## mfpea

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---

### Executive Summary — context and motivation
- Financial sector vulnerabilities have profound macroeconomic consequences; recent examples include the 2008 global financial crisis and the 2023 failures of several banks.
- Over the past two decades, the IMF strengthened financial sector surveillance via the Financial Sector Assessment Program (FSAP) and mainstreaming of macrofinancial surveillance.
- Implementation gaps remain:
  - The 2018 Review of Program Design and Conditionality found that one in five structural conditions in IMF-supported programs targeted the financial sector.
  - The 2021 Comprehensive Surveillance Review (CSR) and the ongoing 2026 CSR emphasize better integration of financial sector issues and clearer articulation of systemic financial risk.
- Standard IMF FPP frameworks omit bank capital and income in monetary sector balance sheets, limiting analysis of interactions between banking soundness (profitability, capital adequacy) and macroeconomic variables.

### The FPP-FIN approach — core design and use cases
- FPP-FIN embeds an extended representation of the banking system—aggregate income statement, balance sheet, and key regulatory, supervisory, and bank performance indicators—into the IMF’s traditional accounting-based FPP framework.
- Core elements:
  - Use standard macroeconomic projections (growth, inflation) to estimate banking system balance sheet dynamics, profitability, and capitalization.
  - Define a “capital gap” as the difference between the actual capital ratio and a “target” capital ratio that incorporates supervisory add-ons and a management buffer.
  - As long as the capital gap remains positive, credit demand from households and firms can be met; if it turns negative, lending may be sharply restricted.
- Two complementary use cases:
  - (1) Extend traditional FPP by integrating banking system indicators (capital adequacy, asset quality, profitability) to produce forward-looking Financial Soundness Indicators (FSIs).
  - (2) Introduce explicit two-way behavioral linkages between the real economy and the financial sector—augment consumption and investment equations with real credit growth and the real lending rate—to capture macrofinancial feedback loops and a financial accelerator mechanism.

### Mechanisms, nonlinear dynamics, and financial accelerators
- Key channels captured:
  - Adverse aggregate demand shocks → higher NPLs → higher LLPs → lower profits → slower capital accumulation → deteriorating capital gap → reduced lending → amplified contraction in consumption and investment.
  - Banks typically cut lending and retain profits faster than they can raise equity during distress.
- Nonlinear dynamics:
  - Shortfall and adjusted capital gap mechanisms (parameters such as φ) generate asymmetric amplification for adverse shocks.
  - Lending rate rLt responds to policy rate, lagged credit–to–GDP gap, and capital gap; when capgapt adj − capgap < 0, lending rates may rise nonlinearly.
- Shock experiment configurations (1-percentage-point negative demand shock, calibrated in 2001):
  - Baseline: brlcapgap = 0 (no lending-rate sensitivity to capital gap).
  - Accelerator: financial accelerator active.
  - Nonlinear: nonlinear financial accelerator active (φ > 0).
- Credit boom dynamics captured via persistent positive shocks to credy with endogenous increases in systemic risk and risk weights.

### Data, operational feasibility, and practical features
- Feasibility requires the IMF’s Financial Soundness Indicators (FSIs) Database; without it, implementation is difficult.
- Framework design principles:
  - Parsimonious, fully recursive closed-economy real model organized into three blocks: (1) real economy, monetary policy, and credit demand; (2) banking system income statement, balance sheet, and metrics; (3) lending rate and Monetary Conditions Index (MCI).
  - Block 2 largely accounting identities to ease calibration; stationarity ensured by anchoring assets and retained earnings to steady-state real GDP growth.
  - Implementable in common platforms, including spreadsheets.
- Exact policy-relevant numeric references preserved:
  - Credit risk RWAs typically account for 70%–90% of total RWAs.
  - Supervisory liquidity standards: Liquidity Coverage Ratio covers 30 days; Net Stable Funding Ratio covers a one-year horizon.
  - Management buffers: in practice, individual banks typically hold an additional 1½%–2½% in capital as a management buffer.

### Pilots, applications, and selected country findings
- Pilots and TA applications: Kazakhstan (2024 Article IV pilot), Bahamas, Costa Rica, Oman, United Arab Emirates (UAE), and internal IMF training (Georgia 2023, Ghana 2023).
- Kazakhstan — baseline and scenario insights:
  - Projected banking system to remain profitable and well capitalized; capable of supporting credit demand.
  - Scenarios analyzed: financial deepening; impact of narrower interest margins on income and capital; enhanced NPL recognition.
  - Selected numeric inputs and outcomes (exact values from Kazakhstan tables):
    - Table 3 — Macroeconomic input data (percent and KZT trillion):
      - Gross domestic product growth, constant prices: 2022 = 3.2; 2023 = 5.1; 2024 = 3.9; 2025 = 5.0
      - Gross domestic product, current prices: 2022 = 103.8; 2023 = 120.6; 2024 = 133.8; 2025 = 149.9
      - Inflation: 2022 = 20.4; 2023 = 9.7; 2024 = 8.2; 2025 = 7.2
      - Unemployment rate, percent: 2022 = 4.9; 2023 = 4.7; 2024 = 4.7; 2025 = 4.6
    - Table 4 — Income statement (KZT trillion and percent indicators):
      - Net interest income (NII): 2022 = 2.1; 2023 = 2.7; 2024 = 2.7; 2025 = 3.4
      - Gross income: 2022 = 3.4; 2023 = 4.5; 2024 = 4.7; 2025 = 5.6
      - Provisions: 2022 = 0.5; 2023 = 0.5; 2024 = 0.7; 2025 = 0.9
      - Retained earnings: 2022 = 1.5; 2023 = 2.2; 2024 = 1.5; 2025 = 1.2
      - ROA (Return on (2Y) average assets): 2022 = 4.2; 2023 = 5.1; 2024 = 4.3; 2025 = 4.6
      - Interest margin to gross income: 2022 = 62; 2023 = 61; 2024 = 58; 2025 = 60
    - Table 5 — Balance sheet (KZT trillion and percent indicators):
      - Total assets: 2022 = 45.6; 2023 = 52.5; 2024 = 60.0; 2025 = 66.1
      - Loans: 2022 = 23.6; 2023 = 29.2; 2024 = 36.1; 2025 = 40.7
      - Gross loans: 2022 = 24.3; 2023 = 29.9; 2024 = 36.7; 2025 = 41.5
      - Capital and reserves: 2022 = 5.2; 2023 = 6.9; 2024 = 8.4; 2025 = 9.6
      - Loans to deposits ratio: 2022 = 73; 2023 = 81; 2024 = 84; 2025 = 86
      - Size of banking system (to GDP): 2022 = 44; 2023 = 44; 2024 = 45; 2025 = 44
    - Table 6 — Regulatory and performance indicators (percent):
      - Regulatory capital to risk-weighted assets: 2022 = 21.7; 2023 = 21.5; 2024 = 21.3; 2025 = 21.2
      - Regulatory Tier 1 capital to risk-weighted assets: 2022 = 18.6; 2023 = 19.2; 2024 = 19.4; 2025 = 19.5
      - Nonperforming loans to gross loans: 2022 = 13.4; 2023 = 2.9; 2024 = 3.0; 2025 = 3.1
      - Provisions as percent of NPL: 2022 = 76.9; 2023 = 76.0; 2024 = 76.0; 2025 = 76.0
      - ROA: 2022 = 4.2; 2023 = 5.1; 2024 = 3.8; 2025 = 4.1
      - ROE: 2022 = 33.0; 2023 = 36.5; 2024 = 25.7; 2025 = 27.6
      - Customer deposits to (non-interbank) loans: 2022 = 73; 2023 = 81; 2024 = 84; 2025 = 86
      - Credit growth, nominal: 2022 = 22; 2023 = 32; 2024 = 18; 2025 = 13
      - Credit growth, real: 2022 = 22; 2023 = 17; 2024 = 6
      - GDP, real, yoy: 2022 = 3.2; 2023 = 5.1; 2024 = 3.9; 2025 = 5.0
- TA applications (examples):
  - Bahamas, Costa Rica, Oman, UAE: enabled quantification of adverse aggregate demand and financial shocks and evaluation of selected macroprudential tools.
  - Oman application integrated banking sector and hydrocarbon sector (stabilization fund dynamics) into OCAEM.

