## A Monitoring Framework for Global Financial Stability (sdnea2019006)

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### Executive summary
- Purpose: describe the conceptual framework that underpins the Global Financial Stability Report (GFSR) approach to evaluating global financial stability risks.
- Objective: enhance transparency about how the GFSR makes its assessments and improve communication; the GFSR is one of the IMF’s flagships assessing financial stability and is released following a discussion by the Executive Board.
- Core framework:
  - Cyclical financial stability risks arise as macro‑financial imbalances increase because of greater risk‑taking by lenders and borrowers.
  - High imbalances can amplify negative shocks and create an adverse feedback loop: prices fall → financial firms deleverage → sharp decline in economic growth.
  - Framework builds on research exploring macro‑financial linkages that were often ignored before the financial crisis.
- Two‑part empirical approach:
  - Part 1: monitor a set of indicators in a matrix defined by types of macro‑financial imbalances across types of lenders and borrowers (assess asset valuations, leverage and funding mismatches of financial intermediaries, and credit of borrowers).
  - Part 2: produce an aggregate measure of financial stability risk expressed as downside risks to forecast GDP growth conditional on financial conditions—growth at risk (GaR).
- Key innovation of GaR:
  - Links the entire distribution of forecast GDP growth to financial conditions (the price of risk), capturing risks to expected growth in addition to expected growth itself.
  - Reflects possible intertemporal trade‑offs: loose financial conditions may raise near‑term growth and reduce near‑term risks but increase medium‑term downside risks as vulnerabilities build.
- Complementarity:
  - The granular vulnerability matrix provides nuance and identifies targets for macroprudential policy.
  - GaR is a continuous, regularly updatable measure allowing incorporation of financial stability risks into prudential or monetary policy decision frameworks and fostering communication/co‑ordination among regulators and central banks.

### Financial stability monitoring framework — mechanics and premise
- Premise:
  - Stylized conceptual framework for identifying and monitoring cyclical risks to financial stability, grounded in macro‑financial linkages and how the financial sector propagates and amplifies shocks.
  - Distinction: shocks (external, hard to predict) versus macro‑financial imbalances (endogenous responses to low price of risk).
- Mechanism:
  - Low price of risk → lenders and borrowers respond → build‑up of imbalances (high asset valuations, high leverage, maturity mismatches).
  - When imbalances are high, negative shocks that raise the price of risk can trigger asset‑price declines and balance‑sheet unwinds → credit restriction → higher downside risks to growth.
  - Degree of financial stability risk depends jointly on severity of shocks and size of imbalances.

### Financial vulnerabilities and externalities
- Role of the price of risk:
  - A low price of risk, given financial frictions, induces macro‑financial imbalances; empirical representation often uses a financial conditions index (FCI) composed of asset prices conditional on the state of the economy.
  - Financial conditions help predict expected growth, but stability assessments must also capture built‑up vulnerabilities and conditional downside risks after periods of low price of risk.
- Financial frictions and credit cycles:
  - Asymmetric information and external finance premia imply loose monetary policy or financial conditions can improve borrower net worth and, via financial accelerator effects, increase credit for households and businesses.
  - Net worth changes for financial institutions facing capital constraints can affect credit supply procyclically.
  - Borrowers may ignore externalities, leading to excess credit.
- Endogenous intermediary responses and correlated behavior:
  - Higher asset prices boost capital adequacy and ease risk‑management constraints, prompting increased leverage, short‑term funding, and maturity mismatches.
  - Investment mandates, agency costs, and relative‑performance compensation can increase correlation in risk‑taking and systemic downside risk.
- International integration and spillovers:
  - Increased international financial integration raises cross‑country correlations and the potential for transmission and amplification through capital flows.
  - Spillover severity from US financial distress depends on domestic banking resilience and credit market conditions.

### Complexity, amplification channels, and risk regimes
- Complexity and neglected downside risks:
  - Increases in the complexity of the financial system that lead to loss of information can result in greater overall uncertainty and nonlinear outcomes.
  - Flight‑to‑quality episodes and Knightian uncertainty can render risk management models obsolete.
  - Beliefs that extrapolate the past and neglect downside risks can keep the price of risk very low for prolonged periods and rationalize high leverage.
- Amplification channels:
  - Compressed risk premiums tend to be followed by a reversal of valuations; narrow risk premiums for corporate bonds predict negative returns and associated output contraction.
  - Leverage cycles: optimistic leveraged investors may be forced to sell assets to more pessimistic buyers who value them less (fire‑sale dynamics).
  - Fire sales generate losses for sellers and other holders; if other holders are constrained they may be forced to sell, producing negative feedback loops that can trigger sharp contractions in credit and decreases in real output.
- Risk regimes and output downside risk:
  - Downside risks to output growth are greatest when both asset price valuations and financial vulnerabilities are high.
  - If valuations are high but vulnerabilities are low, increases in the price of risk likely cause large market volatility but not substantial magnification of output losses.
  - Policies that slow buildup or reduce vulnerabilities mitigate sharp downside risks but may impose costs when risks are not apparent.

