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One-dimensional reaction-diffusion and deposition-etching models adapted for stock-market simulations František Slanina Institute of Physics, Academy of Sciences of the Czech Republic, Prague (In collaboration with: Andrej Svorenčík, Sorin Solomon) slanina@fzu.cz www.fzu.cz/~slanina slanina@fzu.czwww.fzu.cz/~slanina ☻Order book phenomenology ☻Early models ☻Maslov model ☻Towards more realistic models ☻Back again: schematic models
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Stylized facts Return distribution scales Hurst exponent Scaling function has power-law tail return distribution exponent (``inverse cubic law'') long-time autocorr. of abs. returns or
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Order book Cisco: profile relative to current price [D. Challet and R. Stinchcombe, Physica A 300, 285 (2001).]
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order size market orders (line: limit orders (line: [S. Maslov and M. Mills, Physica A 299, 234 (2001).]
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order position (in ticks, relative to current price) Paris bourse line: NASDAQ line: [J.-P. Bouchaud, M. Mézard. and M. Potters, Quant. Finance 2, 251 (2002); M. Potters J.-P. and Bouchaud, Physica A 324, 133 (2003).]
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average order density (tail ) [J.-P. Bouchaud, M. Mézard. and M. Potters, Quant. Finance 2, 251 (2002).] [R. Weber and B. Rosenow, cond-mat/0311457.] D. Farmer and F. Lillo, Quant. Finance 4, C7 (2004). NYSE and London SE (line: ) price impact virtual ( ) and actual ( ) impact
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Origin of power-law tails in return distribution gaps vs. returns distribution (tail: ) [J. D. Farmer et al. cond-mat/0312703.]
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Zero intelligence Dhananjay K. Gode; Shyam Sunder The Journal of Political Economy, Vol. 101, No. 1. (Feb., 1993), pp. 119-137 „Individual irrationality produces collective rationality“
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Bak-Paczuski-Shubik Reaction-diffusion process for 2 species: A asks B bids,
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Hurst H = 1/4 unrealistic. Diffusion unrealistic. Copying placement of orders: promising. Hurst plotReturn distribution
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Stigler (1964) Bids and asks placed randomly in alloved interval
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„free“ Stigler model: allowed interval follows price standard ( ) and free ( ) Return distribution Absolute return correlation Return distribution unrealistic H=0 unrealistic
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„Genoa market model“ [Adapted after M. Raberto, S. Cincitti, S. M. Focardi, and M. Marchesi, Physica A 299, 319 (2001) and subsequent] price
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Phase transition Phase diagram volatility
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Hurst plot Stigler, free Stigler, Genoa
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Maslov model deposition-etching process (+ evaporation) Local deposition: new limit orders at distance 1 from current price
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Hurst plot Without evaporation and with evaporation probability
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Return distribution: scaling inset :
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Return distribution: no scaling No evaporation ( ) evaporation probability
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Autocorrelation of absolute returns With and without evaporation
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Mean-field for Maslov Mean-field approx.: homogeneous upper lower order volume potential price changes: Vector performs random matrix multiplicative process [F. Slanina, Phys. Rev. E 64, 056136 (2001)]
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Probability distribution of price changes (after m limit and one market order): assuming power-law tail Non-trivial solution Why the exponent differs? Assumed constant density of orders. linear
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Uniform deposition (Farmer) model Global deposition: uniform rate [M. G. Daniels, J. D. Farmer, L. Gillemot, G. Iori, and E. Smith, Phys. Rev. Lett. 90, 108102 (2003) and subsequent]
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Return distribution Different evap. Prob. Different densities Different lags (no scaling!)
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Hurst plot No scaling regime Autocorrelation No power law
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Interacting gaps model [A. Svorenčík and F. Slanina, Eur. Phys. J. B 57, 453-462 (2007).] “collision” of a pair of intervals 1.collapse with probability 2.reaction with probability
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Histogram of returnsAutocorrelation of absolute returns
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Mean-field for IGM Master equation for gap distribution Stationary solution 1.: exponential tail special case for Stationary solution 2.: Condensate + power-law tail
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Conclusions BPS, Stigler, uniform deposition Genoa Maslov model with evaporation } candidates Outlook Improvement of schematic models Market maker – merge with Minority Game
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