BCBS Principles for effective risk data aggregation and risk reporting
January 2013
http://www.bis.org/publ/bcbs239.htm
The financial crisis that began in 2007 revealed that many banks,
including global systemically important banks (G-SIBs), were unable to
aggregate risk exposures and identify concentrations fully, quickly and
accurately. This meant that banks' ability to take risk decisions in a
timely fashion was seriously impaired with wide-ranging consequences for
the banks themselves and for the stability of the financial system as a
whole.
The Basel Committee's Principles for effective risk data aggregation
will strengthen banks' risk data aggregation capabilities and internal
risk reporting practices. Implementation of the principles will
strengthen risk management at banks - in particular, G-SIBs - thereby
enhancing their ability to cope with stress and crisis situations.
An earlier version of the principles published today was issued for consultation in June 2012. The Committee wishes to thank those who provided feedback and comments as these were instrumental in revising and finalising the principles.
Objectives (excerpted):
The adoption of these Principles will enable fundamental improvements to the management of banks. The Principles are expected to support a bank’s efforts to:
• Enhance the infrastructure for reporting key information, particularly that used by the board and senior management to identify, monitor and manage risks;
• Improve the decision-making process throughout the banking organisation;
• Enhance the management of information across legal entities, while facilitating a comprehensive assessment of risk exposures at the global consolidated level;
• Reduce the probability and severity of losses resulting from risk management weaknesses;
• Improve the speed at which information is available and hence decisions can be made; and
• Improve the organisation’s quality of strategic planning and the ability to manage the risk of new products and services.
Favorable growth prospects and higher asset
returns in emerging market economies have been led to a sharp increase
in flows of foreign finance in recent years. Massive inflows to the
domestic economy may fuel activity in financial markets and — if not
properly managed — booms in credit and asset prices may arise (Reinhart
and Reinhart, 2009; Mendoza and Terrones, 2008, 2012). In turn, the
expansion of credit and overvalued asset prices have been good
predictors not only of the current financial crises but also past ones
(Schularick and Taylor, 2012; Gourinchas and Obstfeld, 2012).
In a recent paper, Megumi Kubota and I synthesized both strands of
the empirical literature and examine whether gross private inflows can
predict the incidence of credit booms — and, especially, those financial
booms that end up in a systemic banking crises.1
More specifically, our paper finds that surges gross private capital
inflows can help explain the incidence of subsequent credit booms — and,
especially those financial booms that are followed by systemic banking
crises. When looking at the predictive power of capital flows, we argue
that not all types of flows behave alike. We find that gross private
other investment (OI) inflows robustly predict the incidence of credit
booms — while portfolio investment (PI) has no systematic link and FDI
surges will at best mitigate the probability of credit booms. Consequently, gross private OI inflows are a good predictor of credit
booms.
Our paper evaluates the linkages between surges in gross private
capital inflows and the incidence of booms in credit markets. In
contrast to previous research papers in this literature: (i) we use data
on gross inflows rather than net inflows; and, (ii) we use quarterly
data for 71 countries from 1975q1 and 2010q4 instead of annual
frequency. In this context, we argue that the dynamic behavior of
capital flows and credit markets along the business cycle is better
captured using quarterly data.2
As a result, we can evaluate more precisely the impact on credit booms
of (the overall amount and the different types of) financing flows
coming from abroad. On the other hand, we are more interested the impact
on credit markets of investment inflows coming from foreign investors.
Using information on net inflows — especially since the mid-1990s for
emerging markets — would not allow us to appropriately differentiate the
behavior of foreign investors from that of domestic ones and it may
provide misleading inference on the amount of capital supplied from
abroad (Forbes and Warnock, 2012).3
Credit booms are identified using two different methodologies: (a)
Mendoza and Terrones (2008), and (b) Gourinchas, Valdés and Landarretche
(2001) — also applied in Barajas, Dell’Ariccia, and Levchenko (2009).