### Monitoring matrices — scope and illustrative indicators
- Organization:
  - Column headings: asset price valuations and market liquidity; leverage; maturity and liquidity mismatch; external debt claims and currency mismatch; interconnections and complexity.
  - Row headings: banks, nonbanks, market‑based intermediaries, households, businesses, governments.
- Selected illustrative indicators (preserved as in source):
  - Asset Price Valuations and Market Liquidity: LIBOR‑OIS spreads; Term premiums; Risk spreads; Volatility; Market depth; Trading volumes; Equity risk premium; Implied volatility; Volatility risk premium; Cross‑currency swaps; FX implied volatility; House price growth; House‑price‑to‑rent deviation; Commercial property price growth; Commercial‑price‑to‑income deviation; Lending standards; Risk premiums; Underwriting standards.
  - Banking sector: Regulatory capital; Stress test capital; Market‑based capital measures; Off‑balance‑sheet assets and derivatives; Short‑term wholesale funds ratio; Liquid asset ratios; Regulatory liquidity; Asset‑liability duration gap; Collateral eligible for the discount window; US$ funding needs; Cross‑border funding; Reliance on cross‑currency FX swaps; Interbank claims; Nonbank financial claims; Cross‑border activities; Price‑based systemic risk measures.
  - Nonbank and market‑based finance: Regulatory capital; Leverage ratios; Off‑balance‑sheet assets and derivatives; Securitizations (risk retention); Margin credit; Collateralized borrowing and haircuts; Short‑term wholesale funds ratio; Carry trades; Open‑end funds and exchange‑traded funds (with less liquid assets); Open‑end and other funds invested in foreign debt; Claims on banks; Claims on other nonbank institutions; Financial innovations that introduce complexity; Common business models (e.g., index funds).
  - CCPs: Capital; Default fund; Margins; Credit lines; Liquidity lines; Members provide services to CCPs; Members are connected to multiple CCPs.
  - Households: Credit to GDP; Credit growth; Debt service; Lending standards; Debt with adjustable rates; Debt overhang; Home foreclosure externalities.
  - Businesses: Credit to GDP; Credit growth; Interest coverage; Lending standards; Short‑term debt; Adjustable‑rate debt; Liquid assets; Liquidity and depth of securities market; Debt issued in foreign currencies.
  - Government: Government debt to GDP; Debt growth; Off‑balance‑sheet liabilities; Debt maturity profile; Short‑term debt; Liquidity and depth of market; External debt; US dollar versus local currency debt; Short‑term debt to foreign exchange reserves; Capital flows.
- Notes:
  - Framework stresses need for consistent measures across countries to improve multilateral surveillance and notes data limitations for many countries/regions while emphasizing flexibility to incorporate emerging vulnerabilities and financial innovations.

### Aggregate measure: Growth‑at‑Risk (GaR) approach
- Definition and interpretation:
  - GaR is defined as a low percentile of the conditional GDP growth distribution; the lower 5th percentile of the distribution is chosen here.
  - A GaR value indicates that there is a 5 percent probability that forecast growth will be lower than that value.
- Rationale and policy relevance:
  - GaR captures downside risks to GDP growth arising from financial conditions and expresses risks in terms of output growth, the ultimate welfare metric.
  - GaR emphasizes the entire distribution of forecast growth rather than point estimates and highlights intertemporal trade‑offs from loose financial conditions.
- Implementation history:
  - GaR was introduced in the GFSR in April 2017; the presented estimates represent continued methodological improvements and broader applicability.
  - The framework presents a term structure of GaR over a projection horizon of three years based on initial financial conditions to illustrate persistence of effects.

### Estimating GaR — data inputs and methodology
- Panels used:
  - One panel of 11 advanced economies (AEs).
  - One panel of 11 emerging market economies (EMEs).
- Forecasting setup:
  - GDP growth distribution is forecast as a function of: financial conditions index (FCI), GDP growth, inflation, and indicators of financial vulnerabilities (specifically credit growth and a dummy variable for a credit boom), plus country fixed effects and a constant.
  - Quantile regressions are used to model the GDP distribution.
- Financial Conditions Indices (FCIs):
  - FCIs are constructed for each country to capture funding and credit costs, representing the underlying price of risk.
  - Up to 17 variables are used in the FCI, including domestic and global financial price indicators, corporate credit risk spreads, equity prices, volatility, and foreign exchange for each country.
  - Specific variables listed include: interbank spread; corporate spread; sovereign spread; term spread; equity returns; equity return volatility; change in real long‑term rate; MOVE; house price returns; percent change in the equity market capitalization of the financial sector to total market capitalization; equity trading volume; expected default frequencies for banks; market capitalization for equities; market capitalization for bonds; domestic commodity price inflation; foreign exchange moves; and VIX.
  - Two credit variables are excluded from the FCI in this work because credit is modeled separately to capture interactions of price terms and credit.
- Financial vulnerability measures:
  - Growth in credit to GDP.
  - A credit boom dummy defined as the interaction of high credit‑to‑GDP growth and a high FCI.

### Key empirical features and the term structure of downside risks
- Distributional asymmetry:
  - One‑year‑ahead conditional GDP growth distributions show volatility is not constant and risks are more skewed to the downside than upside.
  - When the median projected growth is lower, the 5th percentile is also lower; the 95th percentile exhibits little variability.
- Term structure findings:
  - Coefficient estimates of FCI for the lower 5th percentile differ significantly from coefficients for the median over the near‑term projection horizon for both AEs and EMEs.
  - Negative coefficients in near‑term quarters for the 5th percentile indicate that looser financial conditions (a decrease in the FCI) imply a significant decrease in downside risk in the near term.
  - A reversal in the signs of coefficients on FCI for the 5th percentile suggests an intertemporal trade‑off: reduced downside risks in the near term are not sustained and abate significantly in the medium term.
- Estimation details preserved:
  - Panels plot estimated coefficients on the FCI from panel quantile regressions for the median and the 5th percentile (GaR) for one to twelve quarters into the future.
  - Estimates are based on local projection estimation methods; standard errors from bootstrapping; bands represent plus or minus one standard deviation.
  - Advanced economies (AEs) comprise 11 countries, with data for most from 1973 to 2017.
  - Emerging market economies (EMEs) comprise 11 countries, with data for most from 1996 to 2017.

