## Appendix III. Results of Extended Regressions with Interactions between Monetary Policy and Macroprudential Policy

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### Objective and main hypothesis
- Examine whether an inflation targeting (IT) regime reinforces or dilutes the effectiveness of macroprudential instruments in achieving financial stability.
- Main hypothesis: the effectiveness of macroprudential tools in taming bank systemic risk varies depending on whether a country has an IT regime or not.
- Focus on bank systemic risk as proxied by SRISK, emphasizing cross-sectional interconnectedness among banks.

### Key concepts, measures, and data coverage
- SRISK measures:
  - SRISK: expected capital shortage of a bank during a crisis; reported in US$ million (SRISKm) and percentage terms (SRISK%).
  - MSRISK: annual series computed as the sum of the positive values of SRISKm in US$ billions over 12 months within a calendar year; negative SRISKm values ignored.
  - V‑LAB database (SRISK updated monthly since July 2000); LRMES aggregate market decline set at 40 pp.
  - Prudential capital requirement in SRISKm calculation: 8 pp for firms in Africa, Asia, and Americas and 5.5 pp for European firms.
- Macroprudential policy measures:
  - iMaPP database: monthly dummy-type indices capturing tightening (+1) and loosening (−1) for 134 countries from January 1990 to December 2018.
  - 15 policy instruments and 7 subcategories (22 monthly macroprudential policy indices); annual indices are averages of monthly values.
  - Composite MPI constructed following Cerutti et al. (2017a) and Alam et al. (2019).
- Monetary policy regime indicator:
  - IT index variable equals 1 for an IT country and 0 otherwise.
- Sample and coverage:
  - Final sample: 539 banks, 45 countries, sample period June 2000 to December 2018.
  - Alternative/unrestricted sample: 576 banks across 60 countries for 2000–2018.
  - Time aggregation: monthly iMaPP indices averaged to annual indices; bank and macro controls one-period-lagged.

### Empirical strategy and model specification
- Panel dynamic model at bank level estimated by System GMM (two-step) to address dynamics and unobserved heterogeneity.
- Baseline dynamic panel:
  - MSRISK_{i,k,t} = a·MSRISK_{i,k,t−1} + b·MEC_{k,t−1} + c·BSC_{i,k,t-1} + β1·MPI_{k,t} + β2·MPI_{k,t-1} + ψ_k + μ_{i,k,t}
- Extended specification with IT interactions:
  - MSRISK_{i,k,t} = a·MSRISK_{i,k,t−1} + b·MEC_{k,t-1} + c·BSC_{i,k,t-1} + d·IT_{k,t-1} + β1·MPI_{k,t} + β2·MPI_{k,t-1} + γ1·IT_{k,t}·MPI_{k,t} + γ2·IT_{k,t}·MPI_{k,t-1} + ψ_k + μ_{i,k,t}
  - γ1 and γ2 capture IT-country-specific impacts of macroprudential policy.
- Instruments and diagnostics:
  - GMM instruments include second lags of MEC, BSC, and MPI.
  - Hansen J-test employed; Arellano-Bond serial correlation tests used.
  - Significance markers reported: * (10 percent), ** (5 percent), *** (1 percent).

### Main empirical findings (overview)
- IT regime effect:
  - Coefficient on IT regime index is significantly negative in all benchmark regressions — bank systemic risk is significantly reduced under the IT regime.
- General:
  - Macroprudential policy reduces bank systemic risk and the IT regime enhances effectiveness of many macroprudential tools.
- Timing and context:
  - Pre-GFC: relatively loose macroprudential policies amid rising SRISK.
  - Post-GFC (2011−2018) until monetary normalization end-2015: tighter macroprudential policies accompanied lower SRISK (exceptions: some liquidity and supply-based tools).
  - Since monetary normalization from end-2015: tighter macroprudential policies accompanied tempered SRISK under IT and other regimes but rising SRISK under the ER regime.
- Monetary–macroprudential interactions:
  - Interactive terms of monetary policy stance (policy rate change, INT) and MPI largely positive with some statistical significance, consistent with tighter monetary policy together with tighter macroprudential policy supporting financial stability.

