Statistical Arbitrage
This two day workshop introduces delegates to statistical arbitrage strategies, including pairs trading, with particular reference to research, testing and implementation. Relevant software (MATLAB) will be used throughout the workshop to illustrate examples and to help students practice the essential steps in developing a stat arb strategy. No prior knowledge of MATLAB is required. (Note: Students will be able to apply the principles learnt during the workshop, regardless of which software they choose to use thereafter).
This two day workshop introduces delegates to statistical arbitrage strategies, including pairs trading, with particular reference to research, testing and implementation. Relevant software (EXCEL) will be used throughout the workshop to illustrate examples and to help students practice the essential steps in developing a stat arb strategy.
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Dates coming soon |
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Duration: Two days (9.00am to 5.00pm) |
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Location: The Tower Hotel – London E1, UK |
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Trainer: Christian Schaller |
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Course fee: £1990 + VAT – Register online |
Course Outline
Overview
+ The different types of statistical arbitrage strategies
+ Stationarity, cointegration, mean reversion, and momentum
Required tools in EXCEL
+ The pros and cons of using EXCEL
+ Quick survey of required functions
+ Writing custom add-in functions using VBA
+ Exercises: building some useful utilities for trading research
Directional Trading
+ Concept of stationarity, and why it is useful
+ Statistical test for stationarity
+ Exercise: Testing for stationarity
+ Testing for mean-reversion: half-life based on Ornstein-Uhlenbeck formula
+ Why is computing half-life better than computing average holding period?
+ Exercise: Computing the half-life of mean-reversion
Pairs and Triplets Trading
+ Concept of cointegration and why it is useful
+ How is cointegration different from correlation?
+ Statistical tests for cointegration: cadf and Johansen
+ Exercise: Find out if GLD-GDX is cointegrating
+ Finding the best hedge ratio
+ Exercise: Backtesting a Bollinger Band pairs strategy
+ Trading cointegrated triplets
+ Exercise: Backtesting a Bollinger Band triplet strategy
+ What are the best markets to pairs trade?
Index Arbitrage
+ Trading an ETF against a basket of its component stocks
+ Two ways of constructing a basket
+ Exercise: Backtesting an index arbitrage trading model
Long-Short Portfolio
+ Ranking stocks in an index based on various simple returns criteria
+ How minor variations in strategies can produce big differences in returns
+ Important biases and pitfalls in backtesting long-short portfolio strategies
+ Exercise: Backtesting variations of a long-short portfolio strategy
Course Outline
Overview
+ The different types of statistical arbitrage strategies
+ Stationarity, cointegration, mean reversion, and momentum
Required tools in EXCEL
+ The pros and cons of using EXCEL
+ Quick survey of required functions
+ Writing custom add-in functions using VBA
+ Exercises: building some useful utilities for trading research
Directional Trading
+ Concept of stationarity, and why it is useful
+ Statistical test for stationarity
+ Exercise: Testing for stationarity
+ Testing for mean-reversion: half-life based on Ornstein-Uhlenbeck formula
+ Why is computing half-life better than computing average holding period?
+ Exercise: Computing the half-life of mean-reversion
Pairs and Triplets Trading
+ Pairs and triplets trading
+ Concept of cointegration and why it is useful
+ How is cointegration different from correlation?
+ Statistical tests for cointegration
+ Exercise: Find out if GLD-GDX is cointegrating
+ Finding the best hedge ratio
+ Trading cointegrated triplets
+ What are the best markets to pairs trade?
Stop Losses, Profit Targets, and Dynamic Scaling
+ When do stop losses stop losses?
+ Setting optimal profit targets
+ The purpose of dynamic scaling
+ Using simulation to assess stops, targets, and scaling policies
Managing a Portfolio of Mean-Reverting Trades
+ Formulating a well-posed problem in financial economics
+ Formulating a well-posed mathematical problem
+ Solving well-posed investment problems
+ Implementing solutions in practice
+ Incorporating risk management
