In the following bachelor’s thesis I am going to present a short survey of the hedge fund industry, its regulation and the existent hedge fund strategies. Especially statistical arbitrage is explained in further detail and major performance measurement ratios are presented. In the second part, I am going to introduce a semi-variance model for statistical arbitrage. The model is compared to the standard Garch model, which is so often used in daily option trading, derivate pricing and risk management. Because investment returns are not equally distributed over time, sources for statistical arbitrage occur. The semi-variance model takes skewness into account and provides higher returns at lower volatility than the Garch model. The concept is aimed to be a synopsis of mean reversion and chart pattern detection. The computer model is generated with respect to Brownian motion and technical analysis and provide significant returns to the investment. As market efficiency hypothesis states the impossibility of arbitrage opportunities over the long run, on the other hand market anomalies significantly outstand. Connecting both elements creates a profitable trading system. The combination of both approaches delivers a sensible hedge fund concept. The out-ofsample backtest verifies out-performance and implies the need for further research in the area of higher moment CAPM and additional market timing strategies as sources of statistical arbitrage.
Table of Contents
1.1 Abstract
1.2 Structure
1.3 Table of Contents
1.4 List of Abbreviations
1.5 List of Figures and Tables
2.1 Overview of the Hedge Fund Industry
3.1 Hedge Fund Strategies Overview
3.2 Statistical Arbitrage in Detail
3.3 Performance Analysis
4.1 State of the Art in Research
4.2 Principles of Garch
4.3 Introduction of a Semi -Variance Model
4.3.1 Methodology
4.3.2 Description of Market Data
4.3.3 Prediction Power
4.3.4 Risk Measurement
4.4 Backtest with Real Options
4.4.1 Out-of-Sample Market Data
4.4.2 Performance Comparison
5.1 Conclusion
5.2 Further Research
6.1 List of Literature
6.2 Appendix
Objectives and Topics
This bachelor thesis aims to develop a novel semi-variance prediction model for statistical arbitrage in the hedge fund industry, providing a superior risk-return profile compared to the standard Garch model.
- Analysis of the hedge fund industry and existent trading strategies.
- Examination of statistical arbitrage and performance measurement ratios.
- Comparison of the semi-variance prediction model versus the Garch model.
- Empirical backtesting of the models using DAX30-Index data and real options.
- Investigation of market efficiency, anomalies, and risk management in algorithmic trading.
Excerpt from the Book
Statistical Arbitrage in Detail
Statistical arbitrage refers to highly technical short-term mean-reversion strategies involving large numbers of securities, very short holding periods and substantial computational, trading, and IT infrastructure. It involves data mining and statistical methods, as well as automated trading systems. Statistical arbitrage is actually any strategy that is bottom-up, beta-neutral in approach and uses statistical or econometric techniques in order to provide signals for execution. Signals are often generated through a contrarian mean-reversion.
The goal is to construct a tradable stationary process so that trades are entered when the process reaches an extreme value, and exited when the process reverts to some mean value. Since market inefficiencies are generally small in magnitude, so transaction costs are one reason why inefficiencies remain.
Statistical arbitrage is subject to model weakness as well as stock-specific risk. The statistical relationship on which the model is based may be spurious, or may break down due to changes in the distribution of returns on the underlying assets. Factors which the model may not be aware of having exposure to, could become the significant drivers of price action in the markets, and the inverse applies also. On a stock-specific level, there is risk of M&A activity or even default for an individual name. Such an event would immediately end any historical relationship assumed from empirical statistical analysis.
Chapter Summaries
2.1 Overview of the Hedge Fund Industry: Provides an introduction to the growth of the hedge fund sector, the role of managers, and common regulatory and structural frameworks.
3.1 Hedge Fund Strategies Overview: Categorizes diverse hedge fund investment approaches, including long/short equity, relative value, event-driven, and global macro strategies.
3.2 Statistical Arbitrage in Detail: Defines the conceptual foundations of statistical arbitrage as a technical, mean-reversion-based trading approach.
3.3 Performance Analysis: Introduces critical quantitative performance and risk metrics, such as Alpha, Sharpe-Ratio, and various Value at Risk models.
4.1 State of the Art in Research: Reviews academic perspectives on market efficiency, the random walk hypothesis, and identified market anomalies.
4.2 Principles of Garch: Explains the mathematical mechanism of Garch models for volatility forecasting and its standard application in the field.
4.3 Introduction of a Semi -Variance Model: Presents the development of the custom semi-variance model as an alternative volatility measure for loss-averse strategies.
4.4 Backtest with Real Options: Details the empirical validation of the semi-variance model through a backtest on the DAX30-Index using option spreads.
5.1 Conclusion: Summarizes the thesis findings, noting the model's outperformance relative to Garch and recommending further research directions.
5.2 Further Research: Suggests potential improvements for the model, including delta hedging, incorporating additional technical indicators, and multi-asset portfolio expansion.
Keywords
Hedge Funds, Statistical Arbitrage, Semi-Variance, Garch Model, DAX30, Volatility, Performance Measurement, Value at Risk, Market Anomalies, Mean Reversion, Option Trading, Backtesting, Algorithmic Trading, Risk Management, Quantitative Finance.
Frequently Asked Questions
What is the core focus of this thesis?
The thesis focuses on constructing and evaluating a semi-variance-based prediction model for statistical arbitrage to achieve superior risk-adjusted returns compared to traditional Garch models.
Which central thematic fields are covered?
The work covers hedge fund strategy classification, statistical arbitrage mechanics, performance metrics, volatility modeling, and market anomaly exploitation.
What is the primary research goal?
The goal is to demonstrate that a semi-variance model, which focuses on downside risk and negative return fluctuations, outperforms standard Garch models in predicting market movements for trading purposes.
Which scientific methods are employed?
The research uses time series analysis, technical analysis of chart patterns, and quantitative performance testing, including a direct empirical backtest with real options data on the DAX30.
What topics are discussed in the main body?
The main body treats theoretical foundations of hedge funds and arbitrage, literature review on market anomalies, model development (semi-variance), and a comprehensive comparative performance analysis.
Which keywords best characterize the research?
Key terms include Hedge Funds, Statistical Arbitrage, Semi-Variance, Garch, Performance Measurement, and Backtesting.
How is the semi-variance model different from standard variance?
Unlike standard variance, which considers all deviations, the semi-variance model specifically focuses on observations below the mean, providing a more accurate measure of downside risk for loss-averse investors.
What were the findings regarding the semi-variance model's performance?
The backtest indicated that the semi-variance model achieved higher returns and lower risk (Value at Risk) compared to the standard Garch model and the DAX buy-and-hold strategy during the testing period.
Is the proposed model market-neutral?
While the strategy uses call and put spreads, the author notes it is not inherently perfectly market-neutral and discusses the necessity of delta hedging with future contracts for full neutrality.
- Quote paper
- Jan Becker (Author), 2010, Making Money with statistical Arbitrage, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/194770