### Illustrative term‑structure example and shift in conditional distribution
- Representative country example (panel average of 11 AEs):
  - Projection periods h at 4 and 10 quarters.
  - Conditioning: initial loosest FCI (top 1 percent) and a credit boom (interaction of high credit growth and loose FCI).
- Observations:
  - Near term (h = 4): higher median and lower variance than medium term.
  - Medium term (h = 10): lower median, higher variance, and a much fatter left tail; downside risk is much greater at ten quarters ahead than at four quarters ahead.
  - Term‑structure construction: for each forecast quarter h, estimate the forecast distribution and plot the 5th percentile to trace GaR over time.
- Mechanism interpretation:
  - Loose financial conditions initially raise projected median GDP growth and tighten the distribution.
  - Over the medium term, endogenous buildup of vulnerabilities leads to a sharper rise in downside volatility of GDP growth when the system is hit by a shock—consistent with leverage cycle dynamics.

### Global GaR, recent assessments, and contributors to medium‑term risks
- Global GaR implementation:
  - Global GaR estimated using global GDP and a global FCI; used to communicate aggregate top‑down financial stability risks in April 2018 and in subsequent GFSR work.
  - Global GDP = aggregate GDP for 43 countries.
  - Global FCI based on FCIs for these areas, including the 29 jurisdictions with systemically important financial sectors.
- April 2019 GFSR findings:
  - Global FCI loosened from the previous quarter, though not as loose as 2018:Q3.
  - Conditional on global FCI, one‑year‑ahead forecast densities suggested an improvement in downside risks relative to the previous quarter.
  - One‑year‑ahead GaR forecast rose to near the upper range of its historical distribution.
  - Three‑year‑ahead GaR forecasts are less favorable but indicate downside risks less severe than in late 2017 (when very loose global financial conditions existed).
- Identifying contributors to medium‑term downside risks:
  - Monitoring matrices can highlight contributors; one likely contributor is greater indebtedness of borrowers.
  - Spider map and heat map of financial vulnerabilities highlight elevated sovereign debt, household debt, and nonfinancial business debt in a number of regions, and high leverage at banks and other financial firms in China.

### Improving GaR, systemic contribution measures, and policy tools
- Improvements suggested:
  - Include indicators of financial vulnerability (as in the monitoring matrices) in global GaR estimates to assess which vulnerabilities present significant risks.
  - Account for varying contributions by individual countries beyond GDP weights due to differences in interconnection or financial‑center significance.
  - Possible methodology: use Adrian and Brunnermeier (2016) conditional value at risk method to estimate systemic risk contributions.
- Macroprudential policy and survey findings:
  - IMF guidance note (2014) links types of financial vulnerabilities to macroprudential tools.
  - IMF annual macroprudential policy survey (initial survey early 2017) maps tools to vulnerabilities and reports institutional frameworks.
  - Survey findings from 111 countries reporting a macroprudential authority:
    - 80 countries indicate a significant role for the central bank.
  - Commonly used tools: measures addressing banks’ credit exposures; liquidity and FX mismatches of banks; liquidity and fire‑sale risks of nonbanks; tools for systemically important financial institutions.
  - Liquidity coverage ratio, net stable funding ratio, and net foreign exchange positions were the three most used tools for liquidity and FX mismatches.
  - Fewer countries reported authority or use of tools to address maturity and FX mismatch vulnerabilities of nonbank lenders or financial markets.
- Evidence on effectiveness:
  - Cerutti, Claessens, and Laeven (2015): borrower‑based tools (loan‑to‑value and debt‑service‑to‑income) can significantly reduce household credit growth; effects smaller in open economies with some cross‑border avoidance.
  - Akinci and Olmstead‑Rumsey (2018): tightening loan‑to‑value, debt‑service‑to‑income, and other housing measures have significant effects on credit and house prices, mostly in EMEs.
- Institutional structures:
  - Rapid growth of multiagency financial stability committees since the global financial crisis and increased role of the Ministry of Finance in setting policies noted; wide range of practices and limited evidence on effectiveness.

### Conclusions and policy implications
- The GFSR conceptual framework:
  - Evolving to enhance transparency and communication on cyclical financial stability risks.
  - Grounded in macro‑financial linkages linking the financial sector to macroeconomic growth and stability.
  - Emphasizes looking ahead for different manifestations of vulnerabilities as financial integration increases.
- GaR's contribution:
  - Emphasizes attention to the entire distribution of forecast growth from financial conditions rather than single point estimates.
  - Emphasizes how forecast risks to growth evolve over time: loosening financial conditions can boost near‑term growth and reduce volatility but incentivize risk‑taking that builds vulnerabilities and raises medium‑term downside risks.
- Policy relevance:
  - Consistent metrics improve bilateral and multilateral surveillance and communication among macroprudential policymakers.
  - Better data on the use and effectiveness of macroprudential tools supports systematic assessments and policy implementation to reduce financial stability risks.

