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1. Disclaimer. The information contained herein, including any expression of opinion, and any information which accompanies this presentation or which is supplied subsequently, has been obtained from or is based upon sources believed to be reliable but has not been independently verified and is not
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1. 0 FX Market Dynamics Methods for Trading, Forecasting & Risk Assessment
2. 1 Disclaimer The information contained herein, including any expression of opinion, and any information which accompanies this presentation or which is supplied subsequently, has been obtained from or is based upon sources believed to be reliable but has not been independently verified and is not guaranteed as to accuracy or completeness, although Société Générale and its affiliated companies (“SG”) believe it to be fair and not misleading
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3. 2 Forex Markets – Deep, Liquid and Accessible
$1.9trn average daily turnover
Almost continuous trading 24/7
Growth in currency overlay mandates
Broad “understanding” of market
4. 3 Analysts Comes in All Shapes and Sizes Commentators
Economists
Technical Analysts
Quant analysts
5. 4 Styles of Analysis - All Are Important Commentary
Econometrics / Statistical methods
Technical Analysis
Quantitative trading
Signal generation using all available datasets
Beg, borrow and steal from other areas of expertise
News interpretation
Data interpretation News interpretation
Data interpretation
6. 5 A Combined Approach to FX - A Scorecard Build a committee / agenda / process
Set up infrastructure for FX views
Asset allocation made from aggregating views
Deploy trades and risk management
7. 6 Pitfalls of the Scorecard Market timing and forecast objectives
Arbitrary weighting scheme
Balancing the number of trades
More of a management tool than trading tool Budget rate versus hedging targets
Netting and cash flow considerations
“Consensus” risk from the panel
Budget rate versus hedging targets
Netting and cash flow considerations
“Consensus” risk from the panel
8. 7 The Commentators Approach News for market consumption
Sensitivity to ‘new’ information
Broader knowledge of ‘anecdotal’ information
Typically hard to quantify
Selling a view
Is there a dataset to test the hypothesis
Scheduled or otherwise
Will not stick their neck out
Backward looking Is there a dataset to test the hypothesis
Scheduled or otherwise
Will not stick their neck out
Backward looking
9. 8 Economic Analysis for Markets Data released each week, month or quarter
Monetary policy is decided by people
Inflation targeting regime versus growth models
Huge debate about market relevance
Markets can deviate from the economic backdrop
Markets are volatile
Economics may not be the largest driving force
10. 9 Econometrics and Statistical Methods Linear regression
Many variables feed into economy
Only so many are relevant
Forecast target: average or volatility
11. 10 Examples Include… ARMA, ARIMA family of models
Seasonality
Multiple VAR models
Multifactor models
ARCH GARCH type models
Volatility models
Parametric and non-parametric approaches
Distribution assumptions
12. 11 Problems Associated With Statistical Methods Over fitting and parameterisation
Sample bias
Unstable correlation structures
Explanation factor
13. 12 Investigating Anecdotal Evidence
14. 13 Case Study – Gold & AUD/USD
15. 14 Case Study Conclusions
16. 15 Extending the Case Study Framework
17. 16 Statistical Models – Multiple VAR Make the assumption of normal relationships
Assume stable relationship over time
Most used explain use instantaneous relations
Weighting scheme problematic
18. 17 Technical Analysis Trend following tendencies of markets
Contrarian indicators to pick turning points
Chartist approaches – market price determined
Self fulfilling prophecies on levels
19. 18 Trading Technically in 2006
20. 19 Trading Technically - Highlights
21. 20 Herding, Behavior and Vol Clustering Taking a position because everyone else is
Being a ‘tracker’ not an ‘outlier’
Never typically used in a positive sense
Often associated with ‘stops’
Psychology plays a large part
Datasets on other variables
22. 21 Speculative Positioning
Speculative positions reports are useful
Positions are explained by momentum & carry
Market can stay extreme for significant periods
Deployed as a contrarian indicator
23. 22 Speculative FX Positions
24. 23 Price Action Problems
Many indicators to choose from
Split into trend following or mean reverting
Quantitative trading possible
Benchmarking issues / slippage
25. 24 Fuzzy Logic and GA GA = genetic algorithm
Learning by simplifying inputs
Garbage in – garbage out
Fuzzy Logic
Loosening strict conditions of GA – conditionality
Theoretical way to handle large state space
Tools and expertise required
Deep into ‘black box’ territory
26. 25 Ex-post Rationalization, Hindsight Economics
27. 26 Moving Into the Quantitative Space
28. 27 Quantitative Trading
29. 28 Market Forces That Are Quantifiable
Price action
Economic news
Monetary policy decisions
Political calendar events
30. 29 Markets Are Volatile - But Have Structure
31. 30 A Random Walk With Drift
32. 31 Mathematically Speaking… We live in a “normal” world
Confidence intervals in all estimations
Market prices are “non-stationary”
Make mathematical tests on returns
33. 32 But the Short Term Markets Know… Money made in non-normal times event risks
Timing of trades is imperative
Data and event risks matter
How to frame the problem is critical
There is a microstructure to the market
34. 33 Cyclical Activity
35. 34 Last Weeks “Microstructure” Details
36. 35 Impact of Economic News
Economic data is much more predictable
Key economic data typically has a “consensus”
There are ways to re-frame economic news
Better / Worse than expected
Slowing or accelerating
37. 36 Economic Data and Ways to Re-frame
38. 37 We Can Re-base or Re-frame Time Series Price based
Classical time series analysis
Technical analysis
Tick based
Volume rebased price series
Economic based
Economic calendar known in advance
39. 38 Event Horizons Analysis Can use an implied distribution
Can use historical data
VaR and extreme value theory
Can be used to estimate volatility
40. 39 Empirical Distribution of EUR/USD
41. 40 Data Mining – A Useful Tool A technique that has a bad name
Empirical results based research
Non-parametric
Often not benchmarked
42. 41 Monetary Policy Decisions
Policy communication is important
Made through regular meetings & publications
How can the impact of this news be understood
This is typically not the same over time
43. 42 Sterling & UK Rate Decisions
‘Surprise’ rate decisions
How does the market react
Build the reaction function
Risk over over interpretation
44. 43 What About Sterling Seasonality?
Cable has had a bias to appreciate in December
Must have to see a rational for the seasonal
Once discovered much easier to push story
This year was very similar to 2004
45. 44 Political Uncertainty
Meetings & elections are known in advance
Uncertainty is disliked by the markets
Sentiment around future event risks is not inefficiently priced in by derivative markets
Data mining lends itself to these problems
46. 45 Conclusions and Thoughts Many ways to undertake FX analysis
No one way better than another
But there are many pitfalls to be avoided
Simply look forward and for the next big issue
47. 46