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### I. Introduction and Key Model Features
- Purpose:
  - Assess effectiveness of countercyclical buffers (CCBs) and loan-to-value (LTV) limits for mitigating risk and costs of financial crises using MAPMOD augmented with an explicit housing sector.
- MAPMOD framework highlights:
  - Departs from loanable funds model: bank lending constrained by banks’ expectations about future profitability and banking regulations.
  - Banking system can create purchasing power; excessive risky loans can impair bank balance sheets and trigger crises.
  - Captures correlations of bank credit with business cycle and asset prices.
- Key model features:
  - Significant non-linearities in banks’ responses to regulatory capital and borrowers’ creditworthiness.
  - Loan portfolio supply curve: pricing of loans increases exponentially as banks approach minimum capital-adequacy ratios (CAR).
  - Individual lending supply curve: banks charge increasingly higher interest rate spreads as LTV ratio rises.
  - Interactions of non-linearities can produce financial cycles and crises consistent with historical records.
- Policy insight preview:
  - LTV limits are intrinsically procyclical; recommend basing LTV limits on a historical moving average of house prices over several years rather than current market values.
  - CCBs alone can moderate overall credit expansion but are insufficient to limit the “deadly embrace” between mortgages, household balance sheets, and house prices because CCBs are not housing-sector specific.
  - Combination: CCBs plus moving-average-based LTV limits can reinforce countercyclicality.

### II. Macroprudential Regulations: Context and Tools
- Basel III macroprudential overlay:
  - Includes CCBs between 0.0 and 2.5 percent of risk-weighted assets to raise capital during upswing and reduce in downturns.
  - Basel III leaves implementation timing and cost judgment to supervisors and suggests the credit-to-GDP gap measure.
- Limitations of Basel III and the credit-to-GDP gap:
  - Does not distinguish good vs bad credit expansions.
  - Problematic where significant dollar lending distorts the gap.
  - Not sector specific; CCBs unlikely alone to have prevented the Global Financial Crisis.
- Additional housing-focused macroprudential tools:
  - 1) Sectoral capital surcharges via higher risk weights or LGD ratios.
  - 2) LTV limits.
  - 3) Caps on DSTI or LTI ratios.
- Usage statistics (end-2014):
  - 23 countries used sectoral capital surcharges for housing.
  - 25 countries used LTV limits.
  - 15 countries had explicit DSTI or LTI caps.
- Empirical findings:
  - Macroprudential policies help manage financial cycles but work less well in busts than in booms (Cerruti, Claessens and Laeven, 2015; sample of 119 countries over 2000-13).
- Tradeoffs:
  - Regulations restrain bank credit to housing but do not internalize tradeoffs between crisis-mitigation benefits and costs of lower financial intermediation; procyclicality can accentuate credit crunches in busts.
- MAPMOD objective:
  - Provide analytical foundation to analyze tradeoffs and design less procyclical regulations.

### III. MAPMOD: Structure, Behavioral Premises, and Housing Extension
- Behavioral premises:
  - Banks lend based on expected future profits conditional on outlook and regulatory capital; then fund via deposits, wholesale funding, or debt instruments.
  - Banks contribute directly to consumption and investment by enabling leverage; good credit expansion if expectations are realized, bad expansion otherwise.
- Model sectors and collateral:
  - Sectors: households, local producers, exporters, banks, central bank, rest of world.
  - Collateral: physical capital, housing, stocks; defaults depend on asset prices with common and idiosyncratic risks; incomplete diversification leaves systemic risk on bank balance sheets.
- Two-level bank decision process:
  - Optimal loan portfolio size given lending spreads and minimum CAR.
  - Individual borrower assessment determining LTV-consistent spreads.
- Non-linearities and mispricing:
  - Lending spreads increase as bank capital approaches minimum CAR (optimal loan portfolio curve).
  - Lending spreads increase as borrower LTV increases (individual lending supply curve).
  - Overly optimistic banks shift supply curves, underprice systemic risk, leading to severe downturns when reality diverges from expectations.
- MAPMOD Mark II (housing market added):
  - Housing provides housing services plus anticipated capital gains/losses; fundamental house price defined by expected income/productivity increases.
  - Fixed supply of houses each period; prices determined by matching buyers and sellers with expected prices taken as given.
  - Mortgages: households choose down payments endogenously → LTV ratios; fixed-rate long-term mortgages assumed.
  - Probability of default driven by LTV and LTI ratios.
  - Banks price mortgages on market house values but learn defaults ex-post.
  - LGD parameter fixed at 0.25 in baseline simulations (i.e., recovery of 75,000 on a 100,000 outstanding foreclosure).
  - Bank financing drives house prices: increased mortgage supply raises demand → inflates house prices; reduced mortgage supply lowers demand → slump.
  - Securitization not allowed in model; systemic housing risk not diversifiable at economy level.

