October Algorithmic Trading. Portfolio Management. Peter van Kleef

Transcription

1 October 2007 Algorithmic Trading Portfolio Management Peter van Kleef

2 Main topics Static auto-hedging Dynamic auto-hedging Portfolio optimization 2

3 Evolution of algo strategies Hit prices quicker than others based on predefined scenarios Index arbitrage and short term trend following Hit best price among different venues including optimized re-routing Delta hedge traded hit or picked off volume automatically Work single leg orders price/volatility in the market and auto-hedge Contingent order execution with delta hedging Contingent order trading with reversal and delta hedging Vega, gamma, theta hedging of trades Portfolio delta hedging Hedging the greeks of the portfolio Diversifying / rebalancing the portfolio Auto input date generation Parameterization of models Auto-adaptive learning of model parameterization Auto-adaptive model parameter selection and model building 3

4 Static auto-hedging Strategies Index arbitrage Cross venue arbitrage Pairs trading Delta hedge of options Greek hedge of options Gamma hedging Options Direct hitting/lifting Optimized passive /aggressive hitting lifting 4

5 Static auto-hedging Passive portfolio management strategies suitable for automation Index replication /tracking Benchmarking Constant duration management Currency hedging Savings / retirement plans Issues of passive portfolio management Funds have to compete with bank issued certificates Highly efficient and low margin market necessitates automation Strong growth makes avoidance of negative market impact harder 5

6 Static auto-hedging Non-self training algorithms Ideal case Can and are often used without historic data Applied for anything speed critical Need strict error and limits checking as usually medium to high frequency Can be run with intermittent supervision once fully tested but should be so only in medium to low frequency applications Are ideally suited for maximizing efficiency Once learning process is finished move to this category advised 6

7 Dynamic auto-hedging Strategies GTTA Sector and underlying rotation Volatility and correlation arbitrage Structured products hedging News trading Overlay strategies CTA / Stat arb Generally the need to do more similar trades to insure consistency of approach and expected returns and risks increases significantly with the dissimilarity between the underlyings 7

8 Dynamic auto-hedging Issues of dynamic hedging Correlations Speed Spreads Fees Through growth of hedge funds many strategies overcrowded (convert arb) Few real arbitrage strategies Many stat arb strategies are done with insufficient diversification to be called such For many biggest obstacle is to formalize and execute their strategy consistently (average maximum lot size decays with number of underlyings but has to be observed to ensure consistency) Often not all relevant data available electronically 8

9 Dynamic auto-hedging Self training algorithms Ideally need long and relevant history of clean data Are only as good as their creator (seldom come up with something new that s usable) Are volatile as a child Always need constant supervision Need strict rules and boundaries (also means limited and plausible parameter sets) Need to be seen as tools, nothing more Can aid creativity and out of the box thinking Help to focus Most helpful in the form of strictly controlled genetic algorithms and neural nets Are mostly medium to low frequency or data collection for group below 9

10 Portfolio optimization Reducing imbalances of stat arb portfolios Working off large customer/market trades with minimal market impact Rolling of derivatives contract spreads to keep risk balanced and constant Optimizing gamm/theta and other risk/reward ratios 10

11 Thank You! 11

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