### Quantitative scenario findings (illustrative)
- Adverse aggregate demand shock (illustrative numbers):
  - Without credit channels in behavioral equations: real GDP growth declines by 4 percentage points to −0.2 percent in the illustrative scenario.
  - With credit growth and lending rate included: additional decline of approximately 3 percentage points, pushing real GDP growth below the −3½ percent mark.
- Financial-sector-originated shock (sharp decline in bank profitability):
  - Reduces capital, tightens credit provision, and lowers growth by about 1 percentage point through suppressed consumption and investment.
- Macroprudential buffer experiment:
  - Introducing a CCyB of 50 basis points (gradually) in a few applications did not impose undue pressure on banking systems and did not materially act as a drag on growth.

### Policy relevance, operational recommendations, and extensions
- Policy relevance:
  - FPP-FIN strengthens macrofinancial analysis in IMF operational work without replacing existing models; can be selectively extended for country-specific issues.
  - Aligns with 2026 CSR priorities to better integrate financial sector policies, systemic risks, and emerging vulnerabilities into core IMF work.
- Operational recommendations:
  - Use FSIs Database as primary input; complement with supervisory or bank-level data where available.
  - Apply judgment to calibrate NPL dynamics, provisioning ratios, and management buffer targets; be transparent about judgment-intensive choices.
  - Implement conservative baseline calibrations and amplify accelerator/nonlinear channels only where country conditions warrant.
- Potential extensions:
  - Incorporate CARREG time-variation (countercyclical capital buffer experiments).
  - Add asset-price and exchange-rate channels, FX-denominated lending granularity, solvency–liquidity feedback loops, and explicit government bond holdings in bank balance sheets.
  - Integrate with semi-structural gap-trend forecasting and DSGE models for deeper embedding of financial-sector considerations.

### Limitations and caveats
- Aggregation masks cross-sectional heterogeneity and interconnectedness; FSIs aggregate may obscure institution-level dynamics.
- Framework omits liquidity risk, detailed asset-price dynamics, explicit exchange-rate valuation effects, and direct government financing by banks (extensions noted).
- Best suited for bank-dominated, domestically funded financial systems; less suited as standalone for highly market-based or internationally integrated systems without extensions for NBFIs and cross-border flows.

_Italic: Source: mfpea (mfpea.pdf)._

### Executive Summary.................................................................................................1

### Executive Summary

### Context and motivation
- Financial sector vulnerabilities can have profound macroeconomic consequences; recent examples include the 2008 global financial crisis and the 2023 failures of several banks.
- Over the past two decades, the IMF has strengthened financial sector surveillance through initiatives such as the Financial Sector Assessment Program (FSAP) and the mainstreaming of macrofinancial surveillance.
- Implementation gaps remain in consistently translating financial sector analysis into country teams’ core macroeconomic frameworks:
  - The 2018 Review of Program Design and Conditionality found that one in five structural conditions in IMF-supported programs targeted the financial sector.
  - The 2021 Comprehensive Surveillance Review (CSR) and the ongoing 2026 CSR emphasize the need for better integration of financial sector issues and clearer articulation of systemic financial risk.
- Standard IMF FPP frameworks omit bank capital and income in monetary sector balance sheets, limiting analysis of interactions between banking soundness (profitability, capital adequacy) and macroeconomic variables.

### The FPP-FIN approach
- FPP-FIN embeds an extended representation of the banking system—aggregate income statement, balance sheet, and key regulatory, supervisory, and bank performance indicators—into the IMF’s traditional accounting-based FPP framework.
- Core elements:
  - Use standard macroeconomic projections (growth, inflation) to estimate banking system balance sheet dynamics, profitability, and capitalization.
  - Define a “capital gap” as the difference between the actual capital ratio and a “target” capital ratio that incorporates supervisory add-ons and a management buffer.
  - As long as the capital gap remains positive, credit demand from households and firms can be met; if it turns negative, lending may be sharply restricted.
- Two complementary use cases:
  1. Extend traditional FPP by integrating banking system indicators (capital adequacy, asset quality, profitability) into macroeconomic projection processes to produce forward-looking Financial Soundness Indicators (FSIs).
  2. Introduce explicit two-way behavioral linkages between the real economy and the financial sector—augment consumption and investment equations with real credit growth and the real lending rate—to capture macrofinancial feedback loops and a financial accelerator mechanism.

### Mechanisms and dynamics captured
- The two-way linkage configuration can generate amplified, nonlinear, and asymmetric credit dynamics, increasing GDP growth volatility.
- Adverse aggregate demand shocks can erode capital buffers; if the capital gap turns negative, banks restrict lending, which contracts private consumption and investment and amplifies recessions.
- Bank responses to negative capital gaps: reduce lending and retain profits as quicker adjustments than raising equity or generating higher profits, which usually take time during distress.

### Data and operational feasibility
- Feasibility depends on availability of the IMF’s Financial Soundness Indicators (FSIs) Database, which summarizes capital adequacy, asset quality, and profitability; without these publicly available data, implementation for country teams would be difficult.
- FPP-FIN is deliberately parsimonious and designed to be practical, transparent, and adaptable across country contexts and modeling platforms.

### Pilots, applications, and findings
- Piloted in the 2024 Article IV Consultation for Kazakhstan (IMF 2025):
  - Produced a baseline financial stability outlook alongside standard macroeconomic projections—the first time such an integrated financial stability outlook was included in an Article IV staff report.
  - Projected that Kazakhstan’s banking system would remain profitable, well capitalized, and able to support credit demand from households and firms.
  - Supported illustrative exercises: financial deepening scenario showing further credit expansion possible without compromising stability if credit risk is well managed; analysis of credit growth consistency and impact of narrowing interest margins on income and capital accumulation; quantitative estimates of regulatory initiatives such as enhanced nonperforming loan (NPL) recognition.
- Applied in Institute for Capacity Development (ICD) technical assistance (TA) projects as extensions to existing macroeconomic frameworks:
  - Variants adapted for the Bahamas, Costa Rica, Oman, and the United Arab Emirates (UAE).
  - Enabled quantification of effects from adverse aggregate demand and financial shocks and initial evaluation of selected macroprudential tools.

### Policy relevance and implications
- FPP-FIN provides a practical way to strengthen macrofinancial analysis in IMF operational work without replacing existing models:
  - Can be selectively extended to address country-specific macrofinancial policy issues.
  - Aligns with the forthcoming 2026 Comprehensive Surveillance Review priorities to better integrate financial sector policies, systemic risks, and emerging vulnerabilities into core IMF work.
- Potential extensions:
  - Incorporation into semi-structural gap-trend forecasting models and dynamic stochastic general equilibrium (DSGE) models to further embed financial sector considerations into surveillance, lending, and capacity development.

_Executive Summary, mfpea - Executive Summary.................................................................................................1_

### Box 1. Financial Sector Assessment Program (FSAP)

### Box 1. Financial Sector Assessment Program (FSAP)

### Overview and purpose
- The Financial Sector Assessment Program (FSAP) is the IMF’s key instrument for financial surveillance.
- Established in 1999 in the aftermath of the Asian financial crisis, it began as a voluntary capacity development product and catalyst for financial sector reforms, providing independent, in-depth, and comprehensive assessment of financial systems.
- After the global financial crisis, the FSAP became a mandatory surveillance instrument for jurisdictions with systemically important financial sectors, with assessments conducted periodically.
- Assessments are conducted jointly with the World Bank in emerging market and developing economies (EMDEs) and solely by the IMF in advanced economies (AEs).
- The FSAP complements more frequent Article IV consultations with in-depth investigation of systemic vulnerabilities and policy frameworks by experts.

### Stability assessment pillars
- Stability assessments are anchored by three essential pillars:
  - (1) quantitative risk assessment, including stress testing and interconnectedness analysis;
  - (2) assessment of regulatory and supervisory frameworks based on international standards; and
  - (3) evaluation of financial safety nets and crisis management arrangements.
- By combining quantitative systemic risk analysis with institutional assessments, FSAPs inform surveillance, capacity development, and policy dialogue with national authorities.

### Evolving scope and coverage
- The scope of FSAP has changed as the financial system landscape evolved with emerging risks from nonbank financial intermediation (NBFIs), digitalization, and climate change.
- In addition to banks, assessments increasingly cover NBFIs, cross-sectoral linkages in system-wide analyses, and emerging risks related to climate change, digitalization, and cybersecurity.
- This broader scope strengthens the FSAP’s ability to assess interconnected risks and potential spillovers that could amplify shocks across institutions, markets, and borders.

