Financial liquidity: An emergent phenomena

Alfredo Daniel Garcia; Martin Andrés Szybisz

doi:10.3934/jdg.2020015

Article Contents

2020, Volume 7, Issue 3: 209-224. Doi: 10.3934/jdg.2020015

This issue Previous Article The Solow-Swan model with endogenous population growth Next Article Long-range dependence in the volatility of returns in Uruguayan sovereign debt indices

Financial liquidity: An emergent phenomena

Alfredo Daniel Garcia and
Martin Andrés Szybisz^,

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

^* Corresponding author: Martin Szybisz mszybisz@hotmail.com
^* Corresponding author: Martin Szybisz mszybisz@hotmail.com

Received: February 2020

Revised: May 2020

Published: July 2020

Abstract / Introduction Full Text(HTML) Figure(8) / Table(3) Related Papers Cited by

Abstract

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.

Keywords:

Mathematics Subject Classification: Primary:91B55, 91B69;Secondary: 91G70.

Citation:

Full Text(HTML)

Figure 1. Real and Financial levels

Download: Full-size image PowerPoint slide

Figure 2. 30 Series of rule evolution separately, 1 out of 10 negative periods, maximum normal fluctuation 1%

Download: Full-size image PowerPoint slide

Figure 3. 30 Series of rules evolution separately, 1 out of 5 negative periods, maximum normal fluctuation 2 %

Download: Full-size image PowerPoint slide

Figure 4. 30 Series of Evolution of rules as a whole, stable and unstable paths

Download: Full-size image PowerPoint slide

Figure 5. Average of the final value of 30 Series of joint Evolution with standard deviation for each point, 100 periods each series

Download: Full-size image PowerPoint slide

Figure 6. 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 %

Download: Full-size image PowerPoint slide

Figure 8. Rule $ \beta \gamma $

Download: Full-size image PowerPoint slide

Figure 7. Average of the final value of 30 Series of joint Evolution with standard deviation for each point, 100 periods each series

Download: Full-size image PowerPoint slide

Table 1. 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

| Show Table

DownLoad: CSV

Table 2. 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

| Show Table

DownLoad: CSV

Table 3. 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

| Show Table

DownLoad: CSV

Related Papers

Cited by

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).