Complexity, big data and financial stability

Charilaos Mertzanis; Charilaos Mertzanis

doi:10.3934/QFE.2018.3.637

Quantitative Finance and Economics

2018, Volume 2, Issue 3: 637-660. doi: 10.3934/QFE.2018.3.637

Previous Article Next Article

Research article

Complexity, big data and financial stability

Charilaos Mertzanis ^,

Hellenic Open University, 18 Aristotelous str., Partas 26335, Greece

Received: 11 April 2018 Accepted: 27 June 2018 Published: 17 August 2018
JEL Codes: D85, C55, C80, E30

Financial stability analysis and policy should concentrate on accurate price discovery for complex instruments, realistic financial information generation processes, and system-wide risk materializing within complex financial networks. To this end, complexity analysis can make a useful contribution. The effectiveness of these approaches rests crucially on the quality and standardization of big data that today characterized financial activity throughout the globe. Considerable progress is made over the past year in the development of a key element of such standardization—the global legal entity identifier system. It aims to uniquely identify parties to financial transactions across the globe. While this is a necessary and key first step, it is only one step towards a strong, flexible and adaptable global data infrastructure conducive to financial stability policy.
- financial stability,
- complexity,
- big data,
- global legal entity identifier
Citation: Charilaos Mertzanis. Complexity, big data and financial stability[J]. Quantitative Finance and Economics, 2018, 2(3): 637-660. doi: 10.3934/QFE.2018.3.637

Related Papers:

Abstract

Financial stability analysis and policy should concentrate on accurate price discovery for complex instruments, realistic financial information generation processes, and system-wide risk materializing within complex financial networks. To this end, complexity analysis can make a useful contribution. The effectiveness of these approaches rests crucially on the quality and standardization of big data that today characterized financial activity throughout the globe. Considerable progress is made over the past year in the development of a key element of such standardization—the global legal entity identifier system. It aims to uniquely identify parties to financial transactions across the globe. While this is a necessary and key first step, it is only one step towards a strong, flexible and adaptable global data infrastructure conducive to financial stability policy.