### Risk assessment focus and tools
- The risk assessment pillar relies on stress testing and other quantitative tools to gauge the resilience of the financial system under adverse scenarios.
- It focuses on systemic vulnerabilities, cross-sectoral interlinkages, and potential domestic and cross-border spillovers that could amplify shocks and threaten financial stability.
- The analysis typically examines bank solvency and liquidity under adverse macroeconomic scenarios.
- Recent FSAPs increasingly extend this work to NBFIs—including investment funds, insurers, and pension funds—and often incorporate system-wide analysis of interconnectedness among banks, NBFIs, and other relevant nonfinancial sectors.
- FSAPs may also include microdata-based vulnerability analysis of the corporate and household sectors, sometimes linked to the assessment of macroprudential measures.

### Relationship with FPP-FIN
- The FPP-FIN approach differs from the FSAP in both scope and purpose.
- Whereas FSAPs provide detailed diagnostic assessments of systemic risks, vulnerabilities, and policy frameworks, FPP-FIN offers a more aggregate and tractable representation of the financial sector within a macroeconomic framework.
- FPP-FIN embeds key banking system indicators to capture major macrofinancial linkages and feedback effects.
- The two approaches are therefore complementary: FSAPs provide diagnostic depth, whereas FPP-FIN helps integrate selected financial sector insights into macroeconomic analysis and policy discussions.

*Contributor: Heedon Kang.*

*IMF DEPARTMENTAL PAPERS  •    Macro-Financial Programming5*

### Box 4. An Open-Economy Structural Banking Model

### Box 4. An Open-Economy Structural Banking Model

### Model overview
- This box summarizes the model developed by Elekdag and others (2026), which synthesizes the literature reviewed in the paper and articulates the mechanisms that inform the judgment and satellite equations used in the practical applications.
- The small open-economy New Keynesian model by Elekdag and others (2026) features five domestic agents: households, final goods producers, entrepreneurial firms, banks, and the government.
- Although macrofinancial linkages exist across all sectors, firms and banks are central to the model’s financial amplification mechanisms and represent its novel dimensions.

### Key Macrofinancial Linkages
- Household deposits are intermediated by banks to finance corporate investment.
- A firm’s borrowing cost depends on its leverage: the more indebted it is, the higher the probability of default, and thus the higher its external finance premium.
- In aggregate, higher corporate leverage translates into elevated corporate risk, as reflected in a rising default rate.
- A bank’s funding cost depends on the strength of its balance sheet. A lower capitalization ratio (that is, less bank equity) signals higher risk and raises the probability of bank default, which we refer to as systemic financial risk.

### Mutually Reinforcing Financial Accelerators
- The model features multiple interacting financial accelerators, amplifying the effects of economic shocks:
  - An adverse shock to the economy which results in a fall in asset prices would reduce corporate net worth, increase leverage, and raise firms’ probability of default. As a result, borrowing costs rise, investment falls, and the initial downturn is amplified—this is the classic financial accelerator of Bernanke, Gertler, and Gilchrist (1999).
  - At the same time, higher corporate defaults erode bank profits and deplete capital, reducing the capitalization ratio and increasing the probability of bank default (that is, systemic risk). Banks respond by raising lending rates to rebuild capital, further increasing corporate borrowing costs. This feedback intensifies the downturn and links corporate risk to systemic risk, reinforcing the financial accelerator. This is a novel feature of the model: Bank and corporate default are inter-dependent and therefore reinforce one another directly and indirectly (through endogenous dynamics), introducing an important channel of shock amplification.
  - Firms face a collateral constraint: They can borrow only up to the value of their assets, as governed by a loan-to-value (LTV) ratio. A decline in asset prices reduces collateral value, tightening the borrowing constraint. Even if firms are willing to borrow at higher rates, they may be unable to, forcing cuts to investment and deepening the contraction—this is the financial accelerator introduced by Kiyotaki and Moore (1997), later popularized by Iacoviello (2015).
  - A similar constraint applies to banks through a capital adequacy ratio (CAR): Banks must hold a minimum level of capital relative to their risk-weighted assets (RWAs). An erosion in capital forces banks to contract credit, reinforcing the downturn.
- Second, in fact, as embodied in FPP-FIN, banks create deposits when they create loans. They are constrained not by the stock of reserves available, as these are provided by the Central Bank on demand, given the policy interest rate, but by their stock of capital, given capital adequacy requirements and their risk appetite.

### Policy challenge and implications
- The key policy challenge is to set CAR and LTV to balance the trade-off between:
  - (1) supporting economic growth, and
  - (2) containing the buildup of corporate and systemic financial risks associated with the borrowing needed to finance investment.
- This trade-off underscores the importance of macroprudential regulation within a structural framework that included multiple macrofinancial linkages across the real and financial sectors.

*Box 4. An Open-Economy Structural Banking Model*

### 3. The FPP-FIN Approach

### 3. The FPP-FIN Approach

### A. Overview of a Stylized Banking System
- Framework purpose
  - Embed a richer financial sector within existing IMF macroeconomic frameworks by appending a modular macrofinancial extension.
  - Represent the financial sector by the aggregate banking system’s income statement, balance sheet, and key banking indicators to project capitalization, asset quality, and profitability conditional on country-specific macroeconomic forecasts, leveraging the IMF’s FSIs.
- Income statement key elements
  - Net interest income (NII): interest income on interest-earning assets minus interest expense on interest-bearing liabilities; driven by lending and deposit rates, loan book and funding composition, maturity and repricing structure, credit risk, funding mix, portfolio size, and slope of the yield curve. Pass-through from monetary and financial conditions is gradual and state dependent.
  - Net noninterest income (NNII): fee and commission income and other revenues not directly related to intermediation (payment and transaction fees, asset management and underwriting income, trading revenues); typically projected to evolve broadly in line with economic activity.
  - Loan loss provisions (LLPs): expenses for expected credit losses; provisioning closely linked to NPL ratio; higher NPLs lead to increased provisions, reducing profits and capital accumulation.
  - Dividends and retained earnings: dividend payout ratio (DPR) may be constrained by regulation; retained earnings accumulate to strengthen bank capital.
- Balance sheet simplifications
  - Liabilities often aggregated into capital and “other liabilities” due to limited cross-country funding detail.
  - Assets aggregated with loans and “other assets” (including government securities); total assets often assumed to evolve broadly in line with nominal GDP growth for tractability.
- Selected regulatory, supervisory, and bank performance metrics
  - Capital adequacy: regulatory capital divided by risk-weighted assets (RWA) yields risk-based capital ratios (capital adequacy ratio, Tier 1 ratio, Core Tier 1, Common Equity Tier 1). Basel III emphasizes common equity (CET1).
  - Asset quality: NPL ratio and provisions-to-NPLs (coverage ratio). NPLs that are unresolved encumber regulatory capital.
  - Profitability: ROA and ROE (net income divided by total assets and equity); net interest margin (NIM); noninterest expenses to gross income (cost efficiency).
  - Liquidity and funding: liquid assets to short-term liabilities; loan-to-deposit ratio to assess funding resilience.
- Stylized FSIs table (selected indicators listed)
  - Capital Adequacy: Regulatory capital to risk-weighted assets; Tier 1 capital to risk-weighted assets; Tier 1 capital to assets.
  - Asset quality: NPLs to total gross loans; Provisions to NPLs; NPLs to capital.
  - Earnings and Profitability: ROA; ROE; Interest margin to gross income; Noninterest expenses to gross income.
  - Liquidity: Liquid assets to total assets; Liquid assets to short-term liabilities.
  - Sensitivity to Market Risk: Net open position in foreign exchange to capital.
- Exact numeric references preserved from text
  - Credit risk RWAs typically account for 70%–90% of total RWAs.
  - Supervisory liquidity standards: Liquidity Coverage Ratio covers 30 days; Net Stable Funding Ratio covers a one-year horizon.

