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Data Sourcing, Statistical Processing and Time Series Analysis

This research presented at the EDAMBA Summer School focuses on creating accurate parametric pricing models for hedge funds. The study addresses the unique statistical properties of alternative investment funds and provides a framework for practitioners and statisticians to assess, categorize, and predict hedge fund investments. Utilizing empirical analysis and advanced statistical techniques like factor analysis and regression statistics, the research also incorporates methods to combat data bias effects, ensuring robust model development and evaluation.

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Data Sourcing, Statistical Processing and Time Series Analysis

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  1. Data Sourcing, Statistical Processing and Time Series Analysis Presented at EDAMBA summer school, Soréze (France) 23 July – 27 July 2009 • An Example from Research into Hedge Fund Investments

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

  3. Research Purpose • Developing accurate parametric pricing models for hedge funds and fund of hedge funds • Accounting for the special statistical properties of alternative investment funds • Providing practitioners and statisticians with a framework to assess, categorize and predict hedge fund investments

  4. Research Approach • Research Philosophy • Research Approach • Data Sourcing Logical-positivistic, deductive research: Postulation of hypotheses that are tested via standard statistical procedures Empirical analysis: Interpreting the quality of pricing models on the basis of historical data External secondary data: Historic time series adjusted for data-bias effects

  5. Data Sourcing • DATA POOL

  6. Data Treatment • FACTOR ANALYSIS • DATA POOL • MODEL BUILDING • STATISTICAL CLUSTERING

  7. Data Import Access Database Excel Pivot table report

  8. Database Management • Avoiding duplicate entries • Cross-referencing data from various sources • Combining and aggregating different databases • Efficient storage due to relational data management • Queries allow for retrieval/display of specific data • Linked-in with Microsoft VBA and Excel (data displayable as Pivot table reports) • Searching for specific entries via SQL

  9. Data Bias • Survivorship • Self-Selection • Database • Instant History • Look-ahead Inclusion of graveyard funds Multiple databases Rolling-window observation / Incubation period

  10. Statistical tests for TSA • Regression Statistics (Alpha, Average Error term, Information Ratio) • Normality (Chi-squared, JarqueBera) • Goodness of fit, phase-locking and collinearity (Akaike Information Criterion, Hannan-Schwartz) • Serial Correlation (Durbin-Watson, Portmanteau) • Non-stationarity (unit root)

  11. Prediction Models

  12. Literature Review • 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)

  13. Sources

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