References

[1]	Allen F, Morris S, Shin HS (2006) Beauty contests and iterated expectations in asset markets. Soc Sci Electron Publishing 19: 719–752.
[2]	Anand K, Gai P, Kapadia S, et al. (2012) A network model of financial system resilience. J Econ Behav Organization 85: 219–235.
[3]	Arora S, Barak B, Brunnermeiery M, et al. (2009) Computational complexity and information asymmetry in financial products. Princeton University, April, manuscript.
[4]	Arthur WB (1995) Complexity in economic and financial markets. Complexity 1: 20–25. doi: 10.1002/cplx.6130010106
[5]	Arthur WB (1999) Complexity and the economy. Science 284: 107–109. doi: 10.1126/science.284.5411.107
[6]	Artzner P, Delbaen F, Eber JM, et al. (1999) Coherent measures of risk. Math Finance 9: 203–228. doi: 10.1111/1467-9965.00068
[7]	Berman GE, Transformational technologies, market structure, and the SEC. Remarks to the SIFMA TECH Conference, New York, 2015. Available from: http://www.sec.gov/News/Speech/Detail/Speech/1365171575716.
[8]	Bier W, Data requirements and improvements necessary for assessing the health of systemically important financial institutions, ECB, 2009. Available from: http://www.ieo?imf.org/external/np/seminars/eng/2009/usersconf/pdf/Bier.pdf.
[9]	Borland L (2005) Long-range memory and non-extensivity in financial markets. Europhys News 36: 228–231. doi: 10.1051/epn:2005615
[10]	Bouchaud JF, Potters M (2003) Theory of financial risk and derivative pricing: From statistical physics to risk management. Cambridge University Press.
[11]	Brammertz W, Akkizidis I, Breymann W, et al. (2009) Unified financial analysis: The missing links of finance. Bull Sch Health Sci Tohoku Univ 17: 15–22.
[12]	Brunnermeier MK, Crocket A, Goodhart C, et al. (2009) The Fundamental Principles of Financial Regulation. Geneva Reports on the World Economy, 11.
[13]	Caballero RJ, Simsek A (2009) Complexity and financial panics. Soc Sci Electron Publishing, 37.
[14]	Chakrabarti BK, Chakraborti A (2010) Fifteen years of econophysics research. Papers 76: 293–296.
[15]	Chen YT, Sun EW, Lin YB (2018) Coherent quality management for big data systems: A dynamic approach for stochastic time consistence. Ann Oper Res.
[16]	Christophers B (2009) Complexity, finance, and progress in human geography. Prog Hum Geogr 33: 807–824. doi: 10.1177/0309132509336508
[17]	Cont R (2001) Empirical properties of asset returns: Stylized facts and statistical issues. Quant Finance 1: 223–236. doi: 10.1080/713665670
[18]	Dong XL, Srivastava D (2013) Big data integration. IEEE 29th International Conference on Data Engineering (ICDE), Brisbane, Australia, 1245–1248.
[19]	Fantazzini D, Maggi M (2013) Computing reliable default probabilities in turbulent times, In: Wehn CS, Hoppe C, Gregoriou GN, (Eds.) Rethinking Valuation and Pricing Models, 241–255.
[20]	Farmer JD, Gallegati M, Hommes C, et al. (2012) A complex systems approach to constructing better models for managing financial markets and the economy. Eur Phys J Spec Top 214: 295–310. doi: 10.1140/epjst/e2012-01696-9
[21]	Flood M, Mendelowitz A, Nichols W (2013) Monitoring financial stability in a complex world, In: Lemieux V, (ed.) Financial Analysis and Risk Management, Springer-Verlag Berlin Heidelberg.
[22]	Gromb D, Vayanos D (2010) Limits of arbitrage: The state of the theory. Ann Rev Financ Econ 2: 251–275. doi: 10.1146/annurev-financial-073009-104107
[23]	Haldane A (2009) Rethinking the financial network. Speech as the Financial Student Association, Amsterdam, 28 April, Amsterdam.
[24]	Halevy A, Rajaraman A, Ordille J (2006) Data integration: The teenage years, In: Proceedings of the 32nd international conference on very large databases (VLDB '06), 9–16.
[25]	Hamilton R, Jenkinson N, Penalver A (2007) Innovation and integration in financial markets and the implications for financial stability, Reserve Bank of Australia. Available from: http://www.rba.gov.au/publications/confs/2007/hamilton?jenkinson?penalver.pdf.
[26]	Idier J, Lamé G, Mesonnier JS (2013) How useful is the marginal expected shortfall for the measurement of systemic exposure? A practical assessment. J Banking Finance 47: 134–146.
[27]	Johnson NF, Jefferies P, Pak MH (2003) Financial Market Complexity: What Physicists Can Tell Us About Market Behavior. Oxford University Press, Oxford.
[28]	Keim DB (2008) Financial market anomalies, In: The New Palgrave Dictionary of Economics, Second Edition.
[29]	Latora V, Marchiori M (2004) The architecture of complex systems, In: Gell-Mann M, Tsallis C, (Eds) Nonextensive Entropy-Interdisciplinary Applications. Oxford University Press.
[30]	Lin EM, Sun EW, Yu MT (2018) Systemic Risk, Financial Markets, and Performance of Financial Institutions. Ann Oper Res 262: 579–603.