### B. FPP-FIN: Conceptual Model (Illustrative)
- Design principles
  - Parsimonious, fully recursive closed-economy real model that abstracts from fiscal policy and nominal dynamics (no role for monetary policy in the structural core).
  - Organized into three blocks: (1) real economy, monetary policy, and credit demand; (2) banking system income statement, balance sheet, and metrics; (3) lending rate and monetary conditions index (MCI).
  - Implementable in a wide array of packages, including spreadsheet-based software.
- Block 1: Real economy, monetary policy, and credit demand
  - Four equations summarized:
    - Aggregate demand: output gap (ŷt) depends on its lag, lagged MCI, and exogenous demand shock (εt y); persistence parameters denoted by ρ (e.g., ρy).
    - Output gap to real GDP growth (gt) which converges to steady-state value (ḡ).
    - Passive monetary policy rule: real policy rate (rt) reverts to natural rate (r̄) with persistence ρr.
    - Credit demand: credit–to–GDP ratio (credyt) depends on contemporaneous output gap, lagged lending-rate gap, its own persistence, converging to steady-state credy. First three equations in percentage points; credit–to–GDP ratio measured in percent of GDP.
- Block 2: Banking system balance sheet, income statement, and key metrics
  - Core features and equations (references preserved): equations (5)–(11) represent asset growth, NPL dynamics, LLPs, NNII (Othert), profitability Πt, retained earnings (RE), and capital accumulation; DPR denotes dividend payout ratio.
  - Key summarized relationships:
    - Real bank assets grow at same constant rate as real GDP along balanced-growth path (equation 5); shock terms available to capture judgment or nonmodeled dynamics.
    - NPL ratio evolves per equation (6) depending on steady-state npl, persistence, output gap, and lending-rate conditions; LLPs computed as fixed fraction of lagged assets scaled by contemporaneous NPL ratio (equation 7).
    - NNII captured by Othert scaled to banking system size.
    - Profits Πt evolve per equation (9), driven by NII (interest spread on existing asset stock), offset by provisioning costs and supplemented by NNII.
    - Retained earnings and capital accumulation follow accounting identities (equations 10 and 11).
  - Capital gap concept
    - Capital gap capgapt defined as difference between actual regulatory capital (bank capital relative to RWAs, with ω denoting average risk weight) and minimum regulatory capital requirement (CARREG) (equation 12).
    - Management buffers above minimum regulatory requirements are modeled; example: if minimum is 10 percent and management targets 11 percent, management buffer is 1 percentage point; in practice, individual banks typically hold an additional 1½%–2½% in capital as a management buffer.
    - Capital-conserving behavior: banks may cut lending to reduce RWA to quickly strengthen capital ratios.
- Nonlinear dynamics within Block 2
  - Shortfall and adjusted capital gap (equations 13 and 14):
    - shortfallt captures erosion of management buffer when capgapt < capgap (target buffer); size equals distance between target buffer and actual capital gap.
    - capgapt adj corrects raw capital gap for any active shortfall; parameter φ controls strength of nonlinear amplification once a shortfall materializes.
- Block 3: Lending rate and Monetary Conditions Index
  - Equation (15): lending rate rLt depends on policy rate, lagged credit–to–GDP gap, and difference between capital gap and steady-state value; captures reconciliation of credit demand pressures and credit supply conditions.
    - When capgapt adj − capgap < 0, lending rate rises, possibly nonlinearly, capturing capital crunch dynamics.
    - Note: r̄L = r̄ + η.
  - Equation (16): MCI defined as weighted average of policy-rate and lending-rate gaps; broader financial conditions index can be considered.
  - Pass-through caveats: lending rate interpreted as average lending yield; policy rate proxies funding conditions; pass-through may be gradual due to funding spreads and repricing lags.

### C. Nonlinear Dynamics and Shock Scenarios
- Shock experiments summarized in text
  - Three configurations for impulse responses to a 1-percentage-point negative adverse aggregate demand shock to the output gap (calibrated in 2001):
    - Baseline: lending-rate sensitivity to capital gap shut down (brlcapgap = 0).
    - Accelerator: financial accelerator active (lending rate responds to capital gap).
    - Nonlinear: nonlinear financial accelerator active (φ > 0).
  - Mechanism of adverse demand shock:
    - Output gap falls on impact → NPL ratio increases → LLPs rise → profits fall → capital accumulation slows → capital gap deteriorates.
    - In Accelerator case: banks raise lending rate to rebuild capital buffers → MCI rises → drag on recovery → reinforces downturn through financial accelerator.
    - In Nonlinear case: lending rate rises more markedly once shortfall mechanism activates; response is asymmetric (nonlinear amplification occurs for adverse shocks, not comparable positive shocks).
- Credit boom extension
  - Model can capture a credit boom by allowing credy to evolve over time and be subject to persistent positive shocks; credit-driven expansion can lower lending rates and raise credit demand but may increase systemic risk and endogenous credit risk (e.g., higher risk weights if lending to riskier borrowers).

### D. Model Properties and Implementation Notes
- Practical features
  - Fully recursive structure suitable for scenario analysis and forecasting; implementable in standard statistical, numerical, and spreadsheet environments.
  - Block 2 largely accounting identities, simplifying calibration; LLP parameter (equation 7) can be anchored to observed loan loss provisioning ratio.
  - Equation (6) (NPL dynamics) requires behavioral calibration via regression analysis, credit risk models, or IMF Global Stress Test benchmarks.
  - Stationarity: assets and retained earnings grow with steady-state real GDP, while rates and ratios (including capital gap) are stationary around steady-state values—avoids spurious drift in bank capitalization.
  - Financial accelerator timing: tightening in monetary conditions feeds back into real economy with a one-period lag, ensuring full recursiveness and attenuating contemporaneous accelerator strength relative to fully forward-looking frameworks.
  - Extensibility: minimum regulatory capital requirement (CARREG) can be made time-varying to assess macroprudential policy experiments (for example, countercyclical capital buffer).
- Calibration and conservatism
  - No single parameterization fits all countries; existing macro frameworks inform Blocks 1 and 3; Block 2 anchored by accounting identities and observed provisioning ratios.
  - Calibration presented as deliberately conservative; financial accelerator and nonlinear channels can be amplified where country conditions warrant.
  - Reference benchmarks: IMF’s Global Stress Test and other empirical literature for parameter ranges and steady-state calibration.

*Italic: Source — Chapter 3, "The FPP-FIN Approach," mfpea - 3. The FPP-FIN Approach (mfpea.pdf).*

### 1. Output Gap

### 1. Output Gap

### Key charts and measures
- Charts shown:
  - Output Gap (Percent) with series: Steady state; Baseline; Accelerator; Nonlinear. Vertical axis ticks: −1.20, −1.00, −0.80, −0.60, −0.40, −0.20, 0.00, 0.20. Horizontal axis: 2000 02 04 06 08 10 12.
  - Capital Gap (Percent). Vertical axis ticks: 0.00, 0.20, 0.40, 0.60, 0.80, 1.00, 1.20. Horizontal axis: 2000 02 04 06 08 10 12.
  - Lending Rate (Percent). Vertical axis ticks: 3.80, 4.00, 4.20, 4.40, 4.60. Horizontal axis: 2000 02 04 06 08 10 12.
- Note: 
  - The output gap and capital gap are expressed as percent deviations from their respective steady states.
  - The real lending rate is measured in percentage points.
  - “Baseline” and “Accelerator” refer, respectively, to the linear version of the model without and with an operational financial accelerator mechanism.
  - “Nonlinear” refers to the nonlinear specification of the financial accelerator model.

### Limitations and potential extensions (summary)
- General framing:
  - The framework is deliberately streamlined and not intended as a unified macrofinancial model; it focuses on two-way, and possibly nonlinear, feedback between macroeconomic conditions, banking system soundness, financial conditions (particularly lending rates), the provision of credit, and the financial sector’s ability to support sustainable growth.

