This document provides an overview of research into developing parametric pricing models for hedge funds and fund of hedge funds. The research aims to account for the unique statistical properties of alternative investments and provide a framework for assessing, categorizing, and predicting hedge fund performance. The methodology involves collecting historical hedge fund data from multiple databases, testing for biases, and applying statistical techniques like factor analysis, regression analysis, and Monte Carlo simulation to build pricing models. Preliminary findings indicate accounting for non-normality and non-linearity can improve model accuracy, and that performance can be attributed to both strategy and location factors. Progress includes extensive literature review, model building, and preliminary results testing, with plans to publish upon securing additional data access.

1. Parametric Pricing Models
for Hedge Funds
An Introduction to Quantitative
Research into Hedge Fund
Investments

2. ‘In the business world, the rearview
mirror is always clearer than the
windshield’
- Warren Buffett -

3. Content
I. Research Approach and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix

4. Research Purpose
1. Developing accurate parametric pricing models for
hedge funds and fund of hedge funds
2. Accounting for the special statistical properties of
alternative investment funds
3. Providing practitioners and statisticians with a
framework to assess, categorize and predict hedge
fund investments
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix

5. Positivistic, deductive research:
Postulation of hypotheses that are tested via standard statistical
procedures
Research Philosophy
Empirical analysis:
Interpreting the quality of pricing models on the basis of historical
data
Research Approach
External secondary data:
Historic time series adjusted for data-bias effects
Primary Data
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix
Research Approach

6. Data Sources
Hedge Fund
Databases
CISDM/MAR
Financial
Databases
Risk
Simulation
Monte Carlo
(Solver)
Confidence
(RiskSim)
DATA
POOL
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix
Data Sourcing

7. FACTOR
ANALYSIS
Data
Treatment
Risk
Simulation
Statistical
Processing
Excel /
VBA
Statistica
EViews
DATA
POOL
MODEL
BUILDI
NG
STATISTIC
AL
CLUSTERI
NG
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix
Data Treatment

8. Data
Import
• Extract relevant data from Access (SQL)
• Import data as Pivot table report
Data
Treatment
• Test for serial correlation /databias
• Calculate adjusted excess returns
Data
Analysis
• Select funds with consistent data series
• Determine statistical model
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix
Data Processing (1/2)

9. Weighting
• Estimate weighted average parameters
• Construct style indices
Comparative
Analysis
• Calculate within-group variation
• Calculate between-group variation
Data Output
• Tabular display of aggregate results
• Construction of line - bar charts
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix
Data Processing (2/2)

10. • Code
• Fund (Name)
• Main Strategy
Information
• MM_DD_YYYY (Date)
• Yield
• Ptype (ROI or AUM)
Performance
• Leverage (Yes/No)
System
Information
Access
Database
Excel Pivot table
report
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. AppendixData Import

11. Data Validity
 Consistency of performance history across different
database providers
 Degree of history-backfilling bias
 Exclusion of defaulted funds/non-reporting funds
from databases (survivorship bias)
 Extent of infrequent or inconsistent pricing of assets
(managerial bias)
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix

12. Data Bias
Survivorshi
p
Self-
Selection
Database
Instant
History
Look-ahead
Inclusion of graveyard funds
Multiple databases
Rolling-window observation / Incubation
period
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. Appendix

13. Categories
Directional
Dedicated Short
Bias
Global Macro
Emerging
Markets
Global Macro
Long / Short
Equity
Managed
Futures
Fund of Hedge
Funds
Market Neutral
Equity Market
Neutral
Event Driven
Event Driven
Convertible
Arbitrage
Fixed Income
Arbitrage
I. Research Approach
and Methodology
II. Model Building
III. Preliminary Findings
IV. Progress Report
V. AppendixCategorization (TASS)

14. Statistical tests
• Regression Alpha
• Average Error term
• Information Ratio
• Normality (Chi-squared, Jarque Bera)
• Goodness of fit, phase-locking and collinearity
(Akaike Information Criterion, Hannan-
Schwartz)
• Serial Correlation (Durbin-Watson,
Portmanteau)
• Non-stationarity (unit root)
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix

15. t – test (between
strategies)
Unbalanced
ANOVA (within
and between
treatments)
t – test (leverage
vs. no leverage)
t – test for
equal means
t – test for
equal means
t – test for
equal means
Model 1a
Model
2a
t – test for
equal means
Model 1b
Model
2b
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix
Comparative Analysis

16. Literature Review (1/2)
 Hedge Fund Linear Pricing Models
 Sharpe Factor Model (Sharpe, 1992)
 Constrained Regression (Otten, 2000)
 Fama-French Factor Model (Fama, 1992)
 Factor Component Analysis (Fung, 1997)
 Simulation of Trading component (lookback
straddle)
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix

17. Literature Review (2/2)
 Statistical Properties
 Normality (Jarque & Bera, 1981)
 Serial Correlation (Wald, 1943; Durbin & Watson, 1950;
Durbin & Watson, 1951; Box & Pierce, 1970; Ljung & Box,
1978))
 Non-stationarity (Dickey & Fuller, 1979)
 Goodness of fit
 Akaike Information Criterion (Akaike, 1974)
 Adapted Criteria (Hannan & Quinn, 1979; Schwartz, 1997)
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix

18. Prediction
Models
AR
ARMA
ARIMA
GLS
Univariate
Multivariate
Conditional
PCA
Polynomial
Fitting
Taylor
Series
Higher Co-
Moments
Constrained
Lagrange
KKT
Simulation
Prediction Models
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix

19. Empirical Findings
 The accuracy of pricing models could be significantly
improved when accounting for special statistical
properties of hedge funds (Non-normality, non-
linearity)
 Hedge fund performance can be attributed to
location choice as well as trading strategy
 A limited number of principal components explains a
significant proportion of cross-sectional return
variation
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix

20. Progress (1/2)
Extensive literature review on alternative
investments, recent developments in asset pricing
models and Monte Carlo simulation (completed)
x Securing access to relevant databases and
confidential information (currently access to one of
three databases considered in the proposal stage)
Peer-group review of research proposal and research
to date (EDAMBA summer academy)
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix

21. Progress (2/2)
x Publication of preliminary results (in order to
confirm current results, access to at least one
additional database is required)
Model building and stress testing (completed)
Composition of first draft (introduction and first
chapter)
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix

22. Akaike, H. 1974. A New Look at the Statistical Model Identification. IEEE
Transactions on Automatic Control, 19(6), 716‐723.
Anil K. Bera & Carlos M. Jarque. 1981. Efficient tests for normality,
homoscedasticity and serial independence of regression residuals Monte
Carlo Evidence. Economics Letters, 7(4), 313–318. [Online] Available:
http://www.sciencedirect.com/science/article/B6V84-45DMS48-
6D/2/1f19942c94348a8549c84897ddc4208b. Accessed: 12 June 2009.
Box, G. E. P. & Pierce, D. A. 1970. Distribution of Residual Autocorrelations
in Autoregressive-Integrated Moving Average Time Series Models. Journal
of the American Statistical Association, 65(332), 1509‐1526.
[Online] Available: http://www.jstor.org/stable/2284333. Accessed: 12
June 2009.
Dickey, D. A. & Fuller, W. A. 1979. Distribution of the Estimators for
Autoregressive Time Series With a Unit Root. Journal of the American
Statistical Association, 74(366), 427‐431. [Online] Available:
http://www.jstor.org/stable/2286348. Accessed: 12 June 2009.
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix
Sources (1/4)

23. Durbin, J. & Watson, G. S. 1950. Testing for Serial Correlation in Least
Squares Regression: I. Biometrika, 37(3/4), 409‐428. [Online] Available:
http://www.jstor.org/stable/2332391. Accessed: 12 June 2009.
Durbin, J. & Watson, G. S. 1951. Testing for Serial Correlation in Least
Squares Regression. II. Biometrika, 38(1/2), 159‐177. [Online] Available:
http://www.jstor.org/stable/2332325. Accessed: 12 June 2009.
Fama, E.F. & French, K.R. 1992. The Cross-Section of Expected Stock
Returns. Journal of Finance, 47(2), June, 427-465. [Online] Available:
http://links.jstor.org/sici?sici=0022-
1082%28199206%2947%3A2%3C427%3ATCOESR%3E2.0.CO%3B2-N
Fung, W. & Hsieh, D.A. 1997. Empirical characteristics of dynamic trading
strategies: the case of hedge funds. Review of Financial Studies, 10(2),
Summer, 275-302. [Online] Available:
http://faculty.fuqua.duke.edu/~dah7/rfs1997.pdf
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix
Sources (2/4)

24. Hannan, E. J. & Quinn, B. G. 1979. The Determination of the Order of an
Autoregression. Journal of the Royal Statistical Society. Series B
(Methodological), 41(2), 190‐195. [Online] Available:
http://www.jstor.org/stable/2985032. Accessed: 12 June 2009.
Ljung, G. M. & Box, G. E. P. 1978. On a Measure of Lack of Fit in Time
Series Models. Biometrika, 65(2), 297‐303. [Online] Available:
http://www.jstor.org/stable/2335207. Accessed: 12 June 2009.
Otten, R. & Bams, D. 2000. Statistical Tests for Return-Based Style
Analysis. Paper delivered at EFMA 2001 Lugano Meetings, July. [Online]
Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=277688
Sharpe, W.F. 1992. Asset allocation: management style and performance
measurement. Journal of Portfolio Management, Winter, 7-19. [Online]
Available: www.uic.edu/classes/fin/fin512/Articles/sharpe.pdf
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix
Sources (3/4)

25. Sharpe, W.F. 1992. Asset allocation: management style and performance
measurement. Journal of Portfolio Management, Winter, 7-19. [Online]
Available: www.uic.edu/classes/fin/fin512/Articles/sharpe.pdf
Wald, A. 1943. Tests of Statistical Hypotheses Concerning Several
Parameters When the Number of Observations is Large. Transactions of
the American Mathematical Society, 54(3), 426‐482. [Online] Available:
http://www.jstor.org/stable/1990256. Accessed: 12 June 2009.
I. Research Approach
and Methodology
II. Model Building
III. Preliminary
Findings
IV. Progress Report
V. Appendix
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