[31]	Lo A (2009) The feasibility of systemic risk measurement. Testimony for the House Financial Services Committee Hearing On Systemic Risk Regulation. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1497682.
[32]	Mandelbrot B (1997) Fractals and Scaling in Finance. New York: Springer-Verlag.
[33]	Mertzanis C (2013) Risk management challenges after financial crisis. Econ Notes 42: 285–319. doi: 10.1111/j.1468-0300.2013.12011.x
[34]	Mertzanis C (2014a) Complexity analysis and risk management in finance, In: Batten JA, Wagner NF, (Eds) Risk Management Post Financial Crisis: A Period of Monetary Easing, Contemporary Studies in Economic and Financial Analysis, Emerald Group Publishing, 15–40.
[35]	Mertzanis C (2014b) Complexity theory and systemic risk: Some methodological issues, In: Pardalos PM, (Ed) Network Models in Economics and Finance, Springer-Verlang, 199–239.
[36]	Miller J, Page S (2007) Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.
[37]	Morris S, Shin HS (2007) Optimal Communication. J Eur Econ Assoc 5: 594–602. doi: 10.1162/jeea.2007.5.2-3.594
[38]	O'Hara M (2015) High frequency market microstructure. J Financ Econ 116: 257–270. doi: 10.1016/j.jfineco.2015.01.003
[39]	Persaud A (2000) Sending the herd off the cliff edge: The disturbing interaction between herding and market-sensitive risk management systems. J Risk Finance 2: 59–65. doi: 10.1108/eb022947
[40]	Persaud A (2003) Liquidity Black Holes: Understanding, Quantifying and Managing Financial Liquidity. Risk Books.
[41]	Persaud A (2008) Valuation, Regulation, Liquidity. Financ Stab Rev 12: 83–75.
[42]	Rosenthal A, Seligman L (2011) Data integration for systemic risk in the financial system, In: Fouque JP, Langsam JA, (Eds) Handbook for Systemic Risk, Cambridge University Press.
[43]	Shin HS (2008) Risk and Liquidity. Clarendon Lectures in Finance, Oxford: OUP.
[44]	Shleifer A (2000) Inefficient Markets: An Introduction to Behavioral Finance. Oxford University Press, Oxford.
[45]	Shleifer A, Vishny RW (1997) The limits of arbitrage. J Finance 52: 35–55. doi: 10.1111/j.1540-6261.1997.tb03807.x
[46]	Simon H (1955) A behavioral model of rational choice. Q J Econ 69: 99–118. doi: 10.2307/1884852
[47]	Soramaki K, Bech ML, Arnold J, et al. (2007) The topology of interbank payment flows. Phys A 379: 317–333. doi: 10.1016/j.physa.2006.11.093
[48]	Sornette D (2003) Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press.
[49]	Sprott JC (2003) Chaos and Time-Series Analysis, Oxford University Press.
[50]	Stanley HE, Amaral LAN, Gopikrishnan P, et al. (2001) Quantifying empirical economic fluctuations using the organizing principles of scale invariance and universality, In: Takayasu H (Ed) Empirical Science of Financial Fluctuations: The Advent of Econophysics, Springer-Verlag, 3–11.
[51]	Sun EW, Chen YT, Yu MT (2015) Generalized optimal wavelet decomposing algorithm for big financial data. Int J Prod Econ 165: 194–214. doi: 10.1016/j.ijpe.2014.12.033
[52]	Sun EW, Meinl T (2012) A new wavelet-based denoising algorithm for high-frequency financial data mining. Eur J Oper Res 217: 589–599.
[53]	Tett G (2009) Fool's Gold. Free Press.
[54]	The Basel Committee on Banking Supervision (BCBS), Principles for effective risk data aggregation and risk reporting, 2013. Available from: http://www.bis.org/publ/bcbs239.pdf.
[55]	The Committee on Payment and Settlement Systems (CPSS) and International Organization of Securities Commissions (IOSCO), Report on OTC derivatives data reporting and aggregation requirements, 2012. Available from: http://www.bis.org/publ/cpss100.pdf.
[56]	The European Central Bank, Recent Advances in Modeling Systemic Risk Using Network Analysis, 2010. ECB, January.
[57]	The Financial Stability Board, A global legal entity identifier for financial markets, 2012. Available from: http://www.financialstabilityboard.org/publications/r_120608.pdf.
[58]	The Financial Stability Board (FSB), Key attributes of effective resolution regimes for financial institutions, 2011. Available from: http://www.financialstabilityboard.org/publications/r_111104cc.pdf.
[59]	The Financial Stability Board and the International Monetary Fund, The financial crisis and information gaps, 2009. Available from: http://www.financialstabilityboard.org/publications/r_091107e.pdf.
[60]	The International Monetary Fund, Global Financial Stability Report, 2009. Chapter II on Assessing the Systemic Implications of Financial Linkages, IMF, Washington DC.
[61]	The Warwick Commission on International Financial Reform (2010) In: Praise of Unleveled Playing Fields, Report, University of Warwick.
[62]	Varian HR (2014) Big data: New tricks for econometrics. J Econ Perspect 28: 3–27.

Reader Comments

Your name:*

Email:*
© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)