### IV. Partial-Equilibrium Insights
- Single-bank partial equilibrium (house prices exogenous):
  - Bank lends when expected return on additional mortgage (accounting for default probability) exceeds marginal cost of funds plus marginal cost of maintaining optimal CAR.
  - Value-at-risk increases with LTV → higher spreads required by banks (individual lending supply curve).
  - Household demand varies with down payments; intersection of supply and demand in LTV/spread space determines mortgage equilibrium.
- Household partial equilibrium:
  - Households buy for housing services and expected capital gains; higher expected future house prices raise current willingness to pay.
  - Households form rational expectations over a limited horizon; take banks’ lending behavior as given and do not internalize potential bank deleveraging effects on future prices.
  - Symbiotic relationship between bank lending and household demand called the “deadly embrace.”

### V. General-Equilibrium Dynamics: The Deadly Embrace
- Self-reinforcing bubble mechanism:
  - Positive shock or lowered bank risk standards → rising bank lending → rising house prices → increased household demand → further bank lending.
- Financial soundness indicators can be misleading in a bubble:
  - Increases in lending, falling NPLs, rising profitability, and improving capital adequacy can mask rising systemic leverage.
- Simulation pattern summary:
  - Years 0–5: real bank loans and real house prices rise, consumption and growth increase; average LTV on outstanding loans declines while LTV on new loans rises.
  - Year 5 reassessment: banks sharply cut housing lending and hike spreads → house price collapse, lending stall, severe recession via wealth effects; NPLs and average LTV on outstanding loans jump; capital adequacy falls and deleveraging ensues.
- Conclusion:
  - The deadly embrace can impose large macroeconomic costs.

### VI. Macroprudential Policy to Mitigate the Deadly Embrace — Findings from Simulations
A. LTV Limits
- Rationales:
  - Prevent excessive exposures to individual borrowers if limits are sufficiently low.
  - Create equity buffer against defaults (house price can fall by equity buffer before loan is underwater).
- Cost:
  - Constrains mortgage lending for households without large down payments (first-time buyers, younger households).
- Simulation findings:
  - Standard LTV limits based on current house values:
    - Damp credit growth during bubble upswing but are highly procyclical.
    - As house prices rise, LTV limit becomes less binding; as prices fall, LTV limit tightens and impedes lending recovery, prolonging recession.
  - LTV limits based on a 5-year moving average of house prices:
    - Significantly reduces procyclicality.
    - Reduces swings in house prices, bank lending, and consumption.
    - Leads to faster recovery in lending, consumption, and growth after bubble bursts.
    - In simulations, cumulative consumption gap under 5-year moving average LTV limit is always higher after ten years than under standard LTV limit or no-policy scenario.