### Tool-specific findings (selected, preserving numeric precision)
- Demand-based measures:
  - DSTI:
    - Stricter DSTI limits mitigate bank systemic risk only under an IT regime.
    - DSTI × IT: negative and statistically significant at the 5 percent level.
    - DSTI contemporaneous coefficient: insignificant.
  - LTV:
    - Tightening LTV mitigates bank systemic risk with a lag only under an IT regime.
    - Lagged LTV × IT: negative and statistically significant at the 5 percent level.
    - Contemporaneous LTV coefficient: statistically positive and smaller.
- Capital requirements:
  - Capital (all sectors) and Capital.HH effective in attenuating systemic risk under IT regimes but not under other regimes.
  - Capital.Corp and Capital.Gen: mixed and insignificant.
  - Conservation buffers: effective with a lag; significant at the 1 percent level under IT.
  - CCyB: no pronounced effects (limited positive CCyB observations).
  - LVR (leverage limits): estimated positive effects on systemic risk; effect stronger under IT than other regimes.
- Loan-supply-based measures:
  - LCG (limits on credit growth): insignificant or incorrect sign; LCG t-1 marginally significant at the 10 percent level in some specs.
  - LoanR (loan restrictions): reduce systemic risk significantly, especially for households; effect muted under IT.
  - LLP (loan loss provisions): overall insignificant; under IT there are negative contemporaneous and lagged effects.
  - LTD (loan-to-deposit limits): tightening generally associated with declines in systemic risk; IT regime does not significantly alter contemporaneous LTD impact.
  - LFC (limits on foreign currency lending): can reduce SRISK in open economies and more so under IT; IT.LFC excluded in some tables due to lack of observations.
- Liquidity and other supply-based measures:
  - Liquidity requirements (LCR, NSFR): tighter liquidity requirements increase systemic risk with a lag under IT relative to other regimes; lagged Liquidity × IT positive and significant at the 1 percent level.
  - RR (reserve requirements): tightened RR tend to raise systemic risk via higher funding costs; lagged RR × IT negative and significant at the 5 percent level.
  - LFX (limits on FX exposures): contemporaneous and lagged LFX coefficients negative and significant — limiting FX exposures lowers systemic risk.
  - Tax-based measures and levies: no pronounced short-run effects; may reduce systemic risk in the long run (effects may take up to three years).

### Robustness checks and heterogeneity
- Post-GFC sample (2011−2018):
  - Capital.Corp contemporaneous effect: significant and negative.
  - Capital.Gen effective under IT but not under other regimes.
  - LLP contemporaneous term: significant and positive; under IT it is negative and significant.
  - LTD and tax help contain excessive credit and attenuate systemic risk.
- Unrestricted sample (576 banks, 60 countries, 2000–2018):
  - Most MPIs except LTD under IT are at least as effective as under other regimes.
  - Capital.Corp effective under IT but not under other regimes.
  - DSTI and LTV limits mitigate systemic risk without additional IT-specific contributions in this sample.
  - LVR, LLP, and liquidity requirements appear to help mitigate SRISK under IT in the unrestricted sample.
- AE vs EME split:
  - Effects of macroprudential policies on systemic risk tend to be more pronounced for EMEs than for AEs.
  - Exceptions: LTV, liquidity requirements, and LFC appear effective only for AEs.
- Monetary policy interaction:
  - Including interactive terms INT × MPI largely preserves benchmark qualitative results.
  - Estimated coefficients on INT × MPI largely positive with some statistical significance (examples include CCyB, LoanR, LFC, Tax).
  - Mixed or negative signs for interactions with demand-based measures and sectoral capital requirements in some cases.

### Mechanisms, interpretation, and methodological distinctions
- Why IT may enhance macroprudential effectiveness:
  - IT associated with greater credibility, transparency, and accountability — potentially lowering uncertainty about policy and improving banks’ risk profiles, strengthening macroprudential tightening effects.
  - If shocks move inflation and credit in same direction, monetary policy focused on price stability may help mitigate credit growth (example: Choi and Cook 2018).
- Why some tools may be less effective under IT:
  - Loan restrictions may be counterbalanced by credit extensions endogenously determined by policy rates under IT regimes.
- Distinctions vs related work (e.g., Meuleman and Vander Vennet 2020):
  - Use SRISK (forward-looking expected capital shortfall) instead of MES.
  - Broader sample (AEs and EMEs) and IMF iMaPP database (134 countries) versus MaPPED.
  - Extended models identify IT-specific interaction effects (γ1, γ2).

### Policy implications and conclusions
- Synergy under IT:
  - IT regime reinforces effectiveness of many macroprudential tools, notably DSTI, LTV, and certain capital requirements.
- Coordination:
  - Evidence supports coordination between monetary policy and macroprudential policy to mitigate financial stability risks.
- Heterogeneity and caveats:
  - Effects differ across tools, lags, and country groups; some tools may have counterproductive effects depending on context (e.g., LVR positive on SRISK).
  - Limited observations for certain tools (e.g., CCyB, IT.LFC) constrain inference.
- Timing and transmission:
  - Several tools show lagged effectiveness (e.g., conservation buffers, RR, taxes); others show contemporaneous or mixed effects.
  - Monetary policy normalization since end-2015 linked to instances where macroprudential tightening raised bank funding costs amid higher policy rates, contributing to SRISK dynamics.
- Overall conclusion:
  - Empirical evidence indicates macroprudential policy reduces bank systemic risk and that the IT regime enhances the effectiveness of most macroprudential tools.
  - Coordinated policy frameworks between monetary and macroprudential authorities are beneficial for financial stability.
  - Further research recommended to clarify inter-linkages and transmission channels between monetary regimes and macroprudential tool effectiveness.

