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:
[1]

M. G. Alatriste-ContrerasJ. 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.  Google Scholar

[2]

Franklin Allen and Ana Babus, Networks in finance, The Network Challenge: Strategy, Profit, and Risk in an Interlinked World, 367 (2009). Google Scholar

[3]

I. BaltasA. Xepapadeas and A. N. Yannacopoulos, Robust portfolio decisions for financial institutions, J. Dyn. Games, 5 (2018), 61-94.  doi: 10.3934/jdg.2018006.  Google Scholar

[4]

A. Banerjee, A. G. Chandrasekhar, E. Duflo and M. O. Jackson, The diffusion of microfinance, Science, 341 (2013), 1236498. Google Scholar

[5]

N. Barberis and R. Thaler, A survey of behavioral finance, Handbook of the Economics of Finance, 1 (2003), 1053-1128.   Google Scholar

[6]

Basel Committee on Banking Supervision, A global regulatory framework for more resilient banks and banking systems, 2010. Google Scholar

[7]

B. S. Bernanke, Irreversibility, uncertainty, and cyclical investment, The Quarterly Journal of Economics, 98 (1983), 85-106.   Google Scholar

[8]

R. Bookstaber, Agent-based models for financial crises, Annual Review of Financial Economics, 9 (2017), 85-100.   Google Scholar

[9] R. Bookstaber, The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction, Princeton University Press, 2019.   Google Scholar
[10]

M. K. Brunnermeier and L. H. Pedersen, Market liquidity and funding liquidity, The Review of Financial Studies, 22 (2009), 2201-2238.   Google Scholar

[11]

J. Bullard and A. Butler, Nonlinearity and chaos in economic models: Implications for policy decisions, The Economic Journal, 103 (1993), 849-867.   Google Scholar

[12]

T. ChordiaA. Sarkar and A. Subrahmanyam, An empirical analysis of stock and bond market liquidity, The Review of Financial Studies, 18 (2005), 85-129.   Google Scholar

[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.  Google Scholar

[14]

R. Cont and J.-P. Bouchaud, Herd behavior and aggregate fluctuations in financial markets, Macroeconomic Dynamics, 4 (2000), 170-196.   Google Scholar

[15]

ECB, The monetary policy of ECB, ECB Publications, (2004). Google Scholar

[16]

E. F. Fama, The behavior of stock-market prices, The journal of Business, 38 (1965), 34-105.   Google Scholar

[17]

K. FingerD. 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.  Google Scholar

[18]

F. D. Forte, Network topology of the argentine interbank money market, Working Papers| 2019| N 87, (2019). Google Scholar

[19]

A. N. Huu, Investment under uncertainty, competition and regulation, J. Dyn. Games, 1 (2014), 579-598.  doi: 10.3934/jdg.2014.1.579.  Google Scholar

[20]

M. O. Jackson, Networks in the understanding of economic behaviors, Journal of Economic Perspectives, 28 (2014), 3-22.   Google Scholar

[21]

E. Kawamura and G. Antinolfi, Some observations on the notion of liquidity, Technical report, Society for Economic Dynamics, (2010). Google Scholar

[22]

N. Kiyotaki and J. Moore, Financial deepening, Journal of the European Economic Association, 3 (2005), 701-713.   Google Scholar

[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. Google Scholar

[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.  Google Scholar

[25]

J. S. Mill, Principles of Political Economy: With some of their Applications to Social Philosophy, 1, Longmans, Green, Reader, and Dyer, 1871. Google Scholar

[26]

B. J. Moore, Inflation and financial deepening, Journal of Development Economics, 20 (1986), 125-133.   Google Scholar

[27]

M. Nikolaidi, Bank liquidity and macroeconomic fragility: Empirical evidence for the EMU, (2016). Google Scholar

[28]

K. Nikolaou, Liquidity (risk) concepts: Definitions and interactions, ECB Working paper series, 1008 (2009). Google Scholar

[29]

J. R. Ritter, Behavioral finance, Pacific-Basin Finance Journal, 11 (2003), 429-437.   Google Scholar

[30]

J. A. Schumpeter, Theory of Economic Development, Routledge, 2017. Google Scholar

[31]

J. E. Stiglitz and A. Weiss, Credit rationing in markets with imperfect information, The American Economic Review, 71 (1981), 393-410.   Google Scholar

[32]

S. Vitali, J. B. Glattfelder and S. Battiston, The network of global corporate control, PloS One, 6 (2011). Google Scholar

show all references

References:
[1]

M. G. Alatriste-ContrerasJ. 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.  Google Scholar

[2]

Franklin Allen and Ana Babus, Networks in finance, The Network Challenge: Strategy, Profit, and Risk in an Interlinked World, 367 (2009). Google Scholar

[3]

