Slide 1
Modeling liquidity, risk
and transaction costs in the LSE using low intelligence agents
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Goals
Why economics is hard
Alternative
A few seminal papers
illustrating zero intelligence
Three models
Continuous double auction
Patient trading
Impatient trading
Order cancellation
Limit order collaborators 1
ZI model
(Unrealistic but somewhat tractable)
Achievements of ZI model so
far
Comparison to LSE data
London screen
Upstairs, downstairs
Slide 19
Parameters of model
Testing prediction of
spread
Predicted price diffusion
rate
Top 10 Russian jokes, Oct.
23, 2003
Volatility autocorrelation
Fat tails in prices
Price fluctuations have fat
tails
Investigation of fat tails
Price changes are almost
independent of volume
A typical large price
change
Gap distribution
vs.
price distribution
Tail exponent of rtns. Vs.
gap
Return vs. number trades
Building a better empirical
model for real order flow
Autocorrelation of orders
Unconditional order
placement distribution
Distribution of relative
limit price conditioned on spread
Limit order placement
conditional on spread
Empirical model
Ecology of arbitrage
Market ecology
Building a market ecology
Two low intelligence agents
Profits vs. agent trading
volume
The road to efficiency is
not straight
Practical implications
Conclusions
Upstairs, downstairs
Price changes are almost
independent of volume (NYSE)
Market (price) impact
Market impact fn- dim units
Market impact fn- non dim
units