Macroeconomic effects of systemic stress: a rolling spillover index approach
https://doi.org/10.3326/pse.46.1.4 | Published online: March 8, 2022 Table 1
Variables description
Table 2
Spillover table, full sample, in percent
Figure 1
Total spillover index, h = 12, rolling windows 30, 36 and 42 months
Source: Author’s calculation.
Figure 2
Net pair-wise spillover indices between each variable and CISS, h =12, rolling windows 36 months
Note: Dashed lines indicate dates from left to right: Global financial crisis, Euro crisis, Brexit
referendum vote and COVID-19 crisis.
Source: Author’s calculation. Figure 3
Comparison of rolling net spillover indices (grey lines, right axis) to the Granger causality test p-value (black lines, left axis), 36 months window length, CISS is the cause
Note: Dashed grey line is the zero value for the spillover index; black dashed line is the 10%
significance level.
Source: Author’s calculation. Figure 4
Comparison of rolling net spillover indices (grey lines, right axis) to the Granger causality test p-value (black lines, left axis), 36 months window length, CISS is the response
Note: Dashed grey line is the zero value for the spillover index; black dashed line is the 10%
significance level.
Source: Author’s calculation. Figure 5
Net pair-wise spillover indices between each variable and CISS, h =12, rolling windows 30, 36 and 42months
Source: Author’s calculation.
Figure 6
Comparison of total spillover index, 36 rolling window length, CISS (total 36, black line) and squared root of CISS (sqciss, grey line) in VAR model
Source: Author’s calculation.
Table 3
Spillover table, full sample, in percent, monthly DGDP used
Figure 7
Total spillover index, h = 12, rolling window length 36 months, comparison of DIIP (grey line) to the DGDP (black line) variable specification
Source: Author’s calculation.
Table A1
Unit root test results for all variables in the model
Source: Author’s calculation. Figure A1
Total spillover indices, h =12, rolling windows 30, 36 and 42 months, DGDP compared to DIIP
Source: Author’s calculation.
Figure A2
Net spillover indices between each variable and CISS, h =12, rolling windows 36 months, DGDP compared to DIIP Source: Author’s calculation. Figure A3
Pair-wise net spillover indices between each variable and CISS, h =12, 18 and 24, rolling windows 36 months, DIIP Source: Author’s calculation. Figure A4
Correlation between CISS and selected variables, rolling windows 30, 36 and 42 months
Note: Money market represents the realised volatility of the 3-month Euribor rate, interest rate
spread between 3-month Euribor and 3-month T-bills, and monetary Financial Institutions (MFI)
emergency lending at Eurosystem central banks; Intermediaries represents realised volatility of
the idiosyncratic equity return of the Datastream bank sector stock market index over the total,
yield spread btw A-rated fin. & non-fin. corp. (7y), CMAX for the Datastream non-fin. sector stock
market index interacted with the inverse price-book ratio for the fin. sector eqty. market index;
FX is the realised volatility of the euro exchange rate vis-a-vis the US dollar, the Japanese Yen
and the British Pound; Equity is realised volatility of the Datastram non-financial sector stock
market index, CMAX for the Datastream non-financial sector stock market index, and stock-bond
correlation; and Bond is realised volatility of the German 10-year benchmark government bond
index, yield spread between A-rated non-financial corporations and government bonds (7-year
maturity bracket), and 10-year interest rate swap spread.
Source: Author’s calculation. Figure A5
Generalized IRFs from VAR model, entire sample, reaction of CISS to shocks in other variables and reactions of others to shocks in CISS
Note: Black curve denotes estimated impulse response and red dashed curves denote the 95%
confidence interval.
Source: Author’s calculation. Figure A6
SVAR IRFs, entire sample, reaction of CISS to shocks in other variables and reactions of others to shocks in CISS
Note: Aut = Bεt is the setting within the SVAR, where matrix B is the unit matrix, and matrix A has
unit values on its diagonal with null values above the diagonal, and the rest of the values below
the diagonal estimated in the analysis.
Source: Author’s calculation.
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March, 2022 I/2022 |