Moreover, we look deeper into credit boom episodes and differentiate bad
booms from those that booms that may come along with a soft landing of
the economy. In general, the literature finds that credit booms are not
always followed by a systemic banking crisis — see Tornell and
Westermann (2002) and Barajas et al. (2009). For instance, Calderón and
Servén (2011) find that only 4.6 percent of lending booms may end up in a
full-blown banking crisis for advanced countries whereas its
probability is 8.3 and 4.6 percent for Latin America and the Caribbean
(LAC) and non-LAC emerging markets. Those credit booms that end up in an
episodes of systemic banking crisis are denoted as “bad” credit booms —
see Barajas et al. (2009).
Our panel Probit regression shows that gross private capital inflows
are a good predictor of the incidence of credit booms. This result is
robust with respect to any sample of countries, any criteria of credit
booms and any set of control variables. Next, the probability of credit
booms is higher when the surges in capital flows are driven by gross OI
inflows and, to a lesser extent, by increases in gross portfolio
investment (FPI) inflows. Surges of gross foreign direct investment
(FDI) inflows would, at best, reduce the likelihood of credit booms. The
main conduit is gross OI bank inflows10 when we unbundle the effect of
gross private OI inflows on credit booms. Third, we find that capital
flows do explain the incidence of bad credit booms and that the overall
impact is significantly positive and greater than the impact on overall
credit booms.
Finally, the likelihood of bad credit booms is greater when surges in
capital inflows are driven by increases in OI inflows. As a result, the
overall positive impact of gross OI inflows significantly predicts an
increase in credit booms although the evidence on the impact of gross
FDI and FPI inflows is somewhat mixed. So far, the literature has shown
that increasing leverage in the financial system and overvalued
currencies are the best predictors of financial crisis (Schularick and
Taylor, 2012; Gourinchas and Obstfeld, 2012). Moreover, our findings
suggest that surges in capital flows (especially, rising cross-border
banking flows) are also a good indicator of future financial turmoil.
References
Barajas, A., G. Dell’Ariccia, and A. Levchenko, 2009. “Credit Booms: the Good, the Bad, and the Ugly.” Washington, DC: IMF, manuscript
Calderón, C., and M. Kubota, 2012. “Gross Inflows Gone Wild: Gross
Capital inflows, Credit Booms and Crises.” The World Bank Policy
Research Working Paper 6270, December.
Calderón, C., and M. Kubota, 2012. “Sudden stops: Are global and local investors alike?” Journal of International Economics 89(1), 122-142
Calderón, C., and L. Servén, 2011. “Macro-Prudential Policies over the Cycle in Latin America.” Washington, DC: The World Bank, manuscript
Forbes, K.J., and F.E. Warnock, 2012. “Capital Flow Waves: Surges, Stops, Flight, and Retrenchment.” Journal of International Economics 88(2), 235-251
Gourinchas, P.O., and M. Obstfeld, 2012. “Stories of the Twentieth Century for the Twenty-First.” American Economic Journal: Macroeconomics 4(1), 226-265
Gourinchas, P.O., R. Valdes, and O. Landerretche, 2001. “Lending Booms: Latin America and the World.” Economia, Spring Issue, 47-99.
Mendoza, E.G., and M.E. Terrones, 2008. “An anatomy of credit booms:
Evidence from macro aggregates and micro data.” NBER Working Paper
14049, May
Mendoza, E.G. and M.E. Terrones, 2012. “An Anatomy of Credit Booms and their Demise,” NBER Working Paper 18379, September.
Reinhart, C.M., and V. Reinhart, 2009. “Capital Flow Bonanzas: An
Encompassing View of the Past and Present.” In: Frankel, J.A., and C.
Pissarides, Eds., NBER International Seminar on Macroeconomics 2008.
Chicago, IL: University of Chicago Press for NBER, pp. 9-62
Rothenberg, A., Warnock, F., 2011. “Sudden flight and true sudden stops.” Review of International Economics 19(3), 509-524.
Schularick, M., and A.M. Taylor, 2012. “Credit Booms Gone Bust:
Monetary Policy, Leverage Cycles, and Financial Crises, 1870–2008.” American Economic Review 102(2), 1029–1061
______________________
1 Read Working Paper.
2 Rothenberg and Warnock (2011), Forbes
and Warnock (2012) and Calderón and Kubota (2012) already provide a
more accurate analysis of extreme movement in (net and gross) capital
flows using quarterly data.
3 The “two-way capital flows” phenomena cannot be identified using net inflows.