Introducing a composite indicator of cyclical systemic risk in Croatia: possibilities and limitations

Škrinjarić, Tihana

doi:10.3326/pse.47.1.1.

1827
Views

305
Downloads

Introducing a composite indicator of cyclical systemic risk in Croatia: possibilities and limitations*

Tihana Škrinjarić

Download citation

https://doi.org/10.3326/pse.47.1.1

FULL ARTICLE

FIGURES & DATA

REFERENCES

CROSSMARK POLICY

METRICS

LICENCING

PDF

Table 3
Summary of the three main approaches for composite indicator construction

Indicator	Transformation	Method of data aggregation	Data selection criteria	Advantages	Shortfalls
FCI	Order statistics	Nonlinear function (like portfolio variance)	Financial cycle theory, previous literature, without empirical evaluation of the variable characteristics before the crisis.	Takes correlation into consideration, graphical representation, no problems with statistical filters regarding data transformation, robustness due to scaling variables.	Lack of objective data selection criteria, variable selection affects the dynamics of the indicator, harder to communicate, hart to evaluate the results
Cyclogram	Max min or based on percentiles of distribution	Average, weighted average	Previous experience with variable dynamics tracking.	Graphical representation, no problems with statistical filters regarding data transformation, easy aggregation and interpretation
d-SRI	Normalization, standardization or max min	Average, weighted average	Early warning models of signaling crisis.	Data selection criteria, simple aggregation and interpretation, robust⁹]	Correlations not observed, biased results for one country analysis
PCA	Normalization, standardization	Weighted average based on loadings on the first principal component	Any of the previous three main approaches	Simple aggregation	Assumptions of PCA analysis, changing correlations, bad predictive power of the first principal component.
Geometric average	Normalization, standardization	Geometric average formula			Hard to interpret results in economic way, correlations not observed, depends on the main method of aggregation, negative values in data.
RMS	Normalization, standardization	Root mean square formula			Hard to interpret results in economic way, correlations not observed, depends on the main method of aggregation, negative values in data, lack of risk accumulation in one category is substituted with high risk in other.
OI	Binary variable depending on EWM results	Average or weighted average		If based on d-SRI approach, advantages as there	Hard to interpret results in economic way, correlations not observed, depends on the main method of aggregation, negative values in data.

⁹ However, working with methods that do not rely on EWM has the advantage of being informative and feasible when looking at a country that did not experience notable crises in its own past, i.e., for which a LHS crisis indicator would not be quite informative/moving.

Source: Author's preparation based on previous discussion.

Table 4
Summary of two FCI variants

Abbreviation	Transformation	Variable	Risk category	FCI variant
ΔICSN	Yearly growth rate	House price index	Potential overvaluation of property prices	(1)
A. 2ΔICSN	Annualized two-year growth rate	House price index	Potential overvaluation of property prices	(2)
ΔKK	Yearly growth rate	Bank loans to households	Credit dynamics	(1)
A. 2ΔKK	Annualized two-year growth rate	Bank loans to households		(2)
ΔKNFP	Yearly growth rate	Bank loans to nonfinancial corporations		(1)
A. 2ΔKNFP	Annualized two-year growth rate	Bank loans to nonfinancial corporations		(2)
Δ(LR)	Yearly change	Leverage ratio (multiplied with -1)	Strength of bank balance sheets	(1)
A. 2Δ(LR)	Annualized two-year change	Leverage ratio (multiplied with -1)		(2)
Δ(LTD)	Yearly change	Credit to deposit ratio		(1)
A. 2Δ(LTD)	Annualized two-year change	Credit to deposit ratio		(2)
Δ(K/Y)	Yearly growth rate	Debt (households) to disposable income ratio	Private sector debt burden	(1)
A. 2Δ(K/Y)	Annualized two-year growth rate	Debt (households) to disposable income ratio		(2)
Δ(NFP/BOV)	Yearly growth rate	Debt (nonfinancial corporations) to gross operating surplus ratio		(1)
A. 2 Δ(NFP/BOV)	Annualized two-year growth rate			(2)
ΔCROBEX	Yearly growth rate	CROBEX, stock market index	Mispricing of risk	(1)
A. 2 ΔCROBEX	Annualized two-year growth rate	CROBEX, stock market index		(2)
Δ margin K	Yearly change	Household credits interest rate margin (difference between average new credits interest rate to households and 3 month EURIBOR interest rate) (multiplied with -1)		(1)
A. 2 Δ margin K	Annualized two-year change			(2)
Δ margin NFP	Yearly change	Nonfinancial corporations credits interest rate margin (difference between average new credits interest rate to nonfinancial corporations and 3 month EURIBOR interest rate) (multiplied with -1)		(1)
A. 2 Δ margin NFP	Annualized two-year change			(2)
ΔRN	Yearly change	Current account to GDP ratio (multiplied with -1)	External imbalances	(1)
A. 2 ΔRN	Annualized two-year change	Current account to GDP ratio (multiplied with -1)	External imbalances	(2)

