51% 63%

Factor Model Results

January 1994 – June 2006

SLIDE 52

A Manager as a Discrete Process Binomial Model

[ ]

i l i w i i i i l w i i i i i

P L P W E L W P P f E

i i i

− = = = = = = = = α ς φ ς φ α ) , ( ] [

Inefficiency Set Specific Skill Set Probability of Winning in period i Probability of Losing in period i Excess Return in Period i Win in Period i Loss in Period i

SLIDE 53

Discrete Binomial Model Expected Loss Potential

[ ]

l i w i l l w w i

P E WP L P P P P W W

i i

α − = = = =

Constant Absolute Rate of Return Target Constant Ex Ante Probability of Win Constant Ex Ante Probability of Loss Expected Loss Potential in Period i

SLIDE 54

Periodically Efficient Markets Hypothesis

) , ( ] [ ς φ α ς φ f E = = =

The Inefficiency Set Periodically Declines Towards Zero The Proprietary Skill Set Becomes Increasingly Diffuse

l w v

P WP L =

Law 1: Losses are Proportional to Wins Law 2: Losses are Inversely Proportional to their Probability of Occurrence Tail Loss Potential

SLIDE 55

The Evidence Binomial Loss Estimation

Binomial Loss Estimates Produce Appropriate Scale Results
Estimated Loss Estimates Well Outside Distributional Expectations
Outliers to be Expected
Not ‘Perfect Storm’ Effects…But Perfectly Normal Effects
Risk is Better Understood as the Result of a Process

Conv Arb Evt Drvn Dist Risk Arb L/S Equity HF INDX P(w) 0.8 0.8 0.8 0.8 0.7 0.7 W 0.0 0.0 0.1 0.0 0.1 0.0 Est L

13.5%
21.3%
24.8%
16.5%
14.1%
11.9%

Obs L

9.3%
16.3%
15.7%
9.5%
9.5%
12.2%

95% Bound

7.7%
12.0%
14.0%
7.5%
7.5%
6.6%

5% Bound

27.8%
48.2%
56.0%
30.3%
30.3%
23.2%

Ratio 1.5 1.3 1.6 1.4 1.4 1.0

SLIDE 56

MSCI EM/World TR Index (Non-Rolling, Obs = 5,000)

SLIDE 57

SLIDE 58

2001-2006 Indices (Non-Rolling, Obs = 5,000)

SLIDE 59

SLIDE 60

Conclusion

Advances in Portfolio Construction

Analytics Handle Many Known Issues

Peso Problems Remain a Challenge
A Simple Betting Process Model Provides