Abstract
This chapter exploits the Self-Organizing Financial Stability Map (SOFSM) for tasks in macroprudential oversight. The SOFSM was created in Chap. 7, whereas the Self-Organizing Map (SOM) extensions used for exploiting it were introduced in Chap. 6. The tasks performed with the SOFSM are two, risk identification and assessment, of which the former is supported by early-warning models and the latter by macro stress-testing and contagion or spillover models. The three models target the three respective forms of systemic risk: widespread imbalances, aggregate shocks and contagion and spillover risk. Drawing upon Sarlin and Peltonen (2013) and Sarlin (2013), the SOFSM is exploited by the means of the eight approaches.
Lastly, novel methods such as self-organising financial stability maps provide an alternative means of gauging systemic stress through visual means—thereby providing a useful complement to numerical signalling methodologies.
– Vítor Constâncio, Vice-President of the ECB, Frankfurt am Main, 18 November 2010
This chapter is partly based upon previous research. Please see the following works for further information: Sarlin and Peltonen (2013), Sarlin (2013, 2014a)
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Beyond the static representations herein, the implementation developed by infolytika provides an interactive, web-based interface to the SOFSM (http://risklab.fi/demo/macropru/fsm/). For a description, see Sarlin (2014a).
References
Alessi, L., & Detken, C. (2011). Quasi real time early warning indicators for costly asset price boom/bust cycles: a role for global liquidity. European Journal of Political Economy, 27(3), 520–533.
Borio, C., & Lowe, P., (2002). Asset prices, financial and monetary stability: Exploring the nexus. BIS Working Papers No. 114.
Borio, C., & Lowe, P., (2004). Securing sustainable price stability: Should credit come back from the wilderness? BIS Working Papers No. 157.
Card, S., Mackinlay, J., & Schneidermann, B. (1999). Readings in information visualization, using vision to think. San Diego, CA: . Academic Press Inc.
Dornbusch, R., Park, Y., & Claessens, S. (2000). Contagion: how it spreads and how it can be stopped. World Bank Research Observer, 15, 177–197.
Fioramanti, M. (2008). Predicting sovereign debt crises using artificial neural networks: a comparative approach. Journal of Financial Stability, 4(2), 149–164.
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.
Keim, D., Mansmann, F., Schneidewind, J., & Ziegler, H. (2006). Challenges in visual data analysis. In Proceedings of the IEEE International Conference on Information Visualization (iV 13) (pp. 9–16). London, UK: IEEE Computer Society.
Kohonen, T. (2001). Self-organizing maps (3rd ed.). Berlin: Springer.
Peltonen, T., (2006). Are emerging market currency crises predictable? A test, ECB Working Paper No. 571.
Pericoli, M., & Sbracia, M. (2003). A primer on financial contagion. Journal of Economic Surveys, 17, 571–608.
Sarlin, P. (2013). Exploiting the self-organizing financial stability map. Engineering Applications of Artificial Intelligence, 26(5–6), 1532–1539.
Sarlin, P., (2014a). On biologically inspired predictions of the global financial crisis. Neural Computing & Applications, 24(3–4), 663–673.
Sarlin, P., & Peltonen, T. (2013). Mapping the state of financial stability. Journal of International Financial Markets, Institutions & Money, 26, 46–76.
Saunders, R., & Gero, J. (2001). Designing for interest and novelty: Motivating design agents. In B. de Vries, J. van Leeuwen, & H. Achten (Eds.), CAADFutures (pp. 725–738). Dordrecht: Kluwer.
Thomas, J., & Cook, K., 2005. Illuminating the path: Research and development agenda for visual analytics. New York: IEEE Press.
Vesanto, J., Himberg, J., Siponen, M., & Simula, O. (1998). Enhancing som based data visualization. In Proceedings of the International Conference on Soft Computing and Information/Intelligent Systems (IIZUKA 98) (pp. 64–67). Iizuka, Japan.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sarlin, P. (2014). Exploiting the SOFSM. In: Mapping Financial Stability. Computational Risk Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54956-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-54956-4_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54955-7
Online ISBN: 978-3-642-54956-4
eBook Packages: Business and EconomicsEconomics and Finance (R0)