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