*Source: Excerpt from "A Monitoring Framework for Global Financial Stability," IMF staff (sdnea2019006).*

### EXECUTIVE SUMMARY __________________________________________________________________________ 4

### A MONITORING FRAMEWORK FOR GLOBAL FINANCIAL STABILITY

### EXECUTIVE SUMMARY
- The paper describes the conceptual framework that underpins the current approach in the Global Financial Stability Report (GFSR) for evaluating global financial stability risks.
- Objective: enhance transparency about how the GFSR makes its assessments and improve communication; the GFSR is one of the IMF’s flagships assessing financial stability and is released following a discussion by the Executive Board.
- Core framework:
  - Cyclical financial stability risks arise as macro-financial imbalances increase because of greater risk-taking by lenders and borrowers.
  - High imbalances can amplify negative shocks and create an adverse feedback loop: prices fall → financial firms deleverage → sharp decline in economic growth.
  - Framework builds on research exploring macro-financial linkages that were often ignored before the financial crisis.
- Two-part empirical approach:
  1. Monitor a set of indicators in a matrix defined by types of macro-financial imbalances across types of lenders and borrowers (assess asset valuations, leverage and funding mismatches of financial intermediaries, and credit of borrowers).
  2. Produce an aggregate measure of financial stability risk expressed as downside risks to forecast GDP growth conditional on financial conditions—growth at risk (GaR).
- Key innovation of GaR:
  - Links the entire distribution of forecast GDP growth to financial conditions (the price of risk), capturing risks to expected growth in addition to expected growth itself.
  - Reflects possible intertemporal trade-offs: loose financial conditions may raise near-term growth and reduce near-term risks but increase medium-term downside risks as vulnerabilities build.
- Complementarity:
  - The granular vulnerability matrix provides nuance and identifies targets for macroprudential policy.
  - GaR is a continuous, regularly updatable measure allowing incorporation of financial stability risks into prudential or monetary policy decision frameworks and fostering communication/co‑ordination among regulators and central banks.

### INTRODUCTION
- Purpose: describe the conceptual framework guiding assessments of financial stability risks for multilateral surveillance as presented in the GFSR.
- Emphasis:
  - Cyclical risks stem from increased risk-taking under loose financial conditions, producing macro-financial imbalances (compressed risk premiums, higher leverage, greater maturity transformation).
  - Collective incentives (competition, incomplete compensation contracts) can increase correlated risk-taking.
- Framework supports consistency in measuring vulnerabilities across countries and time and provides a summary statistic to quantify aggregate financial stability risks.
- Two-part empirical approach reiterated:
  - Part 1: broad indicator monitoring of macro-financial imbalances as intermediate targets for macroprudential policies.
  - Part 2: aggregate measure—GDP growth at a low percentile of its forecast distribution conditional on financial conditions (GaR).
- Trade-offs and complementarities:
  - The vulnerability matrix is flexible but cannot by itself provide a single quantitative severity metric or direct policy prescription.
  - GaR provides a time series of downside risks for near term or medium-term (two to three years ahead) comparisons with historical values but lacks the granular detail needed for specific policy actions.
- Development and data challenges:
  - The framework incentivizes data and model improvements.
  - Global GaR is estimated from a single global financial conditions summary index; work on an index of global financial vulnerabilities is underway.
  - GaR is difficult to estimate for countries without deep financial markets or financial data; alternative measures for those countries are needed.
- Policy relevance:
  - GaR translates financial stability risks into terms (GDP growth) used by macro policymakers, aiding policy coordination and comprehensive macroeconomic management.
  - Monetary policy is relevant because the policy rate underpins the price of risk; macroprudential policies may better target specific vulnerabilities, with mutual information improving policy interactions.

### FINANCIAL STABILITY MONITORING FRAMEWORK
- Framework premise:
  - Stylized conceptual framework for identifying and monitoring cyclical risks to financial stability, grounded in macro-financial linkages and how the financial sector propagates and amplifies shocks.
  - Distinction: shocks (external, hard to predict) versus macro-financial imbalances (endogenous responses to low price of risk).
- Mechanism:
  - Low price of risk → lenders and borrowers respond → build-up of imbalances (high asset valuations, high leverage, maturity mismatches).
  - When imbalances are high, negative shocks that raise the price of risk can trigger asset-price declines and balance-sheet unwinds → credit restriction → higher downside risks to growth.
  - Degree of financial stability risk depends jointly on severity of shocks and size of imbalances.

### A. FINANCIAL VULNERABILITIES AND EXTERNALITIES
- Role of the price of risk:
  - A low price of risk, given financial frictions, induces macro-financial imbalances; empirical representation often uses a financial conditions index (FCI) composed of asset prices conditional on the state of the economy.
  - Financial conditions help predict expected growth, but stability assessments must also capture built-up vulnerabilities and conditional downside risks after periods of low price of risk.
- Financial frictions and credit cycles:
  - Asymmetric information and external finance premia: loose monetary policy or financial conditions can improve borrower net worth and, via financial accelerator effects, increase credit for households and businesses.
  - Net worth changes for financial institutions facing capital constraints can affect credit supply procyclically.
  - Borrowers may ignore externalities, leading to excess credit.
  - These mechanisms link financial conditions to stability risks but typically do not by themselves produce sharply nonlinear crisis amplifications.
- Endogenous financial intermediary responses:
  - Higher asset prices boost capital adequacy and ease risk-management constraints, prompting increased leverage, short-term funding, and maturity mismatches (references: Brunnermeier and Pedersen 2009; Adrian and Shin 2010, 2014; Adrian and Boyarchenko 2016).
  - Looser capital constraints raise leverage of the marginal investor and reduce risk premiums (He and Krishnamurthy 2013).
  - Improved business prospects can raise lending but weaken underwriting standards where banks have private borrower information (Dell’Ariccia and Marquez 2006).
  - Local currency appreciation can improve local borrower balance sheets and increase bank leverage, linking stability to exchange-rate shocks (Bruno and Shin 2014).
- Correlated behavior and agency effects:
  - Investment mandates and agency costs can increase correlation in risk-taking; competition in boom periods can reduce screening and raise loan volumes.
  - Relative-performance compensation can incentivize managers to accept correlated downside risk, increasing systemic crisis and fire-sale hazards (Morris and Shin 2014).
- International integration and spillovers:
  - Increased international financial integration raises cross-country correlations and the potential for transmission and amplification through capital flows.
  - Empirical evidence shows contagion and spillovers from shifts in risk preferences; spillover severity from US financial distress depends on domestic banking resilience and credit market conditions.