B. Countercyclical Capital Buffers (CCBs)
- Mechanism:
  - Raise CCBs during upswing to force banks to limit lending to maintain regulatory minimum capital buffer; reduce CCBs in downturn to facilitate credit recovery.
- Simulation findings and limitations:
  - CCBs alone are ineffective at reducing bank lending during upswing in real house prices and do little to dampen positive consumption and growth impacts; boom-crash cycle only slightly moderated.
  - Key limitation: CCBs are not sector specific—while instability originates in housing, CCBs affect all sectors; systemic risk can persist in housing while intermediation in other sectors is reduced.
  - Empirical confirmation: CCBs insufficient to avoid housing-bubble-driven crises (IMF, 2014a and 2014b).
  - IMF advocacy: supplement CCBs with demand-side tools like LTV limits and DSTI caps.

C. Combination: Moving-Average LTV Limits + CCBs
- Synergies and caveats:
  - LTV limits alone are procyclical; if coupled with CCBs, release of CCBs in downturns may not increase housing lending unless LTV limits are based on a moving average.
  - Recommendation from simulations: combine LTV limits based on a moving average of house prices with CCBs to mitigate (but not eliminate) the deadly embrace.
  - DSTI and LTI caps may be useful depending on country specifics.
  - All macroprudential policies reduce financial intermediation and therefore dampen both good and bad credit cycles; the cost of reduced intermediation must be considered in policy design.

### VII. Policy Conclusions
- MAPMOD Mark II with explicit housing market produces housing boom-bust cycles without macroprudential intervention and models the deadly embrace between bank mortgages, household balance sheets, and house prices.
- Key policy findings:
  - LTV limits based on current market values are highly procyclical and impede recovery after busts.
  - LTV limits based on a historical moving average of house prices (simulated at 5 years) considerably reduce procyclicality and improve post-bust recovery; the optimal moving-average length should vary by market specifics.
  - CCBs are limited as a sole tool against housing-driven credit cycles because they are not sector specific; banks may divert credit adjustments to other sectors.
  - A combination of moving-average-based LTV limits and CCBs is likely to mitigate the deadly embrace more effectively than either alone.
  - DSTI and LTI caps may provide additional benefits depending on national financial structures.
  - All macroprudential measures entail a tradeoff by reducing financial intermediation and dampening both beneficial and harmful credit cycles; policy design must weigh this cost.

### VIII. Appendix I — Basel III Regulations: Key Elements and Implementation Notes
- Main goal:
  - Strengthen microprudential regulation and supervision of banks while adding a macroprudential overlay that includes capital buffers.
  - Envisage higher capital buffers for global systemically important financial institutions (SIFIs) and domestic systemically important banks (DSIBs).
- Pillar 1 — Capital and Risk Coverage (numerical elements preserved)
  - Capital adequacy requirements:
    - Increase to 4.5 percent of common equity as a percentage of risk-weighted assets by end-2014.
    - Capital conservation buffer of 2.5 percent of risk-weighted assets phased in over 2016–2019, bringing total common equity to 7.0 percent of risk-weighted assets by 2019.
    - Overall capital adequacy requirement will be 10.5 percent of risk-weighted assets by 2019.
  - Countercyclical capital buffers (CCBs):
    - Range between 0.0 and 2.5 percent.
    - Adjustable at authorities’ discretion to address excessive credit growth and systemic risk.
    - Intended to moderate credit growth in booms and mitigate credit crunches in downturns.
    - Implementation details and economic costs are left to supervisory authorities; guidance suggests use of a credit-to-GDP gap measure.
    - Caveat: the credit-to-GDP gap measure does not distinguish good versus bad credit expansions and is problematic in countries with significant dollar lending due to exchange rate distortions of the gap.
  - SIFI and DSIB surcharges:
    - Progressive common equity capital requirement of between 1.0 and 2.5 percent of risk-weighted assets.
  - Risk calculations and coverage changes:
    - Adjustments for complex securitizations; higher capital charges for trading derivatives and securitized assets; more stringent counterparty credit risk requirements.
    - 2 percent risk weight for exposure to central counterparties (CCPs).
  - Leverage ratio:
    - Defined as tier 1 capital over total exposure.
    - Required to be equal or above 3 percent.
    - Non-risk-based and calculated including off-balance sheet exposures.
- Pillar 2 — Risk Management and Supervision
  - Builds on Basel II, including the Internal Capital Adequacy Assessment Process (ICAAP).
  - Requires firm-wide governance and risk management capturing off-balance sheet exposures and securitization risk, management of risk concentrations, incentives for long-term risk management.
  - Introduces sound compensation practices, valuation practices, stress testing, updated accounting standards for financial instruments, corporate governance, and supervisory colleges.
- Pillar 3 — Market Discipline
  - Focus on financial disclosures and market discipline.
  - Disclosure requirements include securitization exposures and sponsorship of off-balance sheet vehicles; enhanced disclosures on components of regulatory capital.
- Liquidity standards
  - Two liquidity ratios: Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSF).
  - LCR:
    - Ratio of high quality liquid assets to total net liquidity outflows over 30 days.
    - LCR floor introduced incrementally starting at 60 percent in 2015 and reaching 100 percent by 2019.
  - Net Stable Funding (NSF) ratio:
    - Defined as stable funding (customer deposits plus long-term wholesale funding) over long-term assets.
    - Proposal to introduce a minimum standard by 2018; no agreement yet on the numerical standard.
  - Additional measures:
    - New principles for sound liquidity risk management and supervision; a liquidity framework to assist supervisors in identifying and analyzing liquidity risk trends at both the bank and system-wide level.