- Specific limitations and possible extensions:
  - First: Purpose and scope
    - Not a comprehensive macrofinancial model; pragmatic, streamlined framework for selective extension to salient policy issues at the macro–finance intersection.
  - Second: Aggregation
    - Banking system represented in aggregate, which may mask vulnerabilities from cross-sectional interconnectedness and heterogeneity (capitalization, asset quality, profitability, funding structures).
    - Aggregated decomposition of household and corporate balance sheets is not explicitly incorporated.
  - Third: Cross-country applicability
    - Benchmark FPP-FIN better suited for emerging market, frontier, and low-income countries with bank-dominated financial systems where banks are domestically funded and hold substantial domestic assets.
    - Less suited as standalone framework for highly market-based or internationally integrated financial systems; could be extended to capture selected nonbank financial intermediaries (NBFIs) such as finance companies, mortgage and consumer credit providers, and similar institutions (including Fintechs with comparable business models).
  - Fourth: Asset prices and exchange rates
    - Does not explicitly model asset prices and associated collateral effects; reconciling high-frequency asset price movements with lower-frequency macro developments is challenging.
    - Persistent asset price trends can be incorporated indirectly as determinants of NPLs.
    - Exchange rate dynamics and interaction with balance sheets are not included in the current FPP-FIN extension (though part of the underlying macroeconomic framework); greater granularity on FX-denominated lending would improve risk assessment.
    - Valuation effects on government bond holdings and associated interest rate risk, particularly in the bank–sovereign nexus, are not modeled.
    - Liquidity risk is omitted; a natural extension would incorporate solvency–liquidity feedback loops, for example by linking bank funding costs to capital adequacy or relaxing the assumption that the real policy rate fully captures bank funding costs.
  - Fifth: Fiscal sector financing links
    - Explicit government financing by the financial sector is omitted.
    - Extension: expand aggregate banking-system balance sheet with government bond holdings that react to valuation changes (for example, set by a debt accumulation identity times a price function of the policy rate plus a sovereign premium), creating additional shock transmission to banks’ balance sheets and impacts on private credit and output through capital gap effects.
  - Sixth: International capital flows
    - International capital flows vis-à-vis the financial sector are implicitly treated and should be monitored in macroeconomic calibration to ensure realistic forecasts given flow and stock implications for the balance of payments.
    - Lending rates affect credit dynamics and monetary conditions; accommodation in the financial account for net financial sector flows is required to be consistent with FPP-FIN calibrated private credit dynamics affecting banks’ net foreign assets or central bank net foreign assets.
    - Changes in interest rates that affect domestic saving through private consumption and investment will alter other components of the current account balance.
  - Seventh: Systemic externalities and risk dynamics
    - Lending spread responds to balance sheet fundamentals (capitalization) but framework does not explicitly incorporate independent shifts in risk appetite or nonlinear amplification mechanisms (leverage, fire sales, network spillovers) that compress spreads during expansions and cause abrupt repricing in downturns.
  - Additional notes:
    - Several limitations are addressed in the DSGE framework developed by Elekdag and others (2026) and other cited studies; these, combined with tools noted in Box 3, can guide country-specific applications calibrated to data availability and policy needs and complemented by expert judgment.
    - The framework can be implemented at different levels of complexity depending on country needs; two country use cases illustrate simpler FSI-projection extensions relying on expert judgment and a second approach incorporating explicit real–financial feedback.

### Box 5 — The banking system in traditional financial programming (condensed)
- Historical assumptions in traditional FPP:
  - Central bank conducts monetary policy by controlling base money and required reserves determine deposits via a money multiplier; central bank sets base money and required reserve ratio to target broad money consistent with inflation and growth.
  - Banks require deposits to make loans; quantity of deposits determines lending space, creating a mechanical fiscal crowding-out channel where more government credit implies less private credit.
- FPP-FIN refinements:
  - First: Central banks now typically use an interest rate as the operational target rather than a money quantity; base money and reserves do not constrain deposits and bank lending scale in practice.
  - Second: Banks create deposits when they extend loans; system-wide deposits are created endogenously through credit expansion, constrained by capital, regulatory requirements, balance sheet health, and risk appetite—not by reserves.
  - Third: Fiscal crowding out operates primarily through macroeconomic channels (demand, interest rate reaction, monetary policy) rather than being mechanically anchored in the banking-system balance sheet. Bank credit is demand-determined unless banks’ lending space is exhausted while complying with regulatory and accounting requirements.
- Operational outcome:
  - FPP-FIN provides a more operational approach to forecasting private-sector credit growth by emphasizing credit supply–demand interplay and better integrating macrofinancial linkages into the traditional framework.

### Box 6 — Financial Soundness Indicators (FSIs) Database (condensed)
- Role and importance:
  - The IMF’s FSIs database is a key input for financial sector forecasts and macrofinancial analysis, supporting surveillance activities including Article IV reports, Global Financial Stability Reports, and FSAPs.
  - The FSI database covers most IMF member countries and provides aggregated data on banking sector income statements, balance sheets, and other bank indicators.
- Practical considerations:
  - The database’s harmonized indicators facilitate consistent cross-country analysis and are essential for implementing FPP-FIN; assembling the required data without it would be prohibitive or infeasible given data confidentiality.
  - Aggregation can obscure dynamics at the individual institution level; systemic risk from common exposures and interconnectedness is better captured through granular bank-level data and macroprudential stress testing.
  - Differences between FSIs and IMF’s International Financial Statistics (IFS) monetary survey can arise from coverage gaps and prudential consolidation; discrepancies are generally reconcilable via scaling adjustments.

*Source: Authors’ calculations.*

### 4. FPP-FIN: Country Applications

### 4. FPP-FIN: Country Applications

### Overview
- Presents two main FPP-FIN use cases:
  - An extension of a traditional accounting-based macroeconomic framework with FPP-FIN applied to Kazakhstan, placing a central role on expert judgment.
  - A richer structure with explicit two-way feedback between the real and financial sectors, drawing on TA projects.

### A. FPP-FIN: Application to Kazakhstan

#### Purpose and policy questions addressed
- How would the banking system perform under the baseline macroeconomic forecast?
- Can the banking system supply the credit demanded by the real economy while maintaining adequate buffers?
- Can banks collectively support further financial deepening?
- Can the banking system manage accelerated NPL recognition, provisioning, and write-offs?

#### Framework in practice
- Macroeconomic inputs and data:
  - Key inputs include desk projections of nominal and real GDP and prices (Table 3).
  - Credit demand is assumed to grow in line with GDP, except under the financial deepening scenario.
  - Banking sector data are sourced from the FSI database.
- Income statement forecasting:
  - Net interest income (NII) computed from past effective funding and lending rates: NII_t = i_t^L IBA_t − i_t^F FV_t.
  - Interest margin (relative to gross income) close to 65 percent due to high lending–funding spreads in Kazakhstan.
  - NPL ratios modeled as: npl_t = 0.8 npl_{t−1} − 0.3 y_t. LLP rate γ_LLP assumed unchanged at 76 percent with LLP_t = γ_LLP npl_t.
  - Noninterest income and expenses assumed to grow proportionally to the balance sheet size. Income tax rate set at 14 percent and DPR at 25 percent.
  - Retained earnings (RE) computed after forecasting NII, NNII, LLPs, income tax, and dividend payout.
- Balance sheet forecasting:
  - Loan changes drive aggregate balance sheet evolution; currency and deposits, nonfinancial assets, and other assets assumed constant; debt instrument holdings change in line with nominal GDP growth.
  - All new lending assumed funded by deposits for simplicity.
- Capital ratio and credit provision constraint:
  - Tier 1 capital updated by adding RE to previous period capital; total assets converted into RWA using a constant RWA density.
  - Maximum lending possible given the capital gap (capgap_t), average risk weight (ω), and minimum regulatory capital requirement (CAR_REG) acts as a state-dependent constraint on credit supply; negative capital gap can lead to a “credit crunch.”

#### Key takeaways (Kazakhstan application)
- The FPP-FIN application enhanced macrofinancial coverage in Kazakhstan.
- Banking system resilience and ability to meet credit demand:
  - ROA expected to stay at around 4 percent.
  - NPL ratio projected to stabilize at 3 percent.
  - Credit growth forecast to moderate from its post-COVID peak of 32 percent to about 15 percent annually.
  - Current credit penetration about 20 percent of GDP.
- FPP-FIN supported scenario analysis including financial deepening, effects of increased bank competition on interest margins, and quantitative effects of greater NPL recognition on profitability, capitalization, and credit supply.