*International Monetary Fund — Macroprudential Policy and Bank Systemic Risk: Does Inflation Targeting Matter? — Appendix III (Working Paper No. WP/2023/119; source PDF wpiea2023119-print-pdf)*

### References .............................................................................................................

### wpiea2023119-print-pdf - References .............................................................................................................

### References
- References ......................................................................................................................................................... 29

### Appendix I
- Appendix I. Macroprudential Policy and SRISK Metrics: IT vs. Non-IT Countries ...................................... 33

### Appendix II
- Appendix II. Interpretation of the Results Related to Macroeconomic and Bank-Specific Variables ....... 35

*Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023119-print-pdf.pdf*

### Appendix III. Results of Extended Regressions with Interactions between Monetary Policy and

### Appendix III. Results of Extended Regressions with Interactions between Monetary Policy and Macroprudential Policy

### Objective and Main Hypothesis
- Examine whether an inflation targeting (IT) regime reinforces or dilutes the effectiveness of macroprudential instruments in achieving financial stability.
- Main hypothesis: the effectiveness of macroprudential tools in taming bank systemic risk varies depending on whether a country has an IT regime or not.
- Focus: bank systemic risk as a proxy for financial stability, emphasizing the cross-sectional dimension of systemic risk (interconnectedness among banks).

### Key Concepts and Measures
- Systemic risk measure: SRISK (Brownlees and Engle 2017; Acharya et al. 2017).
  - SRISK: expected capital shortage of a bank during a crisis; reported in US$ million (SRISKm) and percentage terms (SRISK%).
  - MSRISK: annual series computed as the sum of the positive values of SRISKm in US$ billions over 12 months within a calendar year.
  - Negative SRISKm values (capital surpluses) are ignored by taking only positive SRISKm.
- Macroprudential policy measures:
  - Integrated Macroprudential Policy Database (iMaPP) (Alam et al. 2019) used as primary source.
  - iMaPP: monthly dummy-type indices capturing tightening (+1) and loosening (−1) for 134 countries from January 1990 to December 2018.
  - Annual indices obtained by averaging monthly values over a calendar year.
  - Composite macroprudential index (MPI) constructed as in Cerutti et al. (2017a) and Alam et al. (2019).
- Monetary policy regime indicator:
  - IT index variable takes value 1 for an IT country and 0 otherwise.

### Empirical Strategy and Model Specification
- Panel and estimation approach:
  - Cross-country dynamic panel at bank-level with yearly frequency.
  - Estimation method: System GMM (to avoid unobserved heterogeneity issues and address dynamics).
  - All bank-specific characteristics and macroeconomic control variables are one-period-lagged to mitigate endogeneity; lagged MPI variables included to allow for transmission delay.
- Baseline dynamic panel model (notation preserved as in source):
  - MSRISK_{i,k,t} = a·MSRISK_{i,k,t−1} + b·MEC_{k,t−1} + c·BSC_{i,k,t-1} + β1·MPI_{k,t} + β2·MPI_{k,t-1} + ψ_k + μ_{i,k,t}
    - i = 1, ..., N (banks); k = 1, ..., K (countries); t = 1, ..., T (years).
    - MEC: vector of global- and country-level macroeconomic variables (macro controls and other policies).
    - BSC: bank-level characteristics.
    - ψ_k: country fixed effects; μ_{i,k,t}: error term.
- Extended (pseudo non-linear) specification to capture IT-specific impacts:
  - MSRISK_{i,k,t} = a·MSRISK_{i,k,t−1} + b·MEC_{k,t-1} + c·BSC_{i,k,t-1} + d·IT_{k,t-1} + β1·MPI_{k,t} + β2·MPI_{k,t-1} + γ1·IT_{k,t}·MPI_{k,t} + γ2·IT_{k,t}·MPI_{k,t-1} + ψ_k + μ_{i,k,t}
  - Interpretation: γ1 and γ2 capture IT-country-specific impacts of macroprudential policy. If IT reinforces stabilizing role, expect negative and significant β1 and γ1 (and/or β2 and γ2 for lagged effects).