*Source: _wp1687 - References*

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

### _wp1687 - References

### Figures

- 1. Non-Linearities in Bank Lending Behavior ...........................................................................8
- 2. Underpricing of Risk by Banks..............................................................................................8
- 3. Good vs. Bad Credit Cycles .................................................................................................10
- 4. Co-movements in Bank Credit and House Prices ................................................................11
- 5. Bank Lending Demand and Supply .....................................................................................13
- 6. Housing Demand and Supply ..............................................................................................14
- 7. House Price Bubble and Burst .............................................................................................15
- 8. House Price Bubble and Burst with LTV Limits .................................................................18
- 9. Cumulative Consumption Gaps ...........................................................................................18
- 10. House Price Bubble and Burst with Countercyclical Buffers ............................................19

*Source: _wp1687 - References*

### Appendix I – Basel III Regulations...................................................................23

### Appendix I – Basel III Regulations...................................................................23

### I. INTRODUCTION
- Purpose: assess effectiveness of countercyclical buffers (CCBs) and loan-to-value (LTV) limits for mitigating risk and costs of financial crises using MAPMOD augmented with an explicit housing sector.
- MAPMOD framework:
  - Departs from loanable funds model: bank lending constrained by banks’ expectations about future profitability and banking regulations rather than loanable funds.
  - Banking system can create purchasing power and facilitate resource allocation when permanent growth improvements occur; excessive risky loans can impair bank balance sheets and trigger crises.
  - Captures financial cycle facts: correlations of bank credit with business cycle and asset prices (references to Brei and Gambacorta, 2014; Mendoza, 2010; Mian and Sufi, 2010; Wong, 2012).
- Key model features:
  - Significant non-linearities in banks’ responses to regulatory capital and borrowers’ creditworthiness.
  - Loan portfolio supply curve: pricing of loans increases exponentially as banks approach minimum capital-adequacy ratios (CAR).
  - Individual lending supply curve: banks charge increasingly higher interest rate spreads as LTV ratio rises.
  - Interactions of non-linearities can produce financial cycles and crises consistent with historical record (e.g., Global Financial Crisis of 2008-09).
- Policy insight preview:
  - LTV limits are intrinsically procyclical; recommended to base LTV limits on a historical moving average of house prices over several years rather than current market values.
  - CCBs alone can moderate overall credit expansion but are insufficient to limit the “deadly embrace” between mortgages, household balance sheets, and house prices because CCBs are not housing-sector specific.
  - Combination: CCBs plus moving-average-based LTV limits can reinforce countercyclicality.