#### Selected numeric inputs and outcomes (from Kazakhstan tables)
- Table 3 — Macroeconomic input data (percent and KZT trillion):
  - Gross domestic product growth, constant prices: 2022 = 3.2; 2023 = 5.1; 2024 = 3.9; 2025 = 5.0
  - Gross domestic product, current prices: 2022 = 103.8; 2023 = 120.6; 2024 = 133.8; 2025 = 149.9
  - Inflation: 2022 = 20.4; 2023 = 9.7; 2024 = 8.2; 2025 = 7.2
  - Unemployment rate, percent: 2022 = 4.9; 2023 = 4.7; 2024 = 4.7; 2025 = 4.6
- Table 4 — Income statement (KZT trillion and percent indicators):
  - Net interest income (NII): 2022 = 2.1; 2023 = 2.7; 2024 = 2.7; 2025 = 3.4
  - Gross income: 2022 = 3.4; 2023 = 4.5; 2024 = 4.7; 2025 = 5.6
  - Provisions: 2022 = 0.5; 2023 = 0.5; 2024 = 0.7; 2025 = 0.9
  - Retained earnings: 2022 = 1.5; 2023 = 2.2; 2024 = 1.5; 2025 = 1.2
  - ROA (Return on (2Y) average assets): 2022 = 4.2; 2023 = 5.1; 2024 = 4.3; 2025 = 4.6
  - Interest margin to gross income: 2022 = 62; 2023 = 61; 2024 = 58; 2025 = 60
- Table 5 — Balance sheet (KZT trillion and percent indicators):
  - Total assets: 2022 = 45.6; 2023 = 52.5; 2024 = 60.0; 2025 = 66.1
  - Loans: 2022 = 23.6; 2023 = 29.2; 2024 = 36.1; 2025 = 40.7
  - Gross loans: 2022 = 24.3; 2023 = 29.9; 2024 = 36.7; 2025 = 41.5
  - Capital and reserves: 2022 = 5.2; 2023 = 6.9; 2024 = 8.4; 2025 = 9.6
  - Loans to deposits ratio: 2022 = 73; 2023 = 81; 2024 = 84; 2025 = 86
  - Size of banking system (to GDP): 2022 = 44; 2023 = 44; 2024 = 45; 2025 = 44
- Table 6 — Regulatory and performance indicators (percent):
  - Regulatory capital to risk-weighted assets: 2022 = 21.7; 2023 = 21.5; 2024 = 21.3; 2025 = 21.2
  - Regulatory Tier 1 capital to risk-weighted assets: 2022 = 18.6; 2023 = 19.2; 2024 = 19.4; 2025 = 19.5
  - Nonperforming loans to gross loans: 2022 = 13.4; 2023 = 2.9; 2024 = 3.0; 2025 = 3.1
  - Provisions as percent of NPL: 2022 = 76.9; 2023 = 76.0; 2024 = 76.0; 2025 = 76.0
  - ROA: 2022 = 4.2; 2023 = 5.1; 2024 = 3.8; 2025 = 4.1
  - ROE: 2022 = 33.0; 2023 = 36.5; 2024 = 25.7; 2025 = 27.6
  - Customer deposits to (non-interbank) loans: 2022 = 73; 2023 = 81; 2024 = 84; 2025 = 86
  - Credit growth, nominal: 2022 = 22; 2023 = 32; 2024 = 18; 2025 = 13
  - Credit growth, real: 2022 = 22; 2023 = 17; 2024 = 6
  - GDP, real, yoy: 2022 = 3.2; 2023 = 5.1; 2024 = 3.9; 2025 = 5.0

#### Role of judgment
- Judgment complements formal analysis to interpret model outputs, incorporate institutional knowledge, and assess nonlinear/state-dependent interactions.
- Calibrated rules of thumb (e.g., GDP growth → NPLs) are indicative and must be accompanied by judgment.
- Data limitations (short time series, backward-looking FSIs) constrain purely model-based approaches; institutional judgment helps fill gaps.
- Emergence of a sizable credit gap may warrant reconsideration of exogenous macro projections; transparency and internal consistency of judgment-intensive scenarios are imperative.

### B. FPP-FIN: Modeling Two-Way Feedback

#### Purpose and value added
- TA projects integrated custom-tailored banking sectors into country macro frameworks with explicit two-way feedback between banking system and real economy via:
  - Credit channel (loan volumes).
  - Market interest rate channel (cost of bank credit).
- Enables amplification via financial accelerator effects; reduction in credit provision can exert a drag on economic growth.

#### Framework in practice (TA applications)
- Income statement: NII, income, and expense components forecast separately; LLPs determined by NPL ratio from credit risk model; noninterest income typically projected to increase in line with inflation.
- Balance sheet: In some net oil-exporting cases, credit to the oil sector excluded from banking sector projections; funding mix calibrated to the previous three years’ average.
- Capital ratios: Target capital ratio includes country-specific regulation and macroprudential buffers and an add-on to reflect market expectations (usually approximately 2 percentage points).
- Explicit two-way feedback:
  - Behavioral consumption and investment equations extended to include real credit growth and the real lending rate (rather than a generic real interest rate).
  - Credit growth can be state-dependent (e.g., depend on the capital gap), allowing price (lending rate) and quantity (credit growth) dimensions of credit conditions to jointly affect aggregate demand.

#### Key takeaways (TA projects)
- Baseline projections: real GDP growth converges over the medium term to potential growth estimates; banking system capital ratio projected to remain above target, enabling provision of credit demanded by households and firms.
- FPP-FIN used to design illustrative scenarios demonstrating amplified, nonlinear, and asymmetric credit dynamics.

#### Illustrative scenarios and quantitative findings
- Adverse aggregate demand shock:
  - Without credit channels in behavioral equations: real GDP growth declines by 4 percentage points to −0.2 percent in the illustrative scenario.
  - With credit growth and lending rate included: additional decline of approximately 3 percentage points, pushing real GDP growth below the −3½ percent mark.
- Financial-sector-originated shock (sharp decline in bank profitability):
  - Shock reduces capital, tightens credit provision, and lowers growth by about 1 percentage point through suppressed consumption and investment.
- Macroprudential buffer scenario:
  - Introducing a CCyB of 50 basis points (gradually) in a few applications did not impose undue pressure on banking systems and did not materially act as a drag on growth.

*IMF Departmental Paper — “4. FPP-FIN: Country Applications”*

### 1. Demand Shock

### 1. Demand Shock

### Summary findings and framework features
- Introduces FPP-FIN as an approach for embedding the financial sector within the IMF’s accounting-based macroeconomic frameworks, with two main use cases:
  - Extending the traditional “Financial Programming and Policies (FPP)” framework.
  - Incorporating explicit two-way feedback loops between real economic activity and the financial sector to capture financial accelerator effects.
- Piloted in IMF surveillance and technical assistance (TA) settings with country cases including the Bahamas, Costa Rica, Kazakhstan, Oman, and the United Arab Emirates.
- Designed to be practical, flexible, and country-specific, relying on publicly available data and amenable to integration into broader macroeconomic models, including semi-structural gap-trend models, quarterly projection models, and dynamic stochastic general equilibrium models.
- Operational challenge highlighted: moving from improved financial sector analysis toward consistent, broad-based integration of financial sector dynamics within core macroeconomic frameworks.

### Box 7 — Incorporating Macrofinancial Linkages: The Case of Oman
- Project context:
  - Integration of a granular banking sector module and a detailed hydrocarbon sector into the Central Bank of Oman’s (CBO) Oman Comprehensive Adaptive Expectations Model (OCAEM).
  - Extended framework embeds fiscal policy considerations, including an explicit role for the sovereign wealth fund.
- Key macrofinancial linkages modeled:
  - Credit channel: new loan issuance linked to bank capital.
  - Market interest rate channel: credit spreads tied to nonperforming loans (NPLs).
  - Real private consumption driven by new (real) loans and a real interest rate gap.
  - Credit interest rate includes a risk-sensitive credit spread that increases with higher nonperforming loan levels.
  - Credit growth depends on bank capital dynamics and real oil price deviations from trend.
- Policy and analytical applications:
  - Used to explore policy-relevant scenarios guided by expert judgment and country-specific circumstances.
  - Assesses implications of oil price shocks with macrofinancial linkages.
  - Can analyze interplay between banking system soundness, credit conditions, nonhydrocarbon economic growth, and the potential role for macroprudential measures.
  - Allows analysis of interactions between fiscal policy and transfers from stabilization funds under adverse shocks.
- Contributors: Mumtaz Hussain and Yurii Sholomytskyi.
- Reference notes: See Hussain and Sholomytskyi (forthcoming) and Sholomytskyi, Butler-Blondel, and Hussain (2026).

### Box Figure 7.1 — Illustrative simulations (variables and horizons)
- Simulated variables shown across 2023–2030:
  - 1. GDP Growth (Percent) — axis ticks include —6, —4, —2, 0, 2, 4, 6, 8, 10, 12, 14; years 2023 24 25 26 27 28 29 30.
  - 2. Consumer Inflation (Percent) — axis ticks include 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5; years 2023 24 25 26 27 28 29 30.
  - 3. Hydrocarbon GDP (Percent) — axis ticks include —20, —10, 0, 10, 20, 30, 40; years 2023 24 25 26 27 28 29 30.
  - 4. Nonhydrocarbon GDP (Percent) — axis ticks include 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5; years 2023 24 25 26 27 28 29 30.
  - 5. New Real Loans Growth (Percent) — axis ticks include —5, 0, 5, 10, 15, 20, 25; years 2023 24 25 26 27 28 29 30.
  - 6. NPL ratio (Percent) — axis ticks include 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5; years 2023 24 25 26 27 28 29 30.
- Scenario labels used in simulations: Baseline; Lower oil price; Drop in oil GDP; Gov. Exp. increase.

