# American Institute of Mathematical Sciences

July  2020, 7(3): 209-224. doi: 10.3934/jdg.2020015

## Financial liquidity: An emergent phenomena

 Universidad de Buenos Aires, Buenos Aires, Argentina, Av. Cordoba 2122, C1120 AAQ

* Corresponding author: Martin Szybisz mszybisz@hotmail.com

Received  February 2020 Revised  May 2020 Published  July 2020

In a complex system model we simulate runs for different strategies of economic agents to study diverse types of fluctuations. The liquidity of financial assets arises as a result of agent's interaction and not as intrinsic properties of the assets. Small differences in the strategic rules adopted by the agents lead to divergent paths of market liquidity. Our simulation also supports the idea that the higher the maximum local allowed fluctuation the higher the path divergence.

Citation: Alfredo Daniel Garcia, Martin Andrés Szybisz. Financial liquidity: An emergent phenomena. Journal of Dynamics & Games, 2020, 7 (3) : 209-224. doi: 10.3934/jdg.2020015
##### References:

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##### References:
Real and Financial levels
30 Series of rule evolution separately, 1 out of 10 negative periods, maximum normal fluctuation 1%
30 Series of rules evolution separately, 1 out of 5 negative periods, maximum normal fluctuation 2 %
30 Series of Evolution of rules as a whole, stable and unstable paths
Average of the final value of 30 Series of joint Evolution with standard deviation for each point, 100 periods each series
Lower bound for rule $\beta$. For rule $\beta$ 30 Series of rule evolution separately, 1 out of 10 negative periods, maximum normal fluctuation 1%. Upper bound for rule $\gamma$. For rule $\gamma$ 30 Series of rules evolution separately, 1 out of 5 negative periods, maximum normal fluctuation 2 %
Rule $\beta \gamma$
Average of the final value of 30 Series of joint Evolution with standard deviation for each point, 100 periods each series
Rules and Scenarios of Simulations
 Scenarios If negative fluctuation (ng) Stable Path every 10 periods max normal 1% Unstable Path every 5 periods max normal 2% action Rules Rule $\beta$ one period multiplies by 10 ng multiplies by 5 ng Rule $\gamma$ three periods multiplies by 10 ng multiplies by 5 ng
 Scenarios If negative fluctuation (ng) Stable Path every 10 periods max normal 1% Unstable Path every 5 periods max normal 2% action Rules Rule $\beta$ one period multiplies by 10 ng multiplies by 5 ng Rule $\gamma$ three periods multiplies by 10 ng multiplies by 5 ng
Average and Standard Deviation of final values of series
 Stable Path Unstable Path Average Std dev Average Std dev Rule $\beta$ alone 0.0349 0.0561 0.0053 0.0080 Rule $\gamma$ alone 0.777 0.0492 0.9049 0.1379 Rule $\beta$ 56% 45% 0.2044 0.1080 0.1970 0.1779
 Stable Path Unstable Path Average Std dev Average Std dev Rule $\beta$ alone 0.0349 0.0561 0.0053 0.0080 Rule $\gamma$ alone 0.777 0.0492 0.9049 0.1379 Rule $\beta$ 56% 45% 0.2044 0.1080 0.1970 0.1779
Average and Standard Deviation of final values of series
 Stable Path Unstable Path Average Std dev Average Std dev Rule $\beta$ alone 0.2699 0.1445 0.1655 0.1212 Rule $\gamma$ alone 0.7024 0.0488 0.7837 0.0845 Rule $\beta$ $\nexists$ 92% $\nexists$ $\nexists$ 0.2001 0.1312
 Stable Path Unstable Path Average Std dev Average Std dev Rule $\beta$ alone 0.2699 0.1445 0.1655 0.1212 Rule $\gamma$ alone 0.7024 0.0488 0.7837 0.0845 Rule $\beta$ $\nexists$ 92% $\nexists$ $\nexists$ 0.2001 0.1312
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