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Presented at EDAMBA summer school, Soréze (France) 23 July – 27 July 2009 Data Sourcing, Statistical Processing and Time Series Analysis An Example from Research into Hedge Fund Investments Presenter: University: Supervisor: Research Title: Contact: Florian Boehlandt University of Stellenbosch – Business School Prof Eon Smit Prof Niel Krige Pricing hedge funds a.k.a. The sustainability of parametric and semiparametric pricing models as estimators of hedge fund performance [email protected] ‘In the business world, the rearview mirror is always clearer than the windshield’ - Warren Buffett - 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 Research Approach Research Philosophy Logical-positivistic, deductive research: Postulation of hypotheses that are tested via standard statistical procedures Research Approach Empirical analysis: Interpreting the quality of pricing models on the basis of historical data Data Sourcing External secondary data: Historic time series adjusted for data-bias effects Data Sourcing Data Sources Hedge Fund Databases Financial Databases Risk Simulation Monte Carlo (Solver) Confidence (RiskSim) CISDM/MAR DATA POOL Data Treatment Data Treatment DATA POOL Risk Simulation Statistical Processing Excel / VBA Statistica EViews FACTOR ANALYSIS STATISTICAL CLUSTERING MODEL BUILDING Data Import Access Database Information • Code • Fund (Name) • Main Strategy Performance • MM_DD_YYYY (Date) • Yield • Ptype (ROI or AUM) System Information • Leverage (Yes/No) Excel Pivot table report 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 Data Bias Survivorship Inclusion of graveyard funds SelfSelection Multiple databases Database Instant History Look-ahead Rolling-window observation / Incubation period Statistical tests for TSA • Regression Statistics (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) Prediction Models Prediction Models AR GLS PCA Polynomial Fitting Constrained ARMA Univariate Taylor Series Lagrange ARIMA Multivariate Higher CoMoments KKT Conditional Simulation 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) Sources 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=00221082%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 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