Mapping the State of Financial Stability

1. Mapping the State of Financial Stability 14th Annual DNB Research Conference 3rd November 2011 DNB, Amsterdam Peter Sarlin (Åbo Akademi / TUCS) & Tuomas Peltonen (European Central Bank)

2. An example of a SOM output at certain time

3. 1. Introduction - What do we do in the paper? • Create the Self-Organizing Financial Stability Map (SOFSM) • A model that can visualize multidimensional macro-financial vulnerabilities and the state of financial stability across countries and over time • A model thathasgood out-of-sample predictivecapabilitiesoffuturesystemicevents/ financialcrises

4. 2. Self-Organizing Financial Stability Map (SOFSM) • Building blocks for creating the SOFSM: • Self-Organizing Maps • Identifying systemic events • Vulnerability indicators • Model training • Model evaluation • Mapping the State of Financial Stability

5. 2.1 Self-Organizing Maps (SOMs) – what are they? • SOM is an Exploratory Data Analysis (EDA) technique by Kohonen (1981) ➨ Viscovery SOMine • It is a clustering and projection technique: • Spatially constrained form of k-means clustering • Preserves the neighbourhood relations of the data (instead of trying to preserve the distances between data) • Projects data onto a grid of nodes (rather than projecting data into a continuous space) • Enables visualization of high-D data ➨ 2D grid of nodes without losing the topological relationships of data and sight of individual indicators. • Enables a flexible distribution and interactions. • Kohonen’s group has continuously reviewed the SOM literature • The SOM has been used in approx. 10 000 works • Applied to currency and debt crises: Arciniegas and Arciniegas Rueda (2009), Resta (2009), Sarlin (2011) and Sarlin and Marghescu (2011)

6. 2.1 Self-Organizing Maps (SOMs) – training algorithm xj mb Radius of the neighborhood σ • Compare all data points xj with all nodes mi to find for each data point the nearest node mb (i.e., best-matching unit, BMU) • Update each node mi to averages of the attracted data, including data located in a specified neighbourhood σ • Repeat steps 1 and 2 a specified number of times. • The SOM parameters are radius of the neighbourhood σ, number of nodes M, map format (ratio of X and Y dimensions), and number of training iterations t.

7. 2.1 Self-Organizing Maps (SOMs) –interpreting the output • This is a 2D map that represents multi-D data with a 2-level clustering • For each indicator, we create a „feature plane“ where the color coding represents the distributionof its values on the 2D map. Indicator 1 Indicator 2 Indicator 3 Indicator 4

8. 2.2 Identifying systemic events and creating financial stability cycle • Use the data set from Lo Duca and Peltonen (2011): 28 countries (18 EMEs & 10 AEs), Quarterly data 1990Q1-2010Q3 • Identification of systemic events: • The Financial Stress Index (FSI) includes 5 components for each country, measuring volatilities and sharp declines in key market segments (stock, foreign exchange and money markets) • A systemic event occurs when the FSI is above the 90th percentile of the country-specific distribution (on average, negative real consequences) • Using the FSI, we identify four classes to describe the financial stability cycle: • Pre-crisis periods (18 months before the systemic event) • Crisis periods (systemic events defined by a financial stress index) • Post-crisis periods (18 months after the systemic event) • Tranquil periods (all other periods)

9. 2.3 Vulnerability indicators • 14 indicators of country-level macro-financial vulnerabilities: • Domestic = inflation, GDP growth, CA deficit, budget balance, credit growth, leverage, equity price growth, equity valuation • Global= inflation, GDP growth, credit growth, leverage, equity price growth, equity valuation • Test several transformations of the indicators (over 200 transformations of the indicators tested). • Select best-performing (as a leadingindicator) transformationsofthe variables

