Visual Macroprudential Surveillance of Banks

• Published date: 01 October 2016
• DOI: http://doi.org/10.1002/isaf.1391
• Date: 01 October 2016
• Author: Peter Sarlin

VISUAL MACROPRUDENTIAL SURVEILLANCE OF BANKS

PETER SARLIN

a,b

*

a

Hanken School of Economics, Helsinki, Finland

b

RiskLab Finland at Arcada, Helsinki, Finland

SUMMARY

We create a tool for visual surveillance of the European banking system from a macroprudential perspective. The

tool performs visual dynamic clustering with the self-organizing time map (SOTM) to visualize evolving multivar-

iate data from two viewpoints: (i) multivariate cluster structures, and (ii) univariate drivers of changes in structures.

In assessing the European banking system, the main tasks the SOTM can be used for are (i) identifying structural

changes and breaking points in a large number of risk indicators, and their specific location in the cross-section,

and (ii) identifying the build-up of, or generally changes in, individual risk indicators in the banking system as a

whole. While the former view provides indications of changes in the banking system, the latter describes the

sources of these changes. Copyright © 2016 John Wiley & Sons, Ltd.

1. INTRODUCTION

In the wake of the Lehman Brothers crash of 2008, the ongoing global financial crisis has highlighted

the fragilityof the financial system as well as theimportance of a system-wide perspective to surveillance.

The crisis, while originating from subprime mortgages in the USA, has spread in the globalfinancial sys-

tem, not the least throughfinancial intermediaries. Theinterconnectedness among financialinstitutions has

led to rapid transmissionof shocks in the financial system.A further by-product has been the impactof the

financial system on the solvency of nations, particularly the bank-sovereign nexus in Europe. While data

from the European Commission showedthat government support to banks amountedto 13% of EU-level

GDP, sovereigns themselves have beenon the brink of collapse. This highlights the importanceof not only

surveillance of risks in individual, even systemically important, financial intermediaries, but also a focus

on gaining an adequate understanding of risks in the financial system as a whole.

For macroprudential oversight, policymakers need surveillance models or tools to measure and ana-

lyse system-wide threats to financial stability as to support decision-making. Following ECB (2010),

surveillance tools are divided into three categories: (i) early-warning models, (ii) macro stress-testing

models, and (iii) contagion and spillover models. First, by focusing on the presence of concentrated

vulnerabilities and imbalances, such as credit in the economy, early-warning models are used to derive

probabilities of the occurrence of systemic financial crises in the future (e.g. Alessi & Detken, 2011;

Sarlin & Peltonen, 2013). Second, macro stress-testing models provide means to assess the resilience

of the financial system to aggregate shocks, such as economic downturns (e.g. Castrén et al., 2009;

Hirtle et al., 2009). Third, contagion and spillover models can be employed to assess how resilient

the financial system is to cross-sectional transmission of financial instability (e.g. Chan-Lau et al.,

2009). Yet, independent of the underlying techniques, an essential part of tools is to provide interpret-

able models that ultimately support understanding and the human activity of policymaking.

* Correspondence to: Peter Sarlin, Hanken School of Economics, Arkadiankatu 22, 00101 Helsinki, Finland. E-mail:

peter@risklab.fi

Copyright © 2016 John Wiley & Sons, Ltd.

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE AND MANAGEMENT

Intell. Sys. Acc. Fin. Mgmt. 23, 257–264 (2016)

Published online 4 May 2016 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/isaf.1391