# **Annex II - Identification of Financial Instability Risks**

Many different approaches have been developed to identify and better understand potential vulnerabilities and systemic risks. Part of the literature focus on early indicator models, while other studies search for the main stylized economic patterns associated with past pre-crisis periods. Both may be useful as tools to identify fragilities and motivate preemptive action before crises.

Qualitative and quantitative intelligence can be very useful to better advice and improve policy making, but to determine timing and triggers is more difficult. In general, quantitative indicators are based on aggregate or sectors/institutions data. One set of measures commonly used are the estimates of deviations of variables from their long term trends, e.g. credit to GDP, housing prices and equity prices. Intuitively, as an economy moves away from its trend, the probability of building up imbalances increases, and consequently the chances of a crisis also rise. However, there are doubts about the capacity of these models to produce valid out-of-sample forecasts (BCBS, 2012) – among other reasons because structural changes may be the explanatory factor of the apparently deviation from trend. Another group of indicators uses micro data, e.g. balance sheet data, banks’ capital and liquidity positions, and distance to default ratios. These Indicators are constructed from medians, means, or correlations.

The IMF’s early warning exercise (EWE) is a systematic effort to combine quantitative analysis of vulnerabilities and transmission channels with feedback from financial professionals, academics and policymakers (IMF, 2010). The models and indicators are grouped in three main blocks: Sectoral and Market Vulnerabilities; Country Risk; and Systemic Implications. The first block assesses external-sector risks (e.g. cross border capital flows and external imbalances and exchange rate misalignments), fiscal risks (e.g. rollover and financing risks, market perception of sovereign risk, and sensitivity of the public sector to shocks), corporate sector risks (e.g. leverage and liquidity, and profitability), asset price and market valuation (e.g. real estate and equity market bubbles) and financial market risk attitudes (e.g. asset and market volatility and the global financial stability map). The second block intends to empirically quantify macro tail risks and worst outcomes, attributing probabilities. Finally, systemic implications from models of cross-border bank contagion and distress dependence framework are drawn from financial market data. Large Complex Financial Institutions and different global scenarios simulations are also considered in this block.

Another initiative is the financial stress index (FSI), which aggregates five indicators: the spread of 3- month interbank rate over government bills for the same maturity, negative quarterly equity returns multiplied by minus one, realized volatility on the equity index, the same indicator for nominal exchange rate, and the volatility of the yield in the 3-month government bill. Duca and Peltonen (2011) did a “quartile” standardization to create an indicator between zero and three, in which higher values mean deeper stress. Then, optimal thresholds for policy interventions are calculated for standalone indicators and probabilities of systemic events. Their results showed that asset price misalignments and credit booms are useful leading indicators for systemic events and that models outperform standalone indicators, as they consider domestic and global macrofinancial vulnerabilities.

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