### Box 8 — Other TA applications of FPP-FIN (high-level takeaways)
- The Bahamas:
  - Integrated a domestic banking sector into the central bank’s macro framework.
  - Identified macrofinancial transmission channels affecting banks’ capacity to provide credit and generated consistent macrofinancial forecasts.
  - Raised awareness of financial sector effects among macro forecasters and banking supervisors.
- Costa Rica:
  - Custom tailoring enabled by more granular supervisory data.
  - Provided a more detailed and accurate representation of financial stability risks and improved reliability across banks.
- United Arab Emirates (UAE):
  - Early TA application integrating a customized banking sector module into the CBUAE’s macro framework.
  - Incorporated two-way feedback between the real sector and the banking system to enhance analysis of macrofinancial linkages, including interactions among banking soundness, credit dynamics, and real GDP growth.
- Source for Box 8: Authors.

### Box 9 — FPP-FIN in IMF internal training courses (case study highlights)
- Augmented FPP courses with an FPP-FIN banking sector module to:
  - Examine macrofinancial linkages.
  - Assess banking system performance under baseline and alternative macroeconomic scenarios.
  - Test whether projected growth can be funded domestically through the banking system and how banking constraints feed back into credit supply and the real economy.
- Georgia 2023 case study:
  - Uses a balance sheet–based framework linking macro projections to Financial Soundness Indicators (FSIs).
  - Evaluates banking sector outcomes conditional on projected credit demand, profitability, asset quality, capital adequacy, and funding conditions.
  - Focuses on whether the banking system can accommodate projected private sector credit growth without eroding capital buffers.
- Ghana 2023 case study:
  - Starts from heightened fragility with macrofinancial stress already reflected in banking sector balance sheets.
  - Assesses whether the banking system is adequately capitalized and how asset-quality pressures interact with credit growth.
  - Treats capital adequacy as a potentially binding constraint on lending and analyzes NPL dynamics as an active transmission channel.
- Contributors to training course materials: Mumtaz Hussain and Emanuel Kopp.

### Conclusion — benefits, limitations, and future directions
- Benefits and practicality:
  - FPP-FIN enriches policy dialogue and tailors analysis to country-specific circumstances.
  - Intuitive and tractable; useful across diverse country contexts and readily incorporated into the Comprehensive Macroeconomic Forecasting Environment.
  - Supports stronger surveillance, lending, and capacity development by expanding the IMF’s macrofinancial toolkit.
- Limitations and avenues for enhancement:
  - More sophisticated models may be appropriate for many operational purposes; these can be extended to include macrofinancial linkages.
  - Future country-specific applications could integrate additional channels, including:
    - Effects of property price fluctuations.
    - Greater emphasis on the role of the exchange rate and interaction with foreign currency–denominated debt.
    - More granular data distinguishing consumer and corporate credit to improve precision of risk analysis.
- Operational imperative: translation of improved analytical techniques into consistent, broad-based integration of financial sector dynamics within core macroeconomic frameworks.

### Annex 1 — IMF Financial Soundness Indicators (FSIs) Database (key points)
- Coverage and purpose:
  - Covers approximately 130 economies that began reporting data in the mid-2000s, forming an unbalanced panel across countries and over time.
  - Provides aggregated data on banking sector income statements, balance sheets, and banking indicators; a key input for financial sector analysis in IMF surveillance.
- Definitions and coverage caveats:
  - Banking sector FSIs intended to encompass all deposit takers, including licensed banks and bank-like institutions that accept deposits; generally excludes nonbanks that perform bank-type activities but do not take deposits.
  - In practice, smaller or specialized institutions may be excluded if immaterial, lack reporting capacity, or fall outside supervisory perimeter.
- Aggregation limitations:
  - Aggregation can obscure institution-level dynamics; systemic risk from common exposures and interconnectedness is better captured through bank-level data and macroprudential stress testing.
- Differences with IFS monetary survey and reconciliations:
  - Differences between FSIs and the IMF’s International Financial Statistics (IFS) monetary survey can arise from coverage gaps and prudential consolidation practices.
  - Notions compared: “claims on the private sector” (monetary survey), “credit to the private sector” (FSIs), and “gross loans” (bank balance sheet); these differ in accounting basis, institutional coverage, and consolidation.
  - Discrepancies can often be reconciled through appropriate scaling adjustments for macrofinancial analysis.

### Annex Table 1.1 — Comparison of credit indicators across data sources (highlights)
- Accounting basis:
  - Monetary Survey: Monetary and sectoral accounts.
  - FSI: Supervisory and financial reporting standards.
  - FSI Gross Loans: Regulatory bank financial statements.
- Coverage and use:
  - Monetary Survey: Includes loans and other credit instruments provided by deposit takers and other financial corporations; measures total claims of the banking system (including the central bank) on the private sector.
  - FSI: Loans plus other credit instruments; assesses the banking system’s overall credit exposure.
  - FSI Gross Loans: Only the volume of bank loans on the balance sheet, with the stock of LLPs excluded; forms the basis for indicators such as loan-to-deposit ratio and NPL ratio.
- Borrower scope:
  - All three references cover “All private sector”; FSI Gross Loans sometimes also includes public sector.
- Consolidation/aggregation differences:
  - Monetary Survey aggregates commercial banks and the central bank with consolidation to avoid double-counting.
  - FSIs aggregate all depository institutions into an “average bank”.
  - Gross loans sum individual banks’ balance sheet entries; LLPs are added in aggregation.
- Source cited for table: IMF (2019a).

*Source: mfpea - 1. Demand Shock (PDF chapter).*

### References

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### Macro-financial modeling, DSGE, and integrated frameworks
- Adrian, T., C. J. Erceg, M. Kolasa, J. Lindé, and P. Zabczyk. 2021. “A Quantitative Microfounded Model for the Integrated Policy Framework.” IMF Working Paper 21/292, International Monetary Fund, Washington, DC. https://doi.org/10.5089/9781616356538.001
- Basu, S. S., E. Boz, G. Gopinath, F. Roch, and F. D. Unsal. 2025. “Integrated Monetary and Financial Policies for Small Open Economies.” Econometrica 93 (6): 2201–34.
- Gerali, A., S. Neri, L. Sessa, and F. M. Signoretti. 2010. “Credit and Banking in a DSGE Model of the Euro Area.” Journal of Money, Credit and Banking 42 (S1): 107–41.
- Gelain, P., and P. Ilbas. 2017. “Monetary and Macroprudential Policies in an Estimated DSGE Model with Financial Intermediation.” Journal of Economic Dynamics and Control 78:164–89.
- Gertler, M., and S. Gilchrist. 2018. “What Happened: Financial Factors in the Great Recession.” Journal of Economic Perspectives 32 (3): 3–30.
- Gertler, M., and N. Kiyotaki. 2015. “Banking, Liquidity, and Bank Runs in an Infinite-Horizon Economy.” American Economic Review 104 (7): 2213–63.
- Gertler, M., N. Kiyotaki, and A. Prestipino. 2020. “Banking Panics as Endogenous Disasters and the Welfare Gains from Macroprudential Policy.” AEA Papers and Proceedings 110: 463–69.
- Clerc, L., A. Derviz, C. Mendicino, S. Moyen, K. Nikolov, L. Stracca, J. Suarez, and A. Vardoulakis. 2015. “Capital Regulation in a Macroeconomic Model with Three Layers of Default.” International Journal of Central Banking 11 (3): 9–63.
- Krznar, I., and T. D. Matheson. 2017. “Towards Macroprudential Stress Testing: Incorporating Macro-Feedback Effects.” IMF Working Papers 17/149, International Monetary Fund, Washington, DC.
- Linde, J., F. Smets, and R. Wouters. 2020. “Challenges for Macro Models Used at Central Banks.” In Handbook of Macroeconomics, vol. 2. Amsterdam: Elsevier.
- Mkhatrishvili, S. 2025. “Inside (the) Money Machine: Modeling Liquidity, Maturity and Credit Transformations.” IMF Working Paper 2025/166, International Monetary Fund, Washington, DC. https://doi.org/10.5089/9798229020176.001
- Lipinsky, F., and M. Miescu. 2020. “Capital Gaps and Macro-Financial Amplification.” IMF Working Paper 20/209, International Monetary Fund, Washington, DC.
- Camara, S., L. Christiano, and H. Dalgic. 2025. “The International Monetary Transmission Mechanism.” In NBER Macroeconomics Annual 2024, edited by M. S. Eichenbaum, J. V. Leahy, and V. A. Ramey, vol. 39. Chicago, IL: University of Chicago Press. https://doi.org/10.1086/735260