*Source: IMF staff, from "EXECUTIVE SUMMARY" and corresponding sections of the referenced chapter.*

### 14.      In addition, increases in the complexity of the financial system that lead to loss of

### 14–30: Monitoring Framework for Global Financial Stability (Selected Excerpts)

### Complexity, neglected downside risks, and leverage
- Increases in the complexity of the financial system that lead to loss of information can result in greater overall uncertainty and nonlinear outcomes.
- Flight-to-quality episodes are triggered by events and unexpected correlations, rendering risk management models obsolete and leading to investor disengagement under Knightian uncertainty (Caballero and Krishnamurthy 2008).
- Beliefs that extrapolate the past and neglect downside risks can explain why the price of risk can be very low for prolonged periods (Gennaioli and Shleifer 2018).
- Neglected downside risks can rationalize how financial systems become highly leveraged as agents leverage up when they believe the price of risk is unlikely to increase sharply and that other agents are protected from negative shocks.

### Amplification channels: compressed risk premiums, repricing, leverage cycles, and fire sales
- Compressed risk premiums tend to be followed by a reversal of valuations; narrow risk premiums for corporate bonds are useful predictors of negative returns in subsequent years and associated output contraction (López-Salido, Stein, and Zakrajšek 2017).
- Leverage cycles: optimistic, leveraged investors may be forced to sell assets, leaving assets to more pessimistic buyers who value them less (Geanakoplos 2009).
- Fire sales generate losses for sellers and other holders; if other holders are constrained they may be forced to sell, producing negative feedback loops that can trigger sharp contractions in credit and decreases in real output.
- Negative shocks increase the price of risk; amplification depends on degree of vulnerability:
  - First amplification: fall in asset prices (larger if assets are overvalued/high asset valuations).
  - Second amplification: financial vulnerabilities (high leverage → forced deleveraging → fire sales → further repricing; Brunnermeier and Pedersen 2009; Greenwood, Landier, and Thesmar 2015).
  - Net worth of borrowers falls, risk-management constraints bind, leading to declines in credit, output, and inflation.

### Risk regimes and implications for output downside risks
- Risks to financial stability (measured as downside risks to output growth) are greatest when both asset price valuations and financial vulnerabilities are high.
- When both valuations and vulnerabilities are low, an increase in the price of risk has a much more muted effect on asset prices and credit supply.
- If valuations are high but vulnerabilities are low, an increase in the price of risk likely causes large financial market volatility but not substantial magnification of output losses (example: deflating of the tech bubble in the United States in 2000 produced a modest recession with little imprint on financial intermediaries).
- Policies that slow the buildup or reduce financial vulnerabilities mitigate steep rises in the price of risk and sharp downside risks to output in the event of large adverse shocks, but they may impose costs when risks are not apparent (e.g., higher price of risk in periods with small negative shocks and low volatility).

### Monitoring matrices: scope and indicators
- Two matrices (Figures 3 and 4) organize monitoring by:
  - Column headings: asset price valuations and market liquidity; leverage; maturity and liquidity mismatch; external debt claims and currency mismatch; interconnections and complexity.
  - Row headings: different parts of the financial sector/markets (banks, nonbanks, market-based intermediaries, households, businesses, governments).
- Illustrative indicators (selected examples preserved as in source):
  - Asset Price Valuations and Market Liquidity: LIBOR-OIS spreads; Term premiums; Risk spreads; Volatility; Market depth; Trading volumes; Equity risk premium; Implied volatility; Volatility risk premium; Cross-currency swaps; FX implied volatility; House price growth; House-price-to-rent deviation; Commercial property price growth; Commercial-price-to-income deviation; Lending standards; Risk premiums; Underwriting standards.
  - Financial Vulnerabilities by sector (selected cell examples preserved):
    - Banking Sector: Regulatory capital; Stress test capital; Market-based capital measures; Off-balance-sheet assets and derivatives; Short-term wholesale funds ratio; Liquid asset ratios; Regulatory liquidity; Asset-liability duration gap; Collateral eligible for the discount window; US$ funding needs; Cross-border funding; Reliance on cross-currency FX swaps; Interbank claims; Nonbank financial claims; Cross-border activities; Price-based systemic risk measures.
    - Nonbank Financial Firms and Market-Based Finance: Regulatory capital; Leverage ratios; Off-balance-sheet assets and derivatives; Securitizations (risk retention); Margin credit; Collateralized borrowing and haircuts; Short-term wholesale funds ratio; Carry trades; Open-end funds and exchange-traded funds (with less liquid assets); Open-end and other funds invested in foreign debt; Claims on banks; Claims on other nonbank institutions; Financial innovations that introduce complexity; Common business models (e.g., index funds).
    - Central Counterparties (CCPs): Capital; Default fund; Margins; Credit lines; Liquidity lines; Members provide services to CCPs; Members are connected to multiple CCPs.
    - Private Nonfinancial—Households: Credit to GDP; Credit growth; Debt service; Lending standards; Debt with adjustable rates; Debt overhang; Home foreclosure externalities.
    - Private Nonfinancial—Business: Credit to GDP; Credit growth; Interest coverage; Lending standards; Short-term debt; Adjustable-rate debt; Liquid assets; Liquidity and depth of securities market; Debt issued in foreign currencies.
    - Government Sector: Government debt to GDP; Debt growth; Off-balance-sheet liabilities; Debt maturity profile; Short-term debt; Liquidity and depth of market; External debt; US dollar versus local currency debt; Short-term debt to foreign exchange reserves; Capital flows.
- The framework stresses the need for consistent measures across countries to improve multilateral surveillance and notes data limitations for many countries/regions, while emphasizing flexibility to incorporate emerging vulnerabilities and financial innovations.