### Data, Coverage, and Sample Construction
- SRISK source:
  - V‑LAB database reports SRISK in US$ million (SRISKm) and SRISK%.
  - MSRISK constructed annually as sum of positive SRISKm values in US$ billions over 12 months.
- Macroprudential policy data:
  - iMaPP database: monthly indices for 134 countries, January 1990 to December 2018.
  - 15 policy instruments and 7 subcategories (22 monthly macroprudential policy indices) used where observations available.
  - Annualization: annual average of monthly indices by country.
- Sample in study:
  - Bank-level data for 45 countries comprising both advanced economies (AEs) and emerging market economies (EMEs) with various monetary policy regimes.
  - MPI classification advantages: iMaPP covers 134 countries (vs. 28 in MaPPED) and presents 17 vs. 11 groups in MaPPED; allows sectoral classification of macroprudential policies.
- Time aggregation and treatment:
  - Monthly iMaPP indices averaged to annual indices.
  - Bank- and macro-level control variables used at one-period lag.

### Rationale and Expected Mechanisms
- Why IT may enhance macroprudential effectiveness:
  - IT often associated with greater policy credibility, transparency, and accountability — potentially lowering uncertainty about economic policy and price levels.
  - Lower uncertainty could improve banks’ risk profiles and strengthen the impact of macroprudential tightening.
  - If shocks move inflation and credit in the same direction, monetary policy oriented to price stability may help mitigate credit growth (example: Choi and Cook 2018).
- Why some macroprudential measures may be less effective under IT:
  - Loan restrictions that impose direct controls over bank loans may be counterbalanced by credit extensions endogenously determined by the policy rate under interest rate policy in IT regimes.

### Methodological Distinctions versus Related Work
- Differences from Meuleman and Vander Vennet (2020):
  - SRISK used instead of MES; SRISK is more forward-looking and captures expected capital shortfall during crisis.
  - Focus on the impact of IT on interaction between macroprudential tools and bank systemic risk, accounting for time variation in country-specific and global variables.
  - Broader sample: includes both AEs and EMEs across regions (not limited to EU).
  - Different macroprudential policy measures: use of IMF’s iMaPP database instead of ECB’s MaPPED.
  - Estimation of extended models allowing identification of IT-specific interaction effects (γ1, γ2).

*Appendix III. Results of Extended Regressions with Interactions between Monetary Policy and Macroprudential Policy — IMF Working Paper content as provided*

### 1. DSTI

### 1. DSTI

### Definitions of macroprudential instruments (classification from Alam et al. (2019))
- DSTI: Limits to the debt-service-to-income ratio restrict the size of debt services relative to income (targeted at housing loans, consumer loans, and commercial real estate).
- LTV: Limits to the loan-to-value ratios, including those mostly targeted at housing loans, but also includes those targeted at automobile loans and commercial real estate loans.
- Capital: Bank capital requirements, which include risk weights, systemic risk buffers, and minimum capital requirements. Subcategories: household sector targeted (HH), corporate sector targeted (Corp), and broad-based (Gen).
- CCyB: A requirement for banks to maintain the countercyclical capital buffer.
- Conservation: Requirements for banks to maintain a capital conservation buffer, including the one established under Basel III.
- LVR: A limit on bank leverage, calculated by dividing a measure of capital by the bank’s non-risk-weighted exposures.
- LCG: Limits on credit growth or the volume of aggregate credit, the household sector credit, or the corporate-sector credit by banks, and penalties for high credit growth. Subcategories: household sector targeted (HH) and broad-based (Gen) measures.
- LoanR: Loan restrictions. They include loan limits and prohibitions, which may be conditioned on loan characteristics, bank characteristics and other factors. Subcategory: household sector targeted (HH).
- LLP: Loan loss provision requirements for macroprudential purposes, which include dynamic provisioning and sectoral provisions (e.g., housing loans).
- LTD: Limits to the loan-to-deposit ratio and penalties for high LTD ratios.
- LFC: Limits on foreign currency lending, and rules or recommendations on FC loans.
- Liquidity: Measures to mitigate systemic liquidity and funding risks, including minimum requirements for liquidity coverage ratios, liquid asset ratios, net stable funding ratios, core funding ratios and external debt restrictions that are not differentiated across currencies.
- RR: Reserve requirements (domestic or foreign currency-based) for macroprudential purposes.
- LFX: Limits on net or gross open foreign exchange (FX) positions, limits on FX exposures and FX funding, and currency mismatch regulations.
- Tax: Taxes and levies applied to specified transactions, assets, or liabilities, which include stamp duties, and capital gain taxes.

