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Leading indicators of financial stress in Croatia: a regime switching approach
Tihana Škrinjarić* 
Article | Year: 2023 | Pages: 205 - 232 | Volume: 47 | Issue: 2 Received: June 1, 2022 | Accepted: November 21, 2022 | Published online: June 12, 2023
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FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
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Category
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Variables
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Transformations
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Credit
dynamics
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Narrow and
broad credit
Narrow and broad credit-to-GDP ratio
Household (HH) credit
Non-financial corporations (NFC) credit
HH credit-to-GDP ratio
NFC credit-to-GDP ratio
(nominal and real variants of credit)
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1YG
1YC
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A2YG
A2YC
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HP gap
25K
85K
125K
400K
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Potential
overvaluation of property prices
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House price
index (nominal and real)
Price to rent ratio (nominal and real)
Price to income ratio (nominal and real)
Price to cost ratio (nominal and real)
Construction work index ratio
Indicator of overvaluation of property prices
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1YG
1YC
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A2YG
A2YC
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HP gap
1600
25K
85K
125K
400K
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External
imbalances
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Gross
external debt (GED)
Net external debt (NED)
GED-to-GDP ratio
NED-to-GDP ratio
Terms of trade
Current account to GDP ratio
Net export to GDP ratio
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1YG
1YC
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A2YG
A2YC
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HP gap
1600
25K
85K
125K
400K
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Strength of
bank balance sheets
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Deposit to
credit ratio
Capital to assets ratio
Assets to GDP ratio
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1YG
1YC
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A2YG
A2YC
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HP gap
1600
25K
85K
125K
400K
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Private
sector debt burden
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Debt service
ratio (household and nonfinancial corporations separately)
Total debt to income ratio
Total debt to gross operating surplus ratio
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1YG
1YC
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A2YG
A2YC
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HP gap
25K
85K
125K
400K
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Mispricing of
risks
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Stock market
index CROBEX
HH credit interest rate margin8
NFC credit interest rate margin
Bank prudence indicator8
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1YG
1YC
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A2YG
A2YC
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HP gap 1600
25K
85K
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Source: CNB (2022).
Source: CNB (2022) and author’s calculation (for probabilities).
wertzd
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Parameter/values
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Regime
1
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Regime
2
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Constant
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0.1718
(0.014)***
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0.0657
(0.006)***
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Variance
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0.0081
(0.005)*
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0.0004
(0.000)***
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βij
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0.7912
(0.149)***
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0.0385
(0.027)
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AR(1)
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0.7351
(0.110)***
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AR(2)
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-0.2186
(0.102)**
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AIC
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-247.566
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Log L
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131.873
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RCM
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14.776
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Note: β ij is the estimated parameter for constant governing the regime switching in eq. (3). AIC and Log L denote the Akaike information criterion and log likelihood value respectively. Values in parentheses denote standard error of the estimator. *, ** and *** denote statistical significance at 10%, 5% and 1% respectively. Source: Author’s calculation.
Note: Shaded areas denote 95% confidence interval for the median value. “balance sheets”, “burden”, “credit”, “external”, “house” and “mispricing” denote the six categories of measures from table 1: strength of balance sheets, private sector debt burden, credit dynamics, external imbalances, potential overvaluation of house prices and mispricing of risks respectively. t_4, t_5, t_6 and t_7 denote models that include indicator variables lagged for 4, 5, 6 and 7 quarters respectively. Source: Author’s calculation.
Note: Shaded areas denote 95% confidence interval for the median value. “balance sheets”, “burden”, “credit”, “external”, “house” and “mispricing” denote the six categories of measures from table 1: strength of balance sheets, private sector debt burden, credit dynamics, external imbalances, potential overvaluation of house prices and mispricing of risks respectively. t_4, t_5, t_6 and t_7 denote models that include indicator variables lagged for 4, 5, 6 and 7 quarters respectively. Source: Author’s calculation.
Note: “balance sheets”, “burden”, “credit”, “external”, “house” and “mispricing” denote the six categories of measures from table 1: strength of balance sheets, private sector debt burden, credit dynamics, external imbalances, potential overvaluation of house prices and mispricing of risks respectively. t_4, t_5, t_6 and t_7 denote models that include indicator variables lagged for 4, 5, 6 and 7 quarters respectively. Source: Author’s calculation.