### Credit dynamics, non-performing loans, and disaster risk
- Ari, A., S. Chen, and L. Ratnovski. 2019. “The Dynamics of Non-performing Loans During Banking Crises: A New Database.” IMF Working Paper 272, International Monetary Fund, Washington, DC.
- Gourio, F. 2012. “Credit Risk and Disaster Risk.” American Economic Journal: Macroeconomics 4 (3): 1–34.
- Schularick, M., and A. M. Taylor. 2012. “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870–2008.” American Economic Review 102 (2): 1029–61.
- Gertler, M., S. Gilchrist, and F. M. Natalucci. 2007. “External Constraints on Monetary Policy and the Financial Accelerator.” Journal of Money, Credit and Banking 39 (2–3): 295–330.
- Iacoviello, M. 2015. “Financial Business Cycles.” Review of Economic Dynamics 18 (1): 140–63.
- Hodbod, A., T. Homar, and R. Meeks. 2018. “The Impact of Macroprudential Policies on House Prices and Credit: Evidence from a Panel of Countries.” Journal of Financial Stability 38:53–70.

### Monetary policy, forecasting, and growth risks
- Adrian, T., N. Boyarchenko, and D. Giannone. 2019. “Vulnerable Growth.” American Economic Review 109 (4): 1263–89.
- Berg, A., P. D. Karam, and D. Laxton. 2006. “A Practical Model-Based Approach to Monetary Policy Analysis—Overview.” IMF Working Papers 06/80, International Monetary Fund, Washington, DC.
- Bernanke, B. S., and M. Gertler. 1989. “Agency Costs, Net Worth, and Business Fluctuations.” American Economic Review 79 (1): 14–31.
- Bernanke, B. S., M. Gertler, and S. Gilchrist. 1999. “The Financial Accelerator in a Quantitative Business Cycle Framework.” In Handbook of Macroeconomics, edited by J. B. Taylor and M. Woodford, vol. 1. Amsterdam: Elsevier.
- Carriere-Swallow, Y., and J. Marzluf. 2021. “Causes of Optimism in Growth Forecasts.” IMF Working Papers 21/275, International Monetary Fund, Washington, DC. https://doi.org/10.5089/9781616356392.001
- Prasad, A., S. Elekdag, P. Jeasakul, R. Lafarguette, A. Alter, X. Feng, and C. Wang. 2019. “Growth at Risk: Concept and Application in IMF Country Surveillance.” IMF Working Papers 19/36, International Monetary Fund, Washington, DC.
- Ho, G., and P. Mauro. 2014. “Growth: Now and Forever?” IMF Working Paper 14/117, International Monetary Fund, Washington, DC. https://doi.org/10.5089/9781498309820.001
- McLeay, M., A. Radia, and R. Thomas. 2014. “Money Creation in the Modern Economy.” Bank of England Quarterly Bulletin 2014 Q1: 14–27.
- Camara, S., L. Christiano, and H. Dalgic. 2025. “The International Monetary Transmission Mechanism.” In NBER Macroeconomics Annual 2024, edited by M. S. Eichenbaum, J. V. Leahy, and V. A. Ramey, vol. 39. Chicago, IL: University of Chicago Press. https://doi.org/10.1086/735260

### IMF publications, policy reviews, and technical manuals
- International Monetary Fund (IMF). 2014a. Financial Programming and Policies. Vol. I. Washington, DC.
- International Monetary Fund (IMF). 2014b. “2014 Triennial Surveillance Review—Overview Paper.”
- International Monetary Fund (IMF). 2019a. Financial Soundness Indicators Compilation Guide. Washington, DC.
- International Monetary Fund (IMF). 2019b. “2018 Review of Program Design and Conditionality.” IMF Policy Papers 012, Washington, DC. https://doi.org/10.5089/9781498315715.007
- International Monetary Fund (IMF). 2021. “2021 Comprehensive Surveillance Review—Overview Paper.” Policy Papers 2021/027, Washington, DC. https://doi.org/10.5089/9781513578422.007
- International Monetary Fund (IMF). 2023. Global Financial Stability Report: Chapter 2, “A New Look at Global Banking Vulnerabilities.” Washington, DC.
- International Monetary Fund (IMF). 2025. “Republic of Kazakhstan: 2024 Article IV Consultation—Press Release and Staff Report.” IMF Country Report 25/030, Washington, DC.
- International Monetary Fund (IMF). 2026a. “2026 Comprehensive Surveillance Review—Overview Paper.” IMF Policy Paper, Washington, DC.
- International Monetary Fund (IMF). 2026b. “2026 CSR—Financial Sector Coverage and Macrofinancial Integration in Article IV Consultations.” IMF Policy Paper, Washington, DC.
- Feridhanusetyawan, T., A. Hegazy, M. P. Aquino Coste, M. Andrle, R. J. Corker, and F. L. Delgado. 2025. “Macroframework Foundations Tool (MFT) 1.0: User Manual.” IMF Technical Notes and Manuals 2025/011, International Monetary Fund, Washington, DC. https://doi.org/10.5089/9798229009256.005
- Ong, L. L., ed. 2014. A Guide to IMF Stress Testing: Methods and Models. Washington, DC: International Monetary Fund.
- Christofides, C., A. R. Ghosh, U. Ramakrishnan, A. H. Thomas, L. Papi, J. Zalduendo, and J. I. Kim. 2005. “The Design of IMF-Supported Programs.” IMF Occasional Paper 241, International Monetary Fund, Washington, DC. https://doi.org/10.5089/9781589061934.084
- Hussain, M., and Y. Sholomytskyi. Forthcoming. “Oman: Technical Assistance to Central Bank on Macroeconomic Forecasting Frameworks: Concluding Report.” IMF Technical Assistance Report, International Monetary Fund, Washington, DC.
- Sholomytskyi, Y., N. Butler-Blondel, and M. Hussain. 2026. “Should Oil Economies Worry About Oil Shocks’ Impact on the Banking System? The Case of Oman.” IMF Working Paper 26/142, International Monetary Fund, Washington, DC.
- Elekdag, S., and E. Kopp. 2026. “Closing the Loop: Integrating the Financial Sector in Macroeconomic Frameworks.” IMF Working Paper, International Monetary Fund, Washington, DC, forthcoming.
- Elekdag, S., F. Lipinsky, A. Rezghi, and H. Toprak. 2026. “Designing the Optimal Macroprudential Policy Mix in Small Open Economies.” IMF Working Paper, International Monetary Fund, Washington, DC, forthcoming.
- Mkhatrishvili, S. 2025. “Inside (the) Money Machine: Modeling Liquidity, Maturity and Credit Transformations.” IMF Working Paper 2025/166, International Monetary Fund, Washington, DC. https://doi.org/10.5089/9798229020176.001

### Theoretical foundations and classic contributions
- Diamond, D. W., and P. H. Dybvig. 1983. “Bank Runs, Deposit Insurance, and Liquidity.” Journal of Political Economy 91 (3): 401–19.
- Kiyotaki, N., and J. Moore. 1997. “Credit Cycles.” Journal of Political Economy 105 (2): 211–48.
- Stiglitz, J. E., and A. Weiss. 1981. “Credit Rationing in Markets with Imperfect Information.” American Economic Review 71 (3): 393–410.
- Bernanke, B. S., and C. S. Lown. 1991. “The Credit Crunch.” Brookings Papers on Economic Activity 1991 (2): 205–47.
- Cochrane, J. 2017. “Macro-Finance.” Review of Finance 21 (3): 945–85.
- Stein, J.L. 2010. “A Critique of the Literature on the US Financial Debt Crisis.” CESifo Working Paper Series No. 2924, January 2010.

*Macro-Financial Programming — References (mfpea - References, ISBN 9798229053143)*

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_Source: https://www.imf.org/-/media/files/publications/dp/2026/english/mfpea.pdf_