### Aggregate measure: Growth-at-Risk (GaR) approach
- GaR overview:
  - GaR is a summary top-down measure of risks to financial stability arising from financial conditions.
  - GaR captures downside risks to GDP growth by defining GaR as a low percentile of the conditional GDP growth distribution; the lower 5th percentile of the distribution is chosen here.
  - A GaR value indicates that there is a 5 percent probability that forecast growth will be lower than that value.
  - GaR was introduced in the GFSR in April 2017; the presented estimates represent continued methodological improvements and broader applicability.
  - The framework then presents a term structure of GaR over a projection horizon of three years based on initial financial conditions to illustrate persistence of effects.
- Policy relevance:
  - GaR expresses risks in terms of output growth, the ultimate welfare metric.
  - Other summary measures (cost of fire-sale externalities in bank capital terms; probability of multiple bank failures; conditional value at risk) are less directly translatable to output risk.
  - Work is ongoing to model how macroprudential and monetary policies affect GaR; the current framework does not offer policy prescriptions.

### A. Estimating GaR: data inputs and methodology
- Empirical estimations use two panels:
  - One panel of 11 advanced economies (AEs).
  - One panel of 11 emerging market economies (EMEs).
- Forecasting setup:
  - GDP growth distribution is forecast as a function of: financial conditions index (FCI), GDP growth, inflation, and indicators of financial vulnerabilities (specifically credit growth and a dummy variable for a credit boom), plus country fixed effects and a constant.
  - Quantile regressions are used to model the GDP distribution.
- Financial Conditions Indices (FCIs):
  - FCIs are constructed for each country to capture funding and credit costs, representing the underlying price of risk.
  - FCIs are estimated by controlling for current macroeconomic conditions and are more than a measure of unconditional cost of funds.
  - Up to 17 variables are used in the FCI, including domestic and global financial price indicators, corporate credit risk spreads, equity prices, volatility, and foreign exchange for each country.
  - Specific variables listed include: interbank spread; corporate spread; sovereign spread; term spread; equity returns; equity return volatility; change in real long-term rate; MOVE; house price returns; percent change in the equity market capitalization of the financial sector to total market capitalization; equity trading volume; expected default frequencies for banks; market capitalization for equities; market capitalization for bonds; domestic commodity price inflation; foreign exchange moves; and VIX.
  - Two credit variables are excluded from the FCI in this work because credit is modeled separately to capture interactions of price terms and credit.
- Financial vulnerability measures:
  - Growth in credit to GDP (importance shown by Borio, Drehmann, and Tsatsaronis 2011).
  - A credit boom dummy variable defined as the interaction of high credit-to-GDP growth and a high FCI.

### B. Key empirical features and the term structure of downside risks
- Distributional asymmetry:
  - One-year-ahead conditional GDP growth distributions show volatility is not constant and risks are more skewed to the downside than upside.
  - When the median projected growth is lower, the 5th percentile is also lower; the 95th percentile exhibits little variability.
- Term structure of GaR (Figure 8):
  - Coefficient estimates of FCI for the lower 5th percentile differ significantly from coefficients for the median over the near-term projection horizon for both AEs and EMEs.
  - Negative coefficients in near-term quarters for the 5th percentile indicate that looser financial conditions (a decrease in the FCI) imply a significant decrease in downside risk in the near term.
  - The larger changes in coefficients for the 5th percentile relative to the median show that the expected growth distribution shifts significantly over the projection horizon.
  - A reversal in the signs of coefficients on FCI for the 5th percentile suggests an intertemporal trade-off for loose financial conditions: reduced downside risks in the near term are not sustained and abate significantly in the medium term.
- Estimation specifics preserved:
  - Panels plot estimated coefficients on the FCI from panel quantile regressions for the median and the 5th percentile (GaR) for one to twelve quarters into the future.
  - Estimates are based on local projection estimation methods; standard errors from bootstrapping; bands represent plus or minus one standard deviation.
  - Advanced economies (AEs) comprise 11 countries, with data for most from 1973 to 2017.
  - Emerging market economies (EMEs) comprise 11 countries, with data for most from 1996 to 2017.