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Indicator
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t-7
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t-6
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t-5
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t-4
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HPI 2y growth
rate
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0.187
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0.164
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0.152
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0.163
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HPI real, gap
25K
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0.176
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0.130
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0.131
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0.161
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Capital/Assets,
gap 1600
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0.147
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0.100
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0.130
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0.119
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CNFP real 1y
change
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0.072
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0.091
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0.171
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0.131
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Basel gap,
25K
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0.277
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0.238
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0.381
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0.456
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Broad credit
1y change
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0.069
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0.075
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0.066
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0.071
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Broad credit
real 1y change
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0.046
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0.050
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0.044
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0.047
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Narrow credit
gap, 125K
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0.654
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0.748
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0.740
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0.689
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Narrow credit
gap, 25K
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0.740
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0.833
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0.836
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0.777
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Narrow credit
gap, 400K
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0.645
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0.737
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0.727
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0.675
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Narrow credit
gap, 85K
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0.663
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0.757
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0.754
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0.705
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CNFC 2y
growth rate
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0.128
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0.139
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0.156
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0.147
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H D-to-I 2y
change
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0.019
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0.018
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0.019
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0.016
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CNFC, 25K
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0.185
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0.181
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0.187
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0.163
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P-to-Income,
400K
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0.110
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0.055
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0.143
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0.165
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Deposits/Credit,
1600
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0.019
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0.018
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0.020
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0.023
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HPI real 1y
growth rate
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0.149
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0.150
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0.177
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0.187
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P-to-I 2y
growth rate
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0.190
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0.181
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0.198
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0.231
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P-to-I real
2y growth rate
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0.164
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0.171
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0.199
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0.238
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Narrow credit
1y growth rate
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0.149
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0.175
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0.230
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0.268
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Narrow credit
2y growth rate
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0.149
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0.175
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0.230
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0.268
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NX, cumsum
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0.407
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0.359
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0.321
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0.311
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Net ext debt,
125K
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0.332
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0.310
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0.404
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0.332
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Net ext debt,
25K
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0.310
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0.404
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0.332
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0.332
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Net ext debt,
400K
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0.332
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0.310
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0.404
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0.332
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Net ext debt,
85K
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0.332
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0.310
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0.404
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0.332
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NX
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0.327
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0.291
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0.301
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0.392
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HH 1y change
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0.509
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0.294
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0.300
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0.556
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Note: 1600, 25K, 85K, 125K and 400K denote the value of the smoothing parameter in HP gap, in values of 1,600, 25,000, 85,000, 125,000 and 400,000 respectively. 1y and 2y are one and two years, CNFP is credit to nonfinancial corporations, HPI is house price index, Capital/Assets is the capital-to-assets ratio. t-4, t-5, t-6 and t-7 denote models that include indicator variables lagged for 4, 5, 6 and 7 quarters respectively. HH denotes credit to households, Net ext debt denotes net external debt, NX is net exports share in GDP, whereas NX cumsum is sum of net exports over 4 quarters share in sum of GDP over 4 quarters, P-to-I is the price to income ratio, HPI is house price index, Deposits/Credit denotes the deposit-to-credit ratio, P-to-Income is house price-to-income ratio, CNFC is credit to nonfinancial corporations, H D-to-I is household debt to income ratio. Source: Author’s calculation.
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AR/MA
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0
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1
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2
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3
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0
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-1.964473
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-2.521833
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-2.679371
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-2.664269
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1
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-2.591213
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-2.672851
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-2.653948
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-2.597705
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2
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-2.683885
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-2.638946
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-2.603793
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-2.553256
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Source: Author’s calculation.
Note: “balance sheets”, “burden”, “credit”, “external”, “house” and “mispricing” denote the six categories of measures from table 1: strength of balance sheets, private sector debt burden, credit dynamics, external imbalances, potential overvaluation of house prices and mispricing of risks respectively. t_4, t_5, t_6 and t_7 denote models that include indicator variables lagged for 4, 5, 6 and 7 quarters respectively. Source: Author’s calculation.
Table 1Brief description of variables used in the study DISPLAY Table
Figure 1HIFS values (left hand side) and the one-step-ahead and filtered probability of being in the regime of higher stress (right hand side) DISPLAY Figure
Table 2Regime switching estimation results for a 2-regime AR(2) model for HIFS DISPLAY Table
Figure 2Box plot for log likelihood values across all 964 models DISPLAY Figure
Figure 3Box plot for AIC values across all 964 models DISPLAY Figure
Figure 4Empirical density functions of log likelihood values across all 964 models DISPLAY Figure
Table 3Significant coefficients, probability of transitioning from lower to higher risk regime (upper panel) and significant coefficients, probability of staying in the higher risk regime (lower panel) DISPLAY Table
Table A1SIC information criterion on different specifications of ARMA(p,q) model for variable HIFS DISPLAY Table
Figure A2Log likelihood statistics for regime switching models with variable indicators included in the equation (3) DISPLAY Figure
* The views expressed are those of the author and do not necessarily reflect the official views of the institution the author works at. The author is grateful to two anonymous referees who have contributed to the quality of the final version of the paper. The paper was written when the author was at Croatian National Bank, Department for Financial Stability.
