Several authors have analyzed specific aspects of the cross-sectional dimension of systemic risk, particularly linkages between banks through the interbank market. Since the early 2000s, the study of systemic risk using network approaches has attracted the attention of economists and scientists in general. These approaches typically involve the study of interbank financial data, using tools initially developed in the natural sciences to shed additional light on possible contagion effects through interbank linkages. The study of interbank data includes the attribution of topologic properties to a financial system, where an intricate web of relations exists through various kinds of exposures. In addition, the stability of the network itself is analyzed, including its capacity to absorb shocks (Montagna and Lux 2013).

In the case of developing economies, these challenges are often compounded by deficiencies in the quality and availability of data and constraints on technical capacity. Data coverage and quality is often problematic, with data on the real economy being particularly troublesome. GDP estimates are often available only with a significant delay and subject to frequent ex post revisions, while granular data on the financial position of households and enterprises are often lacking altogether. Longitudinal analysis of key variables is impossible in many cases due to frequent structural breaks in data series. As a consequence, it can be very difficult to draw a line between beneficial financial deepening and unsustainable credit booms and leverage cycles. Weaknesses in technical skills can add significantly to the challenges.

The presence of a robust, well-calibrated analytical framework is a critical precondition for effective macroprudential policymaking. Macroprudential policy is not costless. Measures aimed at mitigating systemic risk can carry opportunity costs in terms of financial deepening and diversification, with an adverse impact on economic growth—and, ultimately, on poverty reduction. This highlights the importance of a robust framework to identify systematic risks that helps policymakers detect and assess vulnerabilities. Such a framework can help policymakers select and calibrate macroprudential instruments in a manner that reflects the underlying sources of risk. After the framework is implemented, the effectiveness and efficiency of the instruments needs to be assessed, taking into consideration opportunity costs as well as possible leakages and arbitrage effects (ESRB 2014). In the absence of a robust analytical framework for monitoring systemic risk, there is an elevated risk that macroprudential policy will be ineffective (that is, it will respond too little or too late in the face of increasing risks to financial stability, or will be poorly tailored to address the true challenges). The opposite case, in which authorities adopt an excessively restrictive (macroprudential) policy stance, is associated with detrimental consequences on development.<sup>3</sup> Macroprudential oversight should explicitly address both the cross-sectional and the cyclical dimension of systemic risk. A conceptual distinction can be made between the cross-sectional

> 3 There is an extensive debate on whether countercyclical measures should be principally rule-based or discretionary. The optimal balance can be decided only in a particular national context, and may depend on the objectives of specific measures. However, irrespective of whether the authorities favor a rule-based or discretionary approach, effective macroprudential policy requires the authorities to monitor systemic risk and gauge the timing and appropriateness of policy actions (Ren 2011).

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