I. BaltasA. Xepapadeas and A. N. Yannacopoulos, Robust portfolio decisions for financial institutions, J. Dyn. Games, 5 (2018), 61-94.  doi: 10.3934/jdg.2018006.  Google Scholar

[4]

A. Banerjee, A. G. Chandrasekhar, E. Duflo and M. O. Jackson, The diffusion of microfinance, Science, 341 (2013), 1236498. Google Scholar

[5]

N. Barberis and R. Thaler, A survey of behavioral finance, Handbook of the Economics of Finance, 1 (2003), 1053-1128.   Google Scholar

[6]

Basel Committee on Banking Supervision, A global regulatory framework for more resilient banks and banking systems, 2010. Google Scholar

[7]

B. S. Bernanke, Irreversibility, uncertainty, and cyclical investment, The Quarterly Journal of Economics, 98 (1983), 85-106.   Google Scholar

[8]

R. Bookstaber, Agent-based models for financial crises, Annual Review of Financial Economics, 9 (2017), 85-100.   Google Scholar

[9] R. Bookstaber, The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction, Princeton University Press, 2019.   Google Scholar
[10]

M. K. Brunnermeier and L. H. Pedersen, Market liquidity and funding liquidity, The Review of Financial Studies, 22 (2009), 2201-2238.   Google Scholar

[11]

J. Bullard and A. Butler, Nonlinearity and chaos in economic models: Implications for policy decisions, The Economic Journal, 103 (1993), 849-867.   Google Scholar

[12]

T. ChordiaA. Sarkar and A. Subrahmanyam, An empirical analysis of stock and bond market liquidity, The Review of Financial Studies, 18 (2005), 85-129.   Google Scholar

[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.  Google Scholar

[14]

R. Cont and J.-P. Bouchaud, Herd behavior and aggregate fluctuations in financial markets, Macroeconomic Dynamics, 4 (2000), 170-196.   Google Scholar

[15]

ECB, The monetary policy of ECB, ECB Publications, (2004). Google Scholar

[16]

E. F. Fama, The behavior of stock-market prices, The journal of Business, 38 (1965), 34-105.   Google Scholar

[17]

K. FingerD. 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.  Google Scholar

[18]

F. D. Forte, Network topology of the argentine interbank money market, Working Papers| 2019| N 87, (2019). Google Scholar

[19]

A. N. Huu, Investment under uncertainty, competition and regulation, J. Dyn. Games, 1 (2014), 579-598.  doi: 10.3934/jdg.2014.1.579.  Google Scholar

[20]

M. O. Jackson, Networks in the understanding of economic behaviors, Journal of Economic Perspectives, 28 (2014), 3-22.   Google Scholar

[21]

E. Kawamura and G. Antinolfi, Some observations on the notion of liquidity, Technical report, Society for Economic Dynamics, (2010). Google Scholar

[22]

N. Kiyotaki and J. Moore, Financial deepening, Journal of the European Economic Association, 3 (2005), 701-713.   Google Scholar

[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. Google Scholar

[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.  Google Scholar

[25]

J. S. Mill, Principles of Political Economy: With some of their Applications to Social Philosophy, 1, Longmans, Green, Reader, and Dyer, 1871. Google Scholar

[26]

B. J. Moore, Inflation and financial deepening, Journal of Development Economics, 20 (1986), 125-133.   Google Scholar

[27]

M. Nikolaidi, Bank liquidity and macroeconomic fragility: Empirical evidence for the EMU, (2016). Google Scholar

[28]

K. Nikolaou, Liquidity (risk) concepts: Definitions and interactions, ECB Working paper series, 1008 (2009). Google Scholar

[29]

J. R. Ritter, Behavioral finance, Pacific-Basin Finance Journal, 11 (2003), 429-437.   Google Scholar

[30]

J. A. Schumpeter, Theory of Economic Development, Routledge, 2017. Google Scholar

[31]

J. E. Stiglitz and A. Weiss, Credit rationing in markets with imperfect information, The American Economic Review, 71 (1981), 393-410.   Google Scholar

[32]

S. Vitali, J. B. Glattfelder and S. Battiston, The network of global corporate control, PloS One, 6 (2011). Google Scholar

Figure 1.  Real and Financial levels
Figure 2.  30 Series of rule evolution separately, 1 out of 10 negative periods, maximum normal fluctuation 1%
Figure 3.  30 Series of rules evolution separately, 1 out of 5 negative periods, maximum normal fluctuation 2 %
Figure 4.  30 Series of Evolution of rules as a whole, stable and unstable paths
Figure 5.  Average of the final value of 30 Series of joint Evolution with standard deviation for each point, 100 periods each series
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 %
Figure 8.  Rule $ \beta \gamma $
Figure 7.  Average of the final value of 30 Series of joint Evolution with standard deviation for each point, 100 periods each series
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
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
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
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
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
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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