### II. MACROPRUDENTIAL REGULATIONS
- Historical context: centuries of crises driven by large increases in financial leverage; Global Financial Crisis of 2008-09 linked to bank and household leverage in housing market.
- Basel III (adopted 2010) introduces macroprudential overlay:
  - Includes CCBs between 0.0 and 2.5 percent of risk-weighted assets to raise capital during upswing and reduce in downturns.
  - Basel III silent on implementation timing and cost; leaves judgment to supervisors using credit-to-GDP gap measure.
  - Credit-to-GDP gap limitations: does not distinguish good vs bad credit expansions; irrelevant where significant dollar lending distorts measure.
- Limitations of Basel III:
  - Does not focus on sectoral, leverage-driven markets (e.g., housing) prone to systemic risk.
  - CCBs unlikely alone to have prevented Global Financial Crisis.
- Additional macroprudential tools for housing:
  - 1) Sectoral capital surcharges via higher risk weights or loss-given-default (LGD) ratios.
  - 2) LTV limits.
  - 3) Caps on debt-service-to-income (DSTI) or loan-to-income (LTI) ratios.
- Usage statistics (end-2014):
  - 23 countries used sectoral capital surcharges for housing.
  - 25 countries used LTV limits.
  - 15 countries had explicit DSTI or LTI caps.
- Empirical finding: macroprudential policies help manage financial cycles but work less well in busts than in booms (Cerruti, Claessens and Laeven, 2015; sample of 119 countries over 2000-13).
- Tradeoffs: macroprudential regulations restrain bank credit to housing but do not internalize tradeoffs between crisis-mitigation benefits and costs of lower financial intermediation; procyclicality can accentuate credit crunches in busts.
- MAPMOD’s role: provide analytical foundation to analyze tradeoffs and design less procyclical regulations.

### III. A NON-TECHNICAL PRESENTATION OF MAPMOD
A. MAPMOD, Mark I
- Behavioral premises:
  - Banks do not wait for deposits; lend based on expected future profits conditional on outlook and regulatory capital, then fund lending via deposits, wholesale funding, or debt instruments.
  - Banks contribute directly to consumption and investment by enabling leverage; good credit expansion if expectations realized, bad credit expansion otherwise.
- Model structure:
  - Sectors: households, local producers, exporters, banks, central bank, rest of world.
  - Collateral: borrowers use physical capital, housing, stocks; defaults function of asset prices driven by common and idiosyncratic risks; incomplete diversification leaves systemic risk on bank balance sheets.
- Two-level bank decision process:
  - Optimal loan portfolio size given lending spreads and minimum CAR.
  - Individual borrower assessment (creditworthiness) determining LTV-consistent spreads.
- Non-linearities:
  - Lending spreads increase as bank capital approaches minimum CAR (optimal loan portfolio curve).
  - Lending spreads increase as borrower LTV increases (individual lending supply curve).
  - Overly optimistic banks shift supply curves, underprice systemic risk, leading to severe downturns when reality diverges from expectations.
- Good vs bad credit expansions illustrated via simulations: confirmed productivity boost → good expansion; disappointed productivity → bad expansion with higher NPLs, deleveraging, recession; worst-case: overly optimistic banks → severe recession.

B. MAPMOD, Mark II (housing market added)
- Housing sector mechanics:
  - Houses provide stream of housing services plus anticipated capital gains/losses; fundamental house price defined by expected income/productivity increases; inflated house price if above fundamental.
  - Fixed supply of houses each period; prices determined by matching buyers and sellers in recursive equilibrium with expected prices taken as given.
  - Simplifying assumptions: no neighborhood externalities or heterogeneity modeled (acknowledged as strong assumption).
- Mortgages:
  - Households have endogenously-determined down payments → LTV ratios; take fixed-rate long-term mortgages in this paper.
  - Probability of default driven by LTV and LTI ratios.
  - Banks price mortgages based on market house values but learn defaults ex-post.
  - LGD parameter fixed at 0.25 in baseline simulations (i.e., recovery of 75,000 on a 100,000 outstanding foreclosure).
- Bank financing drives house prices: increased mortgage supply raises demand → inflates house prices; reduced mortgage supply lowers demand → slump in prices.
- Systemic risk not diversifiable at economy level; securitization not allowed in model—consistent with evidence that mortgage securitization does not eliminate systemic housing risk.