10. 2.4 Model training • „Static“ model, i.e. model is not re-estimated recursively: • Training set (estimation sample): 1990Q4 - 2005Q1 • Test set (out-of-sample): 2005Q2 - 2009Q2 • In the benchmark, we use 18 months as a forecast horizon • Account for policymakers’ preferences when evaluating the performance as in Alessi and Detken (2011) (benchmark μ=0.5) • Data as aninput to the SOFSM • Class variables + vulnerabilities for training • Only vulnerabilities for mapping and evaluating • Crisis probabilities as an output of the SOFSM • Map data onto SOFSM and retrieve a crisis probability

11. 2.5 Model evaluation • Defining early warning nodes • When calibrating the policymakers’ preferences, we vary the thresholds. This changes the number of “early warning nodes”. µ =0.4 µ =0.5 µ =0.6

12. 2.5 Model evaluation • Training the SOM: • While a higher number of nodes M improves in-sample performance, it decreases generalization, i.e. out-of-sample performance. • We increase M and findthe first model with Usefulness ≥ 0.25 (logit model). • In terms of “Usefulness”, when µ=0.5, the models are by definition very similar on in-sample data, while the SOM performs better on out-of-sample data • Robustness is tested with respect to three aspects • SOM parameters: radius of neighborhood and number of nodes • Policymakers’ preferences • Forecast horizon • Reminder: • Recall positives = TP/(TP+FN), Recall negatives = TN/(TN+FP), Precision positives = TP/(TP+FP), Precisionnegatives = TN/(TN+FN), FP rate = FP/(FP+TN), TP rate = TP/(FN+ TP), Accuracy=(TP+TN)/(FN+FP+TN+ TP).

13. 3. Mapping the State of Financial Stability – The two dimensional SOFSM • Thisisthe 2D SOFSM thatrepresents multi-D data. • The stagesofthefinancialstabilitycyclearederivedusingonlytheclass variables (pre-crisis, crisis, post-crisisandtranquilperiods)

14. 3. Mapping the State of Financial Stability – Constructing the four clusters according to the financial stability cycle • Clustering is performed using hierarchical clustering based on class variables. The map is partitioned into four clusters according to the financial stability cycle: a pre-crisis, crisis, post-crisis and tranquil cluster.

15. 3. Mapping the State of Financial Stability – The distribution of 14 indicators across the 4 clusters • Domestic: early signs of crisis - equity growth and valuation, budget deficit, followed by real GDP and credit growth, leverage, budget surplus, and CA deficit. • Global:early signs of crisis - equity growth and level, followed by real GDPgrowth, while global credit growth and leverage are more concurrent with crises.

16. Pre crisis Tranquil Post crisis Crisis 3. Mapping the State of Financial Stability – Temporal dimension Evolution of macro-financial conditions (all 14 indicators) for the United States and the Euro area (2002-10, first quarter) US 2004–05 Euro area aggregate, did not reflect the crisis in GR, IE, PT. Financial Stress Index also decreased for the euro area aggregate US 2006 US 2002 Euro 2004–05 US 2007 2010 Euro 2006 Euro 2002 US 2008–09 Euro 2010 Euro 2007 Euro 2008 US 2003 Euro 2003 Euro 2009

17. 3. Mapping the State of Financial Stability – Cross section Visualizing current macro-financial vulnerabilities in key advanced and emerging economies (2010Q3) Contagion through similarities in macro-financial vulnerabilities SOFSM enables identifying events surpassing historical experience

18. 3. Mapping the State of Financial Stability – Regional evolution Evolution of the macro-financial conditions in Emerging Market Economies and Advanced Economies (2002-10 , first quarter) Pre-crisis Tranquil Post-crisis Crisis

19. 4. Conclusions • Self-Organizing Financial Stability Map is a useful model for financial stability surveillance: • mapping the state of financial stability and visualizing multidimensional macro-financial vulnerabilities • has good out-of-sample predictive capabilities of future systemic events / financial crises (EWS) • the SOFSM is flexible with respect to, e.g., events of interest, vulnerability indicators, forecast horizons, policymaker‘s preferences

20. Thank you for your attention!