*Italic: Source: Excerpt from "A Monitoring Framework for Global Financial Stability," IMF staff (sdnea2019006).*

### 31.      The shift in the conditional distribution of GDP growth is    consistent with buildups of

### 31.      The shift in the conditional distribution of GDP growth is    consistent with buildups of

### Shift in conditional distribution and macro‑financial linkages
- Loose financial conditions initially: projected GDP growth is higher and its distribution is tighter.
- Mechanism: When financial conditions are loose and risk management constraints are less binding, vulnerabilities can build through various mechanisms in the presence of financial frictions.
- Medium‑term consequence: A sharper rise in downside volatility of GDP growth when the system is hit by a shock.
- The change in coefficients is consistent with a leverage cycle (Geanakoplos (2009); Adrian and Shin (2010)), highlighting an intertemporal trade-off:
  - Loose financial conditions raise growth and reduce volatility in the near term.
  - Loose financial conditions increase volatility in the medium term because of endogenous buildups of vulnerabilities.

### Growth‑at‑Risk (GaR) — representative country and term structure
- Illustration setup:
  - Panel: average country in a panel of 11 advanced economies.
  - Projection periods h at 4 and 10 quarters.
  - Conditioning: initial loosest FCI (top 1 percent) and a credit boom (interaction of high credit growth and loose FCI).
- Key observations from the example (Figure 9, panel 1):
  - Near term (projection quarter h = 4, gold line): higher median and lower variance than medium term.
  - Medium term (projection quarter h = 10, green line): lower median, higher variance, and a much fatter left tail.
  - Downside risk is much greater at ten quarters ahead than at four quarters ahead.
- Term structure construction (Figure 9, panel 2):
  - For each forecast quarter h, estimate the forecast distribution and plot the 5th percentile to trace GaR over time.
  - The term structure conditional on high FCI and a credit boom shows lower downside risks in near‑term quarters than in quarters further ahead; the lower tail gets fatter over time.

### Financial conditions, distributional effects, and limitations
- Financial conditions disproportionately affect the lower tail of the forecast growth distribution.
- Sign switching over horizon:
  - Initially looser financial conditions reduce downside risks to growth in the near term.
  - The same looser conditions increase downside growth risks in the medium term.
- Robustness: Results are robust to other estimation techniques.
- Limitations:
  - Lack of data or less significant effects of financial sector risk‑taking and credit on expected growth limits applicability to every country.

### Global GaR: aggregate monitoring and recent assessments
- Global GaR implementation:
  - Global GaR estimated using global GDP and a global FCI; used to communicate aggregate top‑down financial stability risks in April 2018 (Chapter 1 of the GFSR).
  - Methodology introduced earlier in Chapter 3 of the GFSR in October 2017.
  - Global GDP = aggregate GDP for 43 countries.
  - Global FCI based on FCIs for these areas, including the 29 jurisdictions with systemically important financial sectors.
- April 2019 GFSR findings:
  - Global FCI loosened from the previous quarter, though not as loose as 2018:Q3.
  - Conditional on global FCI, one‑year‑ahead forecast densities suggested an improvement in downside risks relative to the previous quarter.
  - One‑year‑ahead GaR forecast rose to near the upper range of its historical distribution.
  - Three‑year‑ahead GaR forecasts are less favorable but indicate downside risks less severe than in late 2017 (when very loose global financial conditions existed).
- Identifying contributors to medium‑term downside risks:
  - Monitoring matrices can highlight contributors; one likely contributor is greater indebtedness of borrowers.
  - Spider map and heat map of financial vulnerabilities (Figure 6) highlight elevated sovereign debt, household debt, and nonfinancial business debt in a number of regions, and high leverage at banks and other financial firms in China.

### Improving global GaR and systemic contribution measures
- Two avenues for improvement:
  - Include indicators of financial vulnerability (as in Figure 6) in global GaR estimates to assess which vulnerabilities present significant risks.
  - Account for varying contributions by individual countries beyond GDP weights due to differences in interconnection or financial‑center significance.
- Possible methodology:
  - Use Adrian and Brunnermeier (2016) conditional value at risk method to estimate systemic risk contributions (conditional variance of risk as a metric for systemic risk contribution, based on value at risk).

### Macroprudential policy tools, survey findings, and research
- IMF macroprudential guidance:
  - IMF guidance note (2014) links types of financial vulnerabilities to macroprudential tools (matrix in Figure 4).
  - The multilateral framework can foster better global policies even when implemented domestically.
- IMF survey on macroprudential policies (IMF 2018):
  - Launched as a new annual survey; initial survey sent in early 2017.
  - Tools mapped to vulnerabilities include those addressing banks’ credit exposures, liquidity and FX mismatches of banks, liquidity and fire‑sale risks of nonbanks, and risks from systemically important financial institutions.
  - Survey reports on institutional frameworks: roles of central banks, financial regulators, and financial stability committees.
- Survey findings:
  - Many countries have authority and used structural macroprudential tools to reduce balance sheet vulnerabilities at banks.
  - Most countries report availability of tools to manage leverage and liquidity in the banking sector and insurers (Figure 11).
  - Tools to manage banks’ risks from exposures to households and businesses are commonly reported.
  - For liquidity and FX mismatches, the liquidity coverage ratio, net stable funding ratio, and net foreign exchange positions were the three most used tools.
  - Fewer countries reported authority or use of tools to address maturity and FX mismatch vulnerabilities of nonbank lenders or financial markets.
  - Systematic reporting of authorities’ matches to vulnerabilities and use of tools by countries with elevated vulnerabilities is important for GFSR multilateral surveillance.
- Research on effectiveness:
  - Cerutti, Claessens, and Laeven (2015): borrower‑based tools (loan to value and debt service to income) can significantly reduce household credit growth; effects smaller in open economies; some evidence of avoidance via cross‑border borrowing.
  - Akinci and Olmstead‑Rumsey (2018): tightening loan to value, debt service to income, and other housing measures have significant effects on credit and house prices, mostly in emerging market economies.
- Institutional structures:
  - Survey responses from 111 countries that report having a macroprudential authority:
    - 80 countries indicate a significant role for the central bank, consistent with IMF principles on macroprudential policy.
  - Recent research documents the rapid growth of multiagency financial stability committees since the global financial crisis and an increased role of the Ministry of Finance in setting policies (Edge and Liang 2019).
  - Wide range of practices and limited evidence on effectiveness highlight the need to better understand motivations for structures and implications for policymaking.
- Ongoing and future research:
  - Research to allow counterfactual policy analysis for joint setting of macroprudential and monetary policies.
  - Adrian and Duarte (2017): adding financial vulnerability to a New Keynesian model shows optimal monetary policy differs from a standard Taylor rule even when pursuing an inflation target.
  - Additional work to add macroprudential policy is ongoing (Adrian 2018).