### Partial Equilibrium Analyses
- Single-bank partial equilibrium (house prices exogenous to the bank):
  - Bank lends when expected return on additional mortgage (accounting for default probability) exceeds marginal cost of funds plus marginal cost of maintaining optimal CAR.
  - Value-at-risk increases with LTV → higher spreads required by banks (individual lending supply curve).
  - Household demand varies with down payments; intersection of supply and demand in LTV/spread space determines mortgage equilibrium.
- Household partial equilibrium:
  - Households buy for housing services and expected capital gains; higher expected future house prices raise current willingness to pay.
  - Households form rational expectations over a limited horizon; take banks’ lending behavior as given and do not internalize potential bank deleveraging effects on future prices.
  - Symbiotic relationship between bank lending and household demand dubbed “deadly embrace.”

### General Equilibrium: deadly embrace dynamics
- Positive shock or lowered bank risk standards can initiate a self-reinforcing bubble:
  - Rising bank lending → rising house prices → increased household demand → further bank lending.
- Standard financial soundness indicators may not flag the bubble: increases in lending, falling NPLs (due to many new performing loans), rising profitability, and improving capital adequacy can mask rising systemic leverage.
- Simulation pattern:
  - Years 0–5: real bank loans and real house prices rise, consumption and growth increase; average LTV on outstanding loans declines while LTV on new loans rises.
  - Year 5 reassessment: banks sharply cut housing lending and hike spreads → house price collapse, lending stall, severe recession via wealth effects; NPLs and average LTV on outstanding loans jump; capital adequacy falls and deleveraging ensues.
- Conclusion: deadly embrace can impose large macroeconomic costs.

### IV. MACROPRUDENTIAL POLICY TO MITIGATE THE DEADLY EMBRACE
- Rationale: externality arises because neither banks nor households internalize negative impact of their actions on house prices; macroprudential interventions can limit systemic risk.

A. LTV Limits
- Two rationales:
  - Prevent excessive exposures to individual borrowers if limits are sufficiently low.
  - Create equity buffer against defaults (house price can fall by equity buffer before loan is underwater).
- Cost: constrains mortgage lending for households without large down payments (first-time buyers, younger households).
- Simulation findings:
  - Standard LTV limits based on current house values dampen credit growth during bubble upswing but are highly procyclical:
    - As house prices rise, LTV limit becomes less binding; as prices fall, LTV limit tightens and impedes lending recovery, prolonging recession.
  - Alternative: LTV limits based on moving average of last 5 years of house prices (5-year moving average):
    - Significantly reduces procyclicality.
    - Reduces swings in house prices, bank lending, and consumption.
    - Leads to faster recovery in lending, consumption, and growth after bubble bursts.
    - In simulations, cumulative consumption gap under 5-year moving average LTV limit is always higher after ten years than under standard LTV limit or no-policy scenario.

B. CCBs (Countercyclical Capital Buffers)
- Mechanism: raise CCBs during upswing to force banks to limit lending to maintain regulatory minimum capital buffer; reduce CCBs in downturn to facilitate credit recovery.
- Simulation findings:
  - CCBs alone are ineffective at reducing bank lending during upswing in real house prices and do little to dampen positive consumption and growth impacts; boom-crash cycle only slightly moderated.
  - Key limitation: CCBs are not sector specific—while instability originates in housing, CCBs affect all sectors; systemic risk can persist in housing while intermediation in other sectors is reduced.
  - Empirical confirmation: CCBs insufficient to avoid housing-bubble-driven crises (IMF, 2014a and 2014b).
  - IMF advocacy: supplement CCBs with demand-side tools like LTV limits and DSTI caps.