### SRISK/SRISKm data construction and calibration
- SRISK measure for banks sourced from V-LAB (http://vlab.stern.nyu.edu/), updated monthly since July 2000; V-LAB proposes a Global Dynamic MES measure based on Acharya et al. (2017).
- Long-Run Marginal Expected Shortfall (LRMES) aggregate market declines: 40 pp (following Brownlees and Engle (2017)).
- Prudential capital requirement used in calculating the expected capital shortfall (SRISKm): 8 pp for firms in Africa, Asia, and Americas and 5.5 pp for European firms.

### Sample selection and coverage
- Inclusion criteria:
  - Only include banks for which at least one bank-control variable is available for the whole sample period.
  - Limit sample to countries with three or more banks to avoid under-representation bias.
- Final sample:
  - 539 banks
  - 45 countries
  - Sample period: June 2000 to December 2018

### Macroeconomic and global controls (annual frequency)
- Country-specific macro controls (main sources: World Bank, BIS, Bloomberg, central banks’ websites when needed):
  - GDP: year-on-year growth of GDP
  - Savings: gross domestic savings as percent of GDP
  - CPI: consumer price inflation
  - FX: growth of the exchange rate with respect to US dollars
  - INT: policy rates (central bank policy rates from BIS when available, otherwise discount, REPO or short-term interest rates reported by IMF, Bloomberg, OECD, or central banks)
  - FD: index of financial development reported by the IMF
- Global macro controls:
  - VIX: U.S. VIX measure from Chicago Board Options Exchange Market (reported by FRED)
  - FFR: U.S. Fed’s policy rate (from Haver Analytics)
  - Global.GDP: Global GDP growth at constant prices (from the World Bank database)

### Bank-specific variables (from ORBIS, linked to V-LAB identifiers)
- SIZE: total assets and customer deposits (DEP) in US$ bn
- CAP: equity to total assets
- LTA: net loans to total assets
- NPL: non-performing loans in total loans
- ROA: pre-tax income to total assets

### Empirical controls for capital flows and expectations (footnote summary)
- The effect of capital flows on bank systemic risk is largely controlled by VIX and FFR, along with INT, as regressors in benchmark regressions.
- Expected relationships:
  - Bank systemic risk is positively associated with global stock market volatility (VIX), reflecting global investors’ sentiment.
  - U.S. Fed’s policy rate changes (FFR) and INT are likely to affect capital inflows and thereby financial stability risks.
- Empirical results are consistent with these priors, suggesting that capital flows could pose financial stability risk.

*Source: IMF Working Paper content (excerpt).*

### Appendix II). As shown by Choi and Cook (2018), however, domestic interest rate policy under IT would respond to capital

### Macroprudential Policy and Bank Systemic Risk: Does Inflation Targeting Matter?

### Data and Variables
- Sample: 45 countries; bank-level data for 539 banks (Orbis).
- Alternative/unrestricted sample: 576 banks across 60 countries for the period 2000–2018.
- Time coverage referenced in figures and discussion: 2004 – 18; post-GFC period defined as 2011−2018.
- Key dependent variable: SRISK (and MSRISK0 where zero is excluded).
- Country-level controls include: GDP (Real GDP annual growth), Savings (percent of GDP), FX (exchange rates per US$ annual growth), INT (central bank policy rate annual difference), CPI (annual growth), FD (index of financial development), kaopen (Chinn-Ito index).
- Global controls include: VIX (U.S. CBOE Volatility Index annual average), FFR (U.S. Fed’s policy rate annual difference), Global.GDP (Global real GDP annual growth).
- Bank-level controls include: SIZE (Total assets), DEP (Total customer deposits), ROA (Pre-tax income to total assets), NPL (Non-performing loans in total loans), CAP (Equity to total assets), LTA (Net loans to total assets).
- Regime dummies constructed from AREAER: IT dummy (Inflation Targeting) and ER dummy (Exchange Rate Anchor). Dummies take value 1 when the country has the respective regime in the year considered, zero otherwise.

### Methodology
- Benchmark model: dynamic nonlinear panel model estimated via two-step GMM à la Arellano and Bond (1991).
- 22 regression equations: one per MPI (macroprudential index/measure).
- GMM instruments: second lag of global- and country-level macroeconomic variables (MEC), bank-level variables (BSC), and the corresponding MPI.
- Diagnostic tests:
  - Hansen test of overidentifying restrictions (J−test): instruments cannot be rejected; model specification adequate.
  - Arellano-Bond serial correlation tests: no evidence of second-order autocorrelation.
- Interpretation: significance of MPI variables in GMM framework can be interpreted as causal in the Granger sense if instruments are valid.
- Magnitude of MPI total effect computed as sum of contemporaneous and lagged coefficients.