Source: CNB, author's calculation.

Table 6
Best indicators chosen for d-SRI calculation

Risk categories	Indicator description
Credit dynamics measures	HP gap for the broad definition of credit to households, smoothing parameter of 125,000
	HP gap for the broad definition of credit to non-financial corporations, smoothing parameter of 125,000
	HP gap for the ratio of narrow definition of credit and the sum of GDP of the current quarter and the preceding three quarters, smoothing parameter of 125,000
Measures of credit institution financing risk	Annualized two-year change in the negative ratio between credit institutions’ equity and assets
Measures of credit institution financing risk	Annualized two-year change in the negative ratio between private sector deposits and credit
Measures of potential real estate price overvaluation	Annualized two-year growth rate in the residential real-estate price index
	Annualized two-year growth rate in the residential real-estate price-to-disposable income ratio
	Annualized two-year growth rate in the volume index of construction works
Measures of private sector debt burden	HP gap for the ratio between corporate debt and gross operating surplus, smoothing parameter of 125,000
	HP gap for the ratio between household debt and disposable income, smoothing parameter of 125,000
	HP gap of debt service measures – households, smoothing parameter of 125,000
	HP gap of debt service measures – corporations, smoothing parameter of 125,000
Measures of external imbalances	Annualized two-year change in the negative share of net exports of goods and services in GDP
Measures of external imbalances	Annualized two-year change in the negative share of current account balance in GDP
Measures of potential mispricing of risk	Annualized two-year growth rate in CROBEX
	Annualized two-year change in the negative interest margin on new loans to households relative to the 3-month EURIBOR
	Annualized two-year change in the negative interest margin on new corporate loans relative to the 3-month EURIBOR

Source: CNB, author's calculation.

Table 8
Weights assignment based on errors type 1 and 2

Indicator	error T1	error T2	Sum	Weight (%)
HP gap, household credit	0,08	0,08	0,16	8,84
HP gap, nonfinancial corporations credit	0,08	0,21	0,29	4,47
HP gap, narrow definition of credit	0,00	0,41	0,41	2,84
2y change, equity to assets ratio	0,50	0,00	0,50	2,15
2y change, deposit to credit ratio	0,00	0,09	0,09	15,82
2y growth rate, house price index	0,00	0,13	0,13	11,09
2y growth rate, house price to income ratio	0,00	0,09	0,09	17,14
2y growth rate, volume index of construction works	0,00	0,00	0,00	8,00
HP gap, ratio debt to gross operating surplus	0,00	0,22	0,22	6,10
HP gap, ratio debt to disposable income	0,00	0,49	0,49	2,24
HP gap, debt service ratio, households	0,00	0,49	0,49	2,24
HP gap, debt service ratio, nonfinancial corporations	0,00	0,33	0,33	3,73
2y growth rate, net exports to GDP ratio	0,00	0,61	0,61	1,57
2y growth rate, current account to GDP ratio	0,08	0,45	0,53	1,95
2y growth rate, CROBEX	0,00	0,00	0,00	8,00
2y change, interest margin, households	0,33	0,16	0,49	2,22
2y change, interest margin, nonfinancial corporations	0,25	0,19	0,44	2,60

Note: Abbreviations refer to variables from table 6, the following the sequence from first to last one as in the mentioned table.

Source: CNB, author's calculation.

Adalid, R., and Detken, C., 2007. Liquidity shocks and asset price boom/bust cycles. ECB Working Paper, No. 732 [CrossRef]

Aikman, D. [et al.], 2015. Mapping Heat in the U.S. Financial System. Finance and Economics Discussion Series, No. 2015-059.

Aldasoro, I., Borio, C. and Drehmann, M., 2018. Early warning indicators of banking crises: expanding the family. BIS Quarterly Review, March 2018.

Arbatli-Saxegaard, E. C. and Melle Johansen, R., 2017. A Heatmap for Monitoring Systemic Risk in Norway. Staff Memo, No. 10.

Arbatli-Saxegaard, E. C. and Muneer, M. A., 2020. The countercyclical capital buffer: A cross-country overview of policy frameworks. Staff memo, No. 6.

Babecký, J. [et al.], 2014. Banking, Debt, and Currency Crises in Developed Countries: Stylized Facts and Early Warning Indicators. Journal of Financial Stability, 15, pp. 1 –17 [CrossRef]

Bakker, B., 2015. The role of wealth in the financial crisis. World Economic Forum, February 9, 2015.

Basten, C., 2020. Higher Bank Capital Requirements and Mortgage Pricing: Evidence from the Counter-Cyclical Capital Buffer. The Review of Finance, 242, pp. 453–495 [CrossRef]

Behn, M. [et al.], 2013. Setting Countercyclical Capital Buffers Based on Early Warning Models: Would It Work? ECB Working Paper, No. 1604.

Bernanke, B. S. and Gerlter, M., 1995. Inside the black box: the credit channel of monetary policy transmission. Journal of economic perspectives, 94, pp. 27 – 48 [CrossRef]

Bernanke, B. S., 1999. The Financial Accelerator in a Quantitative Business Cycle Framework. In: Handbook of Macroeconomics, 1(1), pp. 1341 – 1393 [CrossRef]

Berti, K., Engelen, C. and Vašiček, B., 2017. A macroeconomic perspective on non-performing loans NPLs. Quarterly Report on the Euro Area, 161, pp. 7 – 21.

BIS, 2011. Basel III: A global regulatory framework for more resilient banks and banking systems. Basel: Bank for International Settlements.

BIS, 2017. Range of Practices in Implementing the Countercyclical Capital Buffer Policy. Basel: Bank for International Settlements.

Bollen, B., 2014. What should the value of lambda be in the exponentially weighted moving average volatility model? UNE Business School Occasional Papers, No. 2014-01.

Bordalo, P., Gennaioli, N., and Shleifer, S., 2018. Diagnostic Expectations and Credit Cycles. The Journal of Finance, 73(1), pp. 199 – 227 [CrossRef]

Borio, C. and Drehmann, M., 2009. Assessing the risk of banking crises – revisited. BIS Quarterly Review, March 2009.

Borio, C. and Lowe, P., 2002. Asset prices, financial and monetary stability: exploring the nexus. BIS Working Papers, No 114 [CrossRef]

Borio, C. and Zhu, H., 2011. Capital regulation, risk-taking and monetary policy: a missing link in the transmission mechanism? Journal of Financial Stability, 84, pp. 236 – 251 [CrossRef]

Borio, C., 2012. The Financial Cycle and the Marco-economy: What have we learnt. BIS Working Paper Series, No. 395.

Brzoza-Brzezina, M., Kolasa, M. and Makarski, K., 2015. Macroprudential policy and imbalances in the euro area. Journal of International Money and Finance, 51, pp. 137 – 154 [CrossRef]

Candelon, B., Dumitrescu, E-I. and Hurlin, C., 2012. How to Evaluate an Early-Warning System: Toward a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods. IMF Economic Review, 60, pp. 75-113 [CrossRef]

Canova. F. 1998. Detrending and business cycle facts. Journal of Monetary Economics, 41(3), pp. 475-512 [CrossRef]

Castro, C., Estrada, Á. and Martínez, J., 2016. The countercyclical capital buffer in Spain: An analysis of key guiding indicators. Working Paper Documentos de Trabajo, No. 1601 [CrossRef]

Chen, D. and Friedrich, C., 2021. The Countercyclical Capital Buffer and International Bank Lending: Evidence from Canada. Staff Working Paper/Document de travail du personnel, No. 2021-61.

Chen, S. and Svirydzenka, K., 2021. Financial Cycles – Early Warning Indicators of Banking Crises? IMF Working paper, WP/21/116 [CrossRef]

Cogley, T. and Nason, J. M., (1995). Output Dynamics in Real-Business-Cycle Models. The American Economic Review, 85(3), pp. 392-511.

Comelli, F. and Ogawa, S., 2021. What Can We Learn from Financial Stability Reports? IMF working paper, No. 200 [CrossRef]

Constâncio, V. [et al.], 2019. Macroprudential policy at the ECB: Institutional framework, strategy, analytical tools and policies. ECB working paper, No 227 [CrossRef]

Dell'Ariccia, G. [et al.], 2012. Policies for Macrofinancial Stability: How to Deal with Credit Booms. IMF Staff Discussion Note, SDN/12/06.

Detken, C. [et al.], 2014. Operationalising the countercyclical capital buffer: indicator selection, threshold identification and calibration options. ESRB Occasional Paper, No. 2014/5 [CrossRef]

Dimova, D., Kongsamut, P. and Vandenbussche, J., 2016. Macroprudential Policies in Southeastern Europe. Working Paper, No. 16/29 [CrossRef]

Drehmann, M. and Juselius, M., 2012. Do debt service costs affect macroeconomic and financial stability? BIS Quarterly Review, September 2012.

Drehmann, M. and Juselius, M., 2014. Evaluating early warning indicators of banking crises: Satisfying policy requirements. International Journal of Forecasting, 30(3), pp. 759-780 [CrossRef]

Drehmann, M. and Tsatsaronis, K., 2014. The credit-to-GDP gap and countercyclical capital buffers: questions and answers. BIS Quarterly Review, March 2014.

Duarte, M., Feliciano, M. and De Lorenzo Buratta, I.,2022. How bad can financial crises be? A GDP tail risk assessment for Portugal. Working paper, No. 2022-04.

Duprey, T. and Klaus, B., 2017. How to predict financial stress? An assessment of Markov switching models. ECB Working Paper, No. 2057 [CrossRef]

ECB, 2017. A new database for financial crises in European countries. ECB/ESRB EU crises database. ECB Occassional Paper Series, No. 194.

ECB, 2018. A new Financial Stability Risk Index FSRI to predict near term risks of recessions and Predicting the likelihood and severity of financial crises over the medium term with a Cyclical Systemic Risk Indicator CSRI. European Central Bank.

Edge, R. and Meisenzahl, R., 2011. The Unreliability of Credit-to-GDP Ratio Gaps in Real Time: Implications for Countercyclical Capital Buffers. International Journal of Central Banking, 7(4)4, pp. 261-298 [CrossRef]

ESRB 2018b. The ESRB handbook on operationalising macroprudential policy in the banking sector. European Systemic Risk Board

ESRB, 2014. Recommendation of the European Systemic Risk Board of 18 June 2014 on guidance for setting countercyclical buffer rats (ESRB/2014/1). Official Journal of the European Union C 293/1.

EU and UN, 2017. Handbook on Cyclical Composite Indicators For Business Cycle Analysis. Luxembourg: European Union and United Nations [CrossRef]

Galán, J. and Rodríguez-Moreno, M., 2020. At-risk measures and financial stability. Financial stability review, 39, pp. 65-92.

Galán, J. E., 2019. Measuring Credit-to-GDP Gaps. The Hodrick-Prescott Filter Revisited. Documentos de Trabajo, No. 1906 [CrossRef]

Gersbach, H. and Rochet, J., 2017. Capital regulation and credit fluctuations. Journal of Monetary Economics, 90(C), pp. 113-124 [CrossRef]

Geršl, A. and Seidler, J., 2012. Credit Growth and Capital Buffers: Empirical Evidence from Central and Eastern European Countries. Research Policy Notes, No. 2/2011.

Giese, J. [et al.], 2014. The credit-to GDP gap and complementary indicator for macroprudential policy: evidence from the UK. International journal of finance and Economics, 19(1), pp. 25-47 [CrossRef]

Grinderslev, O. J. [et al.], 2017. Financial Cycles: Implementing SRISK in a Danish context. Working Paper, No. 105.

Gross, M., 2022. Beautiful Cycles: A Theory and a Model Implying a Curious Role for Interest. Economic Modelling, 106, pp. 1-21 [CrossRef]

Hájek, J., Frait, J. and Plašil, M., 2017. The Countercyclical Capital Buffer in the Czech Republic. CNB Financial Stability Report, 2016/2017, pp. 106‒114.

Hamilton, J. D., 2018. Why You Should Never Use the HP Filter. Review of Economic Statistics, 100(5), pp. 831-843 [CrossRef]

Hodrick, R. and Prescott, E., 1997. Postwar U.S. Business Cycles. An Empirical Investigation. Journal of Money, Credit, and Banking, 29, pp. 1–16 [CrossRef]

Holló, D., Kremer, M.and Lo Duca, M., 2012. CISS – A composite indicator of systemic stress in the financial system. ECB Working Paper Series, No. 1426.

Jackson, J. E., 1991. A User's Guide to Principal Components. Wiley and Sons [CrossRef]

Jiménez, G., Salas, V. and Saurina, J., 2006. Determinants of Collateral. Journal of Financial Economics, 81(2), pp. 255–281 [CrossRef]

Jokipii, T., Nyffeler, R. and Riederer, S., 2021. Exploring BIS credit-to-GDP gap critiques: the Swiss case. Swiss Journal of Economics and Statistics, 157(7), pp. 1-19 [CrossRef]

Jordà, O., Schularick, M. and Taylor, A. M., 2013. When Credit Bites Back. Journal of Money, Credit, and Banking, 45(s2), pp. 3-28 [CrossRef]

Jordà, O., Schularick, M. and Taylor, A. M., 2017. Macrofinancial History and the New Business Cycle Facts. NBER Macroeconomics Annual, 31, pp. 213-263 [CrossRef]

Jordá, O., Schularick, O. and Taylor, A. M., 2015. Leveraged Bubbles. Journal of Monetary Economics, 76(Supplement), pp. S1–S20 [CrossRef]

Kamber, G., Morley, J. and Wong, B., 2018. Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter. The Review of Economics and Statistics, 100(3), pp. 550-566 [CrossRef]

Kamin, S. B.and DeMarco, L. P., 2012. How did a domestic housing slump turn into a global financial crisis? Journal of International Money and Finance, 31(1), pp. 10-41 [CrossRef]

Kaminsky G., Lizondo, S. and Reinhart, C., 1998. Leading Indicators of Currency Crises. IMF Staff Papers, No. 451 [CrossRef]

Karamisheva, T. [et al.], 2019. Financial Cycle in the Bulgarian Economy and Its Interaction with the Business Cycle. Discussion papers, No. DP/113/2019.

Kauko, K., 2012a. Triggers for countercyclical capital buffers. MPRA Paper, No. 85692.

Kauko, K., 2012b. External deficits and non-performing loans in the recent financial crisis. Economics Letters, 115, pp. 196-199 [CrossRef]

Kirman, A., 1992. Whom or What Does the Representative Individual Represent? Journal of Economic Perspectives, 6(2), pp. 117–136 [CrossRef]

Kiyotaki, N. and Moore, J., 1997. Credit Cycles. Journal of Political Economy, 105(2), pp. 211-248 [CrossRef]

Kraft, E. and Galac, T., 2011. Macroprudential Regulation of Credit Booms and Busts, The Case of Croatia. Policy Research Working Paper, No. 5772 [CrossRef]

Krygie, D. and Vasi, T., 2021. Macrofinancial conditions, financial stability and economic growth in Sweden – evaluating the Growth-at-Risk framework. Staff memo, Sveriges Riskbank.

Laeven, L. and Valencia, F., 2008. Systemic Banking Crises: A New Database. IMF Working Paper, No. WP/08/224 [CrossRef]

Laeven, L. and Valencia, F., 2012. Systemic banking crises database: an update. IMF working paper, No. WP/12/163.

Laina, P., Nyholm, J. and Sarlin, P., 2015. Leading Indicators of Systemic Banking Crises: Finland in a Panel of EU Countries. Review of Financial Economics, 24, pp. 18–35 [CrossRef]

Lang, J-H. [et al.], 2019. Anticipating the bust: a new cyclical systemic risk indicator to assess the likelihood and severity of financial crises. Occasional Paper Series, No. 219 [CrossRef]

Lo Duca, M. [et al.], 2017. A new database for financial crises in European countries. Occasional paper series, No. 194 [CrossRef]

Lombardi, M. J., Mohatny, M. and Shim, I., 2017. The real effects of household debt in the short and long run. BIS working paper, No 607.

López-Salido, D., Stein, J. C. and Zakrajšek, E., 2017. Credit-Market Sentiment and the Business Cycle. Quarterly Journal of Economics, 132(3), pp. 1373-1426 [CrossRef]

Minsky, H. P., 1975. John Maynard Keynes. New York: Columbia University Press [CrossRef]

Minsky, H. P., 1982. Can “it” happen again? Essays on instability and finance. New York: M. E. Sharpe [CrossRef]

Minsky, H. P., 1986. Stabilizing an Unstable Economy. New Haven: Yale University Press.

OECD, 2012. OECD system of composite leading indicators. Paris: OECD.

Pedersen, T. M., 2001. The Hodrick-Prescott filter, the Slutzky effect, and the distortionary effect of filters. Journal of Economic Dynamics and Control, 25, pp. 1081-1101 [CrossRef]

Pfeifer, L. and Hodula, M., 2018. A profit-to-provisioning approach to setting the countercyclical capital buffer: the Czech example. CNB working paper, No. 5/2018 [CrossRef]

Plašil, M. [et al.], 2015. In the Quest of Measuring the Financial Cycle. CNB working paper series, No. 5. Czech National Bank.

Plašil, M., Seidler, J. and Hlaváč, P., 2016. A New Measure of the Financial Cycle: Application to the Czech Republic. Eastern European Economics, 544, pp. 296-318 [CrossRef]

Rinaldi, L. and Sanchis-Arellano, A., 2006. Household debt sustainability: what explains household non-performing loans? An empirical analysis. ECB Working Paper Series, No. 570 [CrossRef]

Rünstler, G. and Vlekke, M., 2016. Business, Housing, and Credit Cycles. ECB Working Paper Series, No. 1915 [CrossRef]

Rychtarik, Š., 2014. Analytical background for the counter-cyclical capital buffer decisions in Slovakia. Ročník, 22(4), pp. 10-15.

Rychtárik, Š., 2018. Follow-up on CCyB in Slovakia: build-up, calibration and release. Ročnik, 263, pp. 20 - 24.

Schularick, M. and Taylor, A. M., 2012. Credit booms gone bust: monetary policy, leverage cycles, and financial crises, 1870-2008. The American Economic Review, 1022, pp. 1029-1061 [CrossRef]

Škrinjarić, T. and Bukovšak, M., 2022. Procjena kreditnog jaza: kako unaprijediti kvantitativnu podlogu za oblikovanje protucikličnog zaštitnog sloja kapitala u Hrvatskoj. Working paper, No. 70.

Škrinjarić, T., 2022. Uvođenje kompozitnog indikatora cikličkog sistemskog rizika u Hrvatskoj: mogućnosti i ograničenja. Working paper, No. 71.

Tayler, W. J. and Zilberman, R., 2016. Macroprudential regulation, credit spreads and the role of monetary policy. Journal of Financial Stability, 26, pp. 144–158 [CrossRef]

Tölö, E., Laakkonen, H. and Kalatie, S., 2018. Evaluating Indicators for Use in Setting the Countercyclical Capital Buffer. International Journal of Central Banking, 142, pp. 51-111.