C. Combination: LTV Limits + CCBs
- Synergy and caveats:
  - LTV limits alone are procyclical; if coupled with CCBs, LTV limits can still constrain lending in downturns so release of CCBs may not increase housing lending unless LTV limits are based on a moving average.
  - Recommendation from simulations: combine LTV limits based on a moving average of house prices with CCBs to mitigate (but not eliminate) the deadly embrace.
  - Additional tools (DSTI/LTI caps) may be useful depending on country specifics.
  - All macroprudential policies reduce financial intermediation and therefore dampen both good and bad credit cycles; the cost of reduced intermediation must be considered in policy design.

### V. POLICY CONCLUSIONS
- MAPMOD Mark II with explicit housing market models the deadly embrace between bank mortgages, household balance sheets, and house prices, producing housing boom-bust cycles without macroprudential intervention.
- Key policy findings:
  - LTV limits based on current market values are highly procyclical and impede recovery after busts.
  - LTV limits based on a historical moving average of house prices (simulated at 5 years) considerably reduce procyclicality and improve post-bust recovery; the optimal moving-average length should vary by market specifics.
  - CCBs are limited as a sole tool against housing-driven credit cycles because they are not sector specific; banks may divert credit adjustments to other sectors.
  - A combination of moving-average-based LTV limits and CCBs is likely to mitigate the deadly embrace more effectively than either alone.
  - DSTI and LTI caps may provide additional benefits depending on national financial structures.
  - All macroprudential measures entail a tradeoff by reducing financial intermediation and dampening both beneficial and harmful credit cycles; policy design must weigh this cost.

*Source: Appendix I – Basel III Regulations, _wp1687 - Appendix I – Basel III Regulations...................................................................23*

### References

### References and Appendix I – Basel III Regulations

### References (selected)
- Bibliographic compilation of works on financial cycles, macroprudential policy, DSGE models, leverage, crises, and banking procyclicality, including:
  - Benes, J., Michael Kumhof, and Douglas Laxton, 2014a, 2014b (IMF Working Papers 14/56 and 14/57).
  - Bernanke, B., Mark Gertler and Simon Gilchrist, 1999, The Financial Accelerator in a Quantitative Business Cycle Framework.
  - Bianchi, J. and Enrique G. Mendoza, 2013, “Optimal Time-Consistent Macroprudential Policy,” NBER Working Paper No. 19704.
  - Geanakoplos, J., 2009, “The Leverage Cycle,” NBER Macroeconomic Annual, Volume 24.
  - Goodhart, C. A. E., Anil K. Kashyap, Dimitrios P. Tsomocos, and Alexandros P. Vardoulakis, 2012, 2013 (NBER Working Paper No. 17909; International Journal of Central Banking).
  - Jorda, O., Moritz Schularick, and Alan M. Taylor, 2012, “When Credit Bites Back: Leverage, Business Cycles, and Crises,” FRBSF Working Paper 2011–27.
  - Kindleberger, C. P. and Robert Aliber, 2011, “Manias, Panics, and Crashes: A History of Financial Crises.”
  - Kiyotaki, N. and John H. Moore, 1997, “Credit Cycles,” Journal of Political Economy.
  - Mendoza, E. G., 2010, “Sudden Stops, Financial Crises, and Leverage,” American Economic Review.
  - Mian, A. and Amir Sufi, 2010; 2015, including “The Great Recession: Lessons from Microeconomic Data” and “House of Debt.”
  - Reinhart, C. M. and Kenneth Rogoff, 2009, “This Time is Different: Eight Centuries of Financial Folly.”
  - Wong, Tak-Chuen, 2012, “Banking Procyclicality: Cross Country Evidence,” Ph.D. Dissertation, University of Hong Kong.
- The references include IMF staff guidance notes and working papers on macroprudential policy and the use and effectiveness of macroprudential policies (e.g., IMF Working Paper 15/61; IMF Staff Guidance Note on Macroprudential Policy, December 2014).