### Main Empirical Findings
- IT regime effect:
  - The coefficient estimate on the IT regime index is significantly negative in all benchmark regressions — indicating that bank systemic risk is significantly reduced under the IT regime.
- General statement:
  - Macroprudential policy reduces bank systemic risk and the IT regime enhances the effectiveness of many macroprudential tools.
- Timing and macro context:
  - Pre-GFC: relatively loose macroprudential policies amid rising SRISK.
  - Post-GFC and until monetary normalization starting end-2015: tighter macroprudential policies accompanied lower SRISK (except some liquidity and supply-based tools).
  - Since monetary policy normalization from end-2015: tighter macroprudential policies accompanied tempered SRISK under IT and other regimes but rising SRISK under the ER regime.
- Monetary–macroprudential interactions:
  - Interactive terms of monetary policy stance (policy rate change) and MPI largely positive with some statistical significance, consistent with the expectation that tighter monetary policy together with tighter macroprudential policy support financial stability.

### Tool-Specific Results — Demand-Based Measures
- DSTI (limits on debt-service-to-income):
  - Stricter DSTI limits mitigate bank systemic risk only under an IT regime.
  - Interaction DSTI × IT: negative and statistically significant at the 5 percent level.
  - DSTI contemporaneous coefficient: insignificant.
- LTV (loan-to-value):
  - Tightening LTV mitigates bank systemic risk with a lag only under an IT regime.
  - Lagged interaction LTV × IT: negative and statistically significant at the 5 percent level.
  - Contemporaneous LTV coefficient: statistically positive and smaller.

### Tool-Specific Results — Capital Requirements
- Broad findings:
  - Capital requirements (Capital, CCyB, Conservation) are expected to moderate systemic risk; however, interactions with monetary policy can create trade-offs.
- Results:
  - Capital (all sectors) and Capital.HH (household-targeted) requirements are effective in attenuating systemic risk under IT regimes but not under other regimes.
  - Capital.Corp and Capital.Gen: mixed and insignificant effects under both IT and other regimes.
  - Conservation buffers: effective with a lag (significant at the 1 percent level) in attenuating systemic risk under the IT regime but not under other regimes.
  - CCyB: no pronounced effects in the sample, possibly due to limited observations of positive CCyB implementations.
  - Limits on bank leverage (LVR): estimated positive effects on systemic risk; may reflect side effects increasing banks' ability to take on debt, increasing default likelihood — effect stronger under IT than other regimes.

### Tool-Specific Results — Loan-Supply-Based Measures
- Limits on credit growth (LCG):
  - Estimates insignificant or have incorrect sign; LCG.Gen and LCG.HH do not have pronounced effects on systemic risk.
  - LCG lag (LCG t-1) marginally significant at the 10 percent level in some specifications.
- Loan restrictions (LoanR):
  - LoanR reduce systemic risk significantly, especially for households.
  - Effect is muted under IT, potentially due to circumvention/leakages and credit provision driven by policy rates under IT.
- Loan loss provisions (LLP):
  - Overall LLP requirements have insignificant effects on systemic risk; under IT there are negative contemporaneous and lagged effects.
- Loan-to-deposit ratio (LTD) limits:
  - Tightening LTD generally associated with declines in systemic risk, though not pronounced; IT regime does not significantly alter contemporaneous LTD impact.
- Limits on foreign currency lending (LFC):
  - LFC could notably reduce SRISK in open economies and more so under IT, but IT.LFC interaction excluded due to lack of observations.

### Tool-Specific Results — Liquidity Requirements and Other Supply-Based Measures
- Liquidity requirements (LCR, NSFR):
  - Tighter liquidity requirements increase systemic risk with a lag under IT relative to other regimes.
  - Lagged interaction Liquidity × IT: positive and significant at the 1 percent level.
- Reserve requirements (RR):
  - Tightened RR tend to raise systemic risk potentially via higher funding costs and lower profitability; effect mitigated under IT.
  - Lagged interaction RR × IT: negative and significant at the 5 percent level.
- Limits on foreign currency exposures (LFX):
  - Contemporaneous and lagged LFX coefficients: negative and significant — limiting foreign currency exposures lowers systemic risk.
- Tax-based measures and bank levies:
  - No pronounced short-run effects on systemic risk; could reduce systemic risk in the long run (evidence like stamp duty effects may take up to three years).