### Conclusions and policy implications
- The conceptual framework in the GFSR for multilateral surveillance:
  - Evolving to enhance transparency and communication on cyclical financial stability risks.
  - Grounded in macro‑financial linkages linking the financial sector to macroeconomic growth and stability.
  - Emphasizes looking ahead for different manifestations of vulnerabilities as financial integration increases.
- GaR's contribution:
  - Emphasizes attention to the entire distribution of forecast growth from financial conditions rather than single point estimates.
  - Emphasizes how forecast risks to growth evolve over time: loosening financial conditions can boost near‑term growth and reduce volatility but incentivize risk‑taking that builds vulnerabilities and raises medium‑term downside risks.
- Monitoring framework and macroprudential policy:
  - Consistent metrics improve bilateral and multilateral surveillance and communication among macroprudential policymakers.
  - Better data on the use and effectiveness of macroprudential tools supports systematic assessments and policy implementation to reduce financial stability risks.

*Source: IMF, April 2019 Global Financial Stability Report.*

### REFERENCES

### REFERENCES

### Systemic risk and financial stability
- Acharya, Viral V., Lasse H. Pedersen, Thomas Philippon, and Matthew Richardson. 2017. “Measuring Systemic Risk.” Review of Financial Studies 30 (1): 2–47.  
- Adrian, Tobias. 2018. “Alternative Monetary Policy Paths and Downside Risk.” Remarks at European Central Bank Conference on Monetary Policy, October 29.  
- Adrian, Tobias, and Nina Boyarchenko. 2016. “Intermediary Leverage Cycles and Financial Stability.” Federal Reserve Bank of New York Staff Report 567.  
- Adrian, Tobias, and Domenico Giannone. 2017. “Vulnerable Growth.” Federal Reserve Bank of New York Staff Report 794, American Economic Review, forthcoming.  
- Adrian, Tobias, and Markus Brunnermeier. 2016. “CoVar.” American Economic Review 106 (7): 1705–41.  
- Adrian, Tobias, Daniel Covitz, and Nellie Liang. 2015. “Financial Stability Monitoring.” Annual Review of Financial Economic 7 (December): 357–95.  
- Adrian, Tobias, and Fernando Duarte. 2017. “Financial Vulnerability and Monetary Policy.” Federal Reserve Bank of New York Staff Report 804. Revised September 2017.  
- Adrian, Tobias, and Nellie Liang. 2018. “Monetary Policy, Financial Conditions, and Financial Stability.” International Journal of Central Banking 14 (1): 73–131.  
- Adrian, Tobias, Hyun Song Shin. 2010. “The Changing Nature of Financial Intermediation and the Financial Crisis of 2007–09.” Federal Reserve Bank of New York Staff Report 439. Revised April 2010.  
- Adrian, Tobias, Hyun Song Shin. 2014. “Procyclical Leverage and Value-at-Risk.” Review of Financial Studies 27 (2): 373–403.  
- Blancher, Nicolas, Srobona Mitra, Hanan Morsy, Akira Otani, Tiago Severo, and Laura Balderrama. 2013. “Systemic Risk Monitoring (SysMo) Toolkit—A User Guide.“ IMF Working Paper 13/168, International Monetary Fund, Washington, DC.  
- Borio, Claudio, Mathias Drehmann, and Kostas Tsatsaronis. 2011. “Anchoring Countercyclical Capital Buffers: The Role of Credit Aggregates.” International Journal of Central Banking 7 (4): 189–240.  
- Bruno, Valentina, and Hyun Song Shin. 2014. “Cross-Border Banking and Global Liquidity.” Review of Economic Studies 82 (2): 535–64.  
- Caballero, Ricardo, and Arvind Krishnamurthy. 2008. “Collective Risk Management in a Flight to Quality Episode.” Journal of Finance 63 (5, October): 2195–230.  
- Gennaioli, Nicola, and Andrei Shleifer. 2018. A Crisis of Beliefs: Investor Psychology and Financial Fragility. Princeton, NJ: Princeton University Press.  
- Green wood, Robin, Augustin Landier, and David Thesmar. 2015. “Vulnerable Banks.” Journal of Financial Economics 115 (3, March): 471–85.  
- Jin, Xisong, and Francisco de A. Nadal De Simone. 2014. “Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach.” Journal of Financial Stability 14 (October): 81–101.  
- Edge, Rochelle, and Nellie Liang. 2019. “New Financial Stability Governance Structures and Central Banks.” Hutchins Center Working Paper 50, Brookings Institution