# 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 and Games, 2020, 7 (3) : 209-224. doi: 10.3934/jdg.2020015
##### References:
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show all references

##### References:
 [1] M. G. Alatriste-Contreras, J. G. Brida and M. P. Anyul, Structural change and economic dynamics: Rethinking from the complexity approach, J. Dyn. Games, 6 (2019), 87-106.  doi: 10.3934/jdg.2019007. [2] Franklin Allen and Ana Babus, Networks in finance, The Network Challenge: Strategy, Profit, and Risk in an Interlinked World, 367 (2009). [3] I. Baltas, A. Xepapadeas and A. N. Yannacopoulos, Robust portfolio decisions for financial institutions, J. Dyn. Games, 5 (2018), 61-94.  doi: 10.3934/jdg.2018006. [4] A. Banerjee, A. G. Chandrasekhar, E. Duflo and M. O. Jackson, The diffusion of microfinance, Science, 341 (2013), 1236498. [5] N. Barberis and R. Thaler, A survey of behavioral finance, Handbook of the Economics of Finance, 1 (2003), 1053-1128. [6] Basel Committee on Banking Supervision, A global regulatory framework for more resilient banks and banking systems, 2010. [7] B. S. Bernanke, Irreversibility, uncertainty, and cyclical investment, The Quarterly Journal of Economics, 98 (1983), 85-106. [8] R. Bookstaber, Agent-based models for financial crises, Annual Review of Financial Economics, 9 (2017), 85-100. [9] R. Bookstaber, The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction, Princeton University Press, 2019. [10] M. K. Brunnermeier and L. H. Pedersen, Market liquidity and funding liquidity, The Review of Financial Studies, 22 (2009), 2201-2238. [11] J. Bullard and A. Butler, Nonlinearity and chaos in economic models: Implications for policy decisions, The Economic Journal, 103 (1993), 849-867. [12] T. Chordia, A. Sarkar and A. Subrahmanyam, An empirical analysis of stock and bond market liquidity, The Review of Financial Studies, 18 (2005), 85-129. [13] R. Cont, A. Moussa and E. B. Santos, Network structure and systemic risk in banking systems, SSRN Electronic Journal, (2010). doi: 10.2139/ssrn.1733528. [14] R. Cont and J.-P. Bouchaud, Herd behavior and aggregate fluctuations in financial markets, Macroeconomic Dynamics, 4 (2000), 170-196. [15] ECB, The monetary policy of ECB, ECB Publications, (2004). [16] E. F. Fama, The behavior of stock-market prices, The journal of Business, 38 (1965), 34-105. [17] K. Finger, D. Fricke and T. Lux, Network analysis of the e-MID overnight money market: The informational value of different aggregation levels for intrinsic dynamic processes, Comput. Manag. Sci., 10 (2013), 187-211.  doi: 10.1007/s10287-013-0171-9. [18] F. D. Forte, Network topology of the argentine interbank money market, Working Papers| 2019| N 87, (2019). [19] A. N. Huu, Investment under uncertainty, competition and regulation, J. Dyn. Games, 1 (2014), 579-598.  doi: 10.3934/jdg.2014.1.579. [20] M. O. Jackson, Networks in the understanding of economic behaviors, Journal of Economic Perspectives, 28 (2014), 3-22. [21] E. Kawamura and G. Antinolfi, Some observations on the notion of liquidity, Technical report, Society for Economic Dynamics, (2010). [22] N. Kiyotaki and J. Moore, Financial deepening, Journal of the European Economic Association, 3 (2005), 701-713. [23] L. Loepfe, A. Cabrales and A. Sánchez, Towards a proper assignment of systemic risk: The combined roles of network topology and shock characteristics, PloS One, 8 (2013), e77526. [24] S. London and F. Tohmé, Economic evolution and uncertainty: Transitions and structural changes, J. Dyn. Games, 6 (2019), 149-158.  doi: 10.3934/jdg.2019011. [25] J. S. Mill, Principles of Political Economy: With some of their Applications to Social Philosophy, 1, Longmans, Green, Reader, and Dyer, 1871. [26] B. J. Moore, Inflation and financial deepening, Journal of Development Economics, 20 (1986), 125-133. [27] M. Nikolaidi, Bank liquidity and macroeconomic fragility: Empirical evidence for the EMU, (2016). [28] K. Nikolaou, Liquidity (risk) concepts: Definitions and interactions, ECB Working paper series, 1008 (2009). [29] J. R. Ritter, Behavioral finance, Pacific-Basin Finance Journal, 11 (2003), 429-437. [30] J. A. Schumpeter, Theory of Economic Development, Routledge, 2017. [31] J. E. Stiglitz and A. Weiss, Credit rationing in markets with imperfect information, The American Economic Review, 71 (1981), 393-410. [32] S. Vitali, J. B. Glattfelder and S. Battiston, The network of global corporate control, PloS One, 6 (2011).
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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