### Robustness Checks and Extensions
- Post-GFC sample (2011−2018):
  - Capital.Corp contemporaneous effect: significant and negative.
  - Capital.Gen effective in reducing systemic risk under IT but not under other regimes.
  - LLP contemporaneous term: significant and positive; under IT it is negative and significant.
  - LTD and tax help contain excessive credit and attenuate systemic risk.
- Unrestricted sample (576 banks, 60 countries, 2000–2018):
  - Most MPIs except LTD under IT are at least as effective as under other regimes.
  - Capital.Corp effective under IT but not under other regimes.
  - DSTI and LTV limits mitigate systemic risk without additional IT-specific contributions in this sample.
  - LVR, LLP, and liquidity requirements appear to help mitigate SRISK under IT in the unrestricted sample but did not in benchmark.
- AE vs EME split:
  - Effects of macroprudential policies on systemic risk tend to be more pronounced for EMEs than for AEs.
  - Exceptions: LTV, liquidity requirements, and LFC appear effective only for AEs.
- Monetary policy interaction:
  - Including interactive terms of domestic policy rate change and each MPI largely preserves benchmark qualitative results.
  - Estimated coefficients on policy rate change × MPI largely positive with some statistical significance (e.g., CCyB, LoanR, LFC, Tax).
  - Mixed or negative signs for interactions with demand-based measures and sectoral capital requirements in some cases.

### Policy Implications and Interpretation
- Synergy under IT:
  - IT regime reinforces the effectiveness of many macroprudential tools (notably DSTI, LTV, and certain capital requirements), potentially due to greater transparency, accountability, and the monetary policy focus on interest rates and funding costs.
- Coordination:
  - Findings support coordination between monetary policy and macroprudential policy to mitigate financial stability risks.
- Heterogeneity and caveats:
  - Effects differ across tools, lags, and country groups (AEs vs EMEs); some tools show counterproductive effects depending on context (e.g., LVR positive on SRISK).
  - Limited observations for certain tools (e.g., CCyB, IT.LFC) constrain inference.
- Timing and transmission:
  - Some tools show lagged effectiveness (e.g., conservation buffers, reserve requirements, taxes); others show contemporaneous or mixed effects.
  - Monetary policy normalization since end-2015 linked to instances where macroprudential tightening raised bank funding costs amid higher policy rates, contributing to SRISK dynamics.

### Conclusion
- Empirical evidence indicates macroprudential policy reduces bank systemic risk and that the inflation-targeting (IT) regime enhances the effectiveness of most macroprudential tools.
- The IT regime’s transparency, accountability, and interest-rate-focused monetary conduct likely contribute to this synergy.
- Monetary policy can reinforce macroprudential effectiveness for several tools; coordinated policy frameworks between monetary and macroprudential authorities are beneficial for financial stability.
- Further research recommended to clarify inter-linkages and transmission channels between monetary regimes and macroprudential tool effectiveness.

*International Monetary Fund — Macroprudential Policy and Bank Systemic Risk: Does Inflation Targeting Matter?*

### References

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### Appendix I. Macroprudential Policy and SRISK Metrics: IT vs. Non-IT Countries

- The appendix provides information about the SRISK measure and the macroprudential policy stance of each MPI (Figures A.1-A.4).
- Empirical observation: very high SRISK observations are mostly associated with loose macroprudential policies (without implying a causal link).
- Figures referenced:
  - Figure A.1. Synchronicity between SRISKm and Macroprudential Policy: Demand-Based Measures
  - Figure A.2. Synchronicity between SRISKm and Macroprudential Policy: Capital Requirements
  - Figure A.3. Synchronicity between SRISKm and Macroprudential Policy: Loan-Supply-Based Measures
  - Figure A.4. Synchronicity between SRISKm and Macroprudential Policy: Liquidity and Other Measures
- Notes on figures:
  - The graph shows observational synchronicity between the SRISKm (US$ bn) and each MPI.
  - Red figures correspond to IT countries, blue ones to OT (other regimes).
  - The width of the figure corresponds to the number of times the corresponding level of SRISKm at the moment when the MPI is loosened or tightened.
  - In almost all cases, higher levels of SRISKm (not controlled for bank size) are observed when the MPIs are loosened.