### Appendix I – Basel III Regulations: key elements, findings, and implementation notes
- Main goal:
  - Strengthen microprudential regulation and supervision of banks while adding a macroprudential overlay that includes capital buffers.
  - Envisage higher capital buffers for global systemically important financial institutions (SIFIs) and domestic systemically important banks (DSIBs).

- Pillar 1 – Capital and Risk Coverage
  - Capital adequacy requirements:
    - Increase to 4.5 percent of common equity as a percentage of risk-weighted assets by end-2014.
    - Capital conservation buffer of 2.5 percent of risk-weighted assets phased in over 2016–2019, bringing total common equity to 7.0 percent of risk-weighted assets by 2019.
    - Overall capital adequacy requirement will be 10.5 percent of risk-weighted assets by 2019.
  - Countercyclical capital buffers (CCBs):
    - Range between 0.0 and 2.5 percent.
    - Adjustable at authorities’ discretion to address excessive credit growth and systemic risk.
    - Intended to moderate credit growth in booms and mitigate credit crunches in downturns.
    - Implementation details and economic costs are left to supervisory authorities; guidance suggests use of a credit-to-GDP gap measure.
    - Caveat: the credit-to-GDP gap measure does not distinguish good versus bad credit expansions and is problematic in countries with significant dollar lending due to exchange rate distortions of the gap.
  - SIFI and DSIB surcharges:
    - Progressive common equity capital requirement of between 1.0 and 2.5 percent of risk-weighted assets.
    - Purpose: protect the global and domestic financial systems from potential systemic insolvency.
  - Risk calculations and coverage changes:
    - Capital treatment adjustments for complex securitizations.
    - Higher capital charges for trading derivatives and securitized assets in trading books.
    - More stringent requirements for counterparty credit risk.
    - 2 percent risk weight for exposure to central counterparties (CCPs).
  - Leverage ratio:
    - Defined as tier 1 capital over total exposure.
    - Required to be equal or above 3 percent.
    - Non-risk-based and calculated including off-balance sheet exposures.

- Pillar 2 – Risk Management and Supervision
  - Builds on Basel II, including the Internal Capital Adequacy Assessment Process (ICAAP).
  - Requires firm-wide governance and risk management capturing:
    - Off-balance sheet exposures and securitization risk.
    - Management of risk concentrations.
    - Incentives for banks to manage risk and returns over the long term.
  - Introduces:
    - Sound compensation practices.
    - Valuation practices.
    - Stress testing.
    - Updated accounting standards for financial instruments.
    - Corporate governance and supervisory colleges.

- Pillar 3 – Market Discipline
  - Focus on financial disclosures and market discipline.
  - Disclosure requirements include:
    - Securitization exposures and sponsorship of off-balance sheet vehicles.
    - Enhanced disclosures on components of regulatory capital and reconciliation to reported accounts.
    - Comprehensive explanation of how a bank calculates its regulatory capital.

- Liquidity standards
  - Two liquidity ratios introduced: Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSF).
  - LCR:
    - Defined as the ratio of high quality liquid assets to total net liquidity outflows over 30 days.
    - LCR floor introduced incrementally starting at 60 percent in 2015 and reaching 100 percent by 2019.
  - Net Stable Funding (NSF) ratio:
    - Defined as stable funding (customer deposits plus long-term wholesale funding) over long-term assets.
    - Proposal to introduce a minimum standard by 2018; no agreement yet on the numerical standard.
  - Additional measures:
    - New principles for sound liquidity risk management and supervision.
    - A liquidity framework to assist supervisors in identifying and analyzing liquidity risk trends at both the bank and system-wide level.

*Source: _wp1687 - References and Appendix I – Basel III Regulations*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2016/_wp1687.pdf_