1 This is due to the intuitive interpretation of dynamics and characteristics of economic and financial variables via expansions and contractions in real economy or bull and bear markets in financial markets (see Ang and Timmermann,  2012). Structural and abrupt changes in the economy and/or financial markets due to political issues, legislative changes, new methodological approaches, etc. can be captured via the regime-switching approach (Baele, 2003 is an example of the economic and monetary integration of Western Europe affecting the European financial markets).
2 Croatia has a specific, i.e., unique experience regarding macroprudential policymaking and monitoring cyclical risk accumulation. It stands out due to it belonging to a group of countries that had the most intensive use of instruments before the global financial crisis (Vujčić and Dumičić, 2016). This means that macroprudential policy was active during the boom and bust phases of the financial cycle. Analysis regarding Croatian data could provide insights into the effects of macroprudential policy during all phases of the cycle, the effects on the financial stress, and other analyses of interactions of this policy with the rest of the economy. As Vujčić and Dumičić ( 2016) state, Croatian experience shows that policymakers shouldn’t focus only on textbook approaches to macroprudential policy conduct but, to keep their minds open, also on analysis such as the one in this study. However, a not-typical one could be one of the starting points. Moreover, Bambulović and Valdec ( 2020) state that Croatia is an interesting country for a study of the effects of macroprudential policies on credit growth, as the Croatian National Bank employed many measures in the pre-GFC period. More specifically, greater macroprudential activity started in 2003, as it was seen that monetary policy would not be efficient alone (see Kraft and Galac, 2011).
3 That is why forecasting future financial stress would be helpful in order to adjust the instruments and measures so that the financial system is more robust and stable over time, alongside reducing the costs of financial crises. Moreover, financial stress indicators are used in the quick release of the countercyclical capital buffers (CCyB), as at the beginning of the COVID-19 crisis in some countries (see Arbatli-Saxegaard and Muneer, 2020).
4 A stress event is defined if the stress indicator exceeds its mean value enlarged by its standard deviation multiplied by a constant k. This could be arbitrary, as opposed to the regime-switching that is based on the optimization of the likelihood function in which optimal switching behaviour is governed by the data. This is what the authors concluded at the end of the study: future research should focus on switching models.
5 A drawback of the study is found in determining the stress periods when the financial condition indicator exceeds the 90 th percentile of the country's indicator distribution. The regime-switching approach does not ask for such interventions from the researcher's side.
6 Calculation of HIFS is based on volatilities and similar measures and correlations derived from daily data on bond yields, stock market returns, money market interest rates, exchange rates, and interbank market rates and fragility. It does not include any of the individual indicators (or their transformations) used in MRS modelling.
7 Margins were calculated as the difference between the interest rates on credits to HH or NFC and the Euribor interest rate, as the national referent interest rate ceased to exist in 2020. Comparable studies (e.g. Kupkovič and Šuster, 2020) also use Euribor as the referent interest rate.
8 Defined in Pfeifer and Hodula ( 2018).
9 The results in figure 1 are comparable to those in Dumičić ( 2015a) in sub-periods of greater and lower stress. Furthermore, based on the discussion in section 4.3., in a comparison of the results to the discussion in the mentioned paper, findings on indicators in table 3 of this research confirm the importance of these indicators for macroprudential policy-making, alongside the measures that the CNB conducted in the observed sample. For more details, please see section 4.3.
10 As the baseline model has LogL value of 131.873 (see table 2), inclusion of indicator variables in the model is considered successful if the new model has a higher value than the baseline model to ensure comparability across all specifications, all models are contrasted on the same length of T.
11 See literature review section, and introduction.
12 And more data on the characteristics of individual indicators during different phases of the financial cycle, so that the duration of the cycle could be estimated better.
13 That is why additional analysis was done by constructing a composite indicator of cyclical risks based on the results from this study and it has a potential for being used in practice. Details are available upon results.
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June, 2023
II/2023
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