### Appendix II. Interpretation of the Results Related to Macroeconomic and Bank-Specific Variables

- General consistency:
  - The signs of macroeconomic variables—especially on the external front—are consistent across the 22 regression results, in line with expectations.
- External/global factors (estimated results):
  - One-period-lagged FFR: tighter U.S. monetary policy increases systemic risk; significant and positive estimates in all regressions.
    - Mechanism: reduces cross-border credit flows and increases banks’ funding costs.
  - VIX: positive and significant effect on systemic risk in all regressions.
    - Implication: U.S. stock market risk sentiment influences global financial market impacts on banks’ fragility.
  - Global GDP growth: significant and positive effects on systemic risk.
    - Interpretation: may capture rising investment sentiment and credit demand that portend credit quality deteriorations.
    - Note (footnote 11): The negative impact of global GDP growth on systemic risk reflects the "volatility paradox" (Brunnermeier and Sannikov 2014) and intertemporal trade-offs between short-term benefits and long-term risks (Adrian et al. 2022).
  - Exchange rate growth (FX): coefficient significantly negative in seven of the 22 regressions.
    - Interpretation: depreciations’ adverse impacts on banks with foreign-currency liabilities can be outweighed by favorable impacts on exporters and on bank balance sheets with foreign-currency assets (Choi and Cook 2004).
- Domestic factors:
  - Financial development: increases systemic risk (prone to abrupt capital flows in the early stage of development).
  - Capital account openness: tends to reduce systemic risk.
  - Other domestic macro-financial variables: no pronounced effects due to offsetting movements on systemic risk (footnote 12 elaborates on offsetting channels for GDP growth, savings composition, monetary policy, and CPI inflation).
- Bank-specific variables (controls in bank-level analysis):
  - Bank size: systemic risk is positively and significantly associated with bank size—reflecting systemic importance and incentives to take on more risk (e.g., Laeven et al. 2016).
  - Deposits: systemic risk declines significantly with deposits (core funding more stable than non-core funding).
  - NPL ratio: appears to have insignificant effects on bank systemic risk despite being an indicator of credit risk (e.g., Kaminsky and Reinhart 1999; Nkusu 2011).
  - Other bank-specific factors: mostly insignificant—possibly due to confluence of two offsetting effects (footnote 13).
    - Example: CAP (equity to total asset ratio) could lower insolvency risk (negative) but incentivize risk-taking (positive).
    - Example: LTA (loan-to-asset ratio) captures loan creation capacity (positive) and credit risk exposure (negative).
  - ROA (profitability): coefficient appears insignificant—possibly reflecting offsetting effects between risk-taking incentives for higher profits and profitability fostering resilience (as Meuleman and Vander Vennet (2020) also find).

*IMF Working Papers — Macroprudential Policy and Bank Systemic Risk: Does Inflation Targeting Matter?*

### Appendix III. Results of Extended Regressions

### Appendix III. Results of Extended Regressions with Interactions between Monetary Policy and Macroprudential Policy

### Overview
- Reports estimated results of extended regressions which additionally include the interactive term of the monetary policy variable (INT) and the macroprudential variable (MPI) in the benchmark regression.
- The results are discussed in section III.E.

### Estimation method
- Estimation performed using GMM (two-step).
- Dependent variable in all regressions: SRISK.
- Each column displays coefficients obtained when regressing SRISK on the corresponding MPI one by one, with the interactive term between the monetary policy variable (INT) and the macroprudential variable (MPI) included.
- Significance markers: * (10 percent), ** (5 percent), *** (1 percent).
- Ø indicates that the variable has been excluded due to the lack of observations.

### Table listings and focus
- Table A.1. Estimation Results: Demand-Based Measures Interacting with Monetary Policy
  - Presents GMM (two-step) coefficients for demand-based MPI measures interacting with monetary policy.

- Table A.2. Estimation Results: Capital Requirements Interacting with Monetary Policy (1/2)
  - First part of capital-requirements MPI measures interacting with monetary policy, showing GMM (two-step) coefficients.

- Table A.3. Estimation Results: Capital Requirements Interacting with Monetary Policy (2/2)
  - Second part of capital-requirements MPI measures interacting with monetary policy, showing GMM (two-step) coefficients.

- Table A.4. Estimation Results: Loan-Supply-Based Measures Interacting with Monetary Policy (1/3)
  - First part of loan-supply-based MPI measures interacting with monetary policy, showing GMM (two-step) coefficients.

- Table A.5. Estimation Results: Loan-Supply-Based Measures Interacting with Monetary Policy (2/3)
  - Second part of loan-supply-based MPI measures interacting with monetary policy, showing GMM (two-step) coefficients.

- Table A.6. Estimation Results: Loan-Supply-Based Measures Interacting with Monetary Policy (3/3)
  - Third part of loan-supply-based MPI measures interacting with monetary policy, showing GMM (two-step) coefficients.

- Table A.7. Estimation Results: Liquidity Requirements and Other Supply-Based Measures Interacting with Monetary Policy (2/3)
  - Presents GMM (two-step) coefficients for liquidity requirements and other supply-based MPI measures interacting with monetary policy.

### Notes on interpretation
- All tables use the same notation and estimation approach; results should be interpreted with reference to the interactive term between INT and MPI and the significance markers provided.
- Variables excluded due to lack of observations are denoted by Ø.

*Macroprudential Policy and Bank Systemic Risk: Does Inflation Targeting Matter? Working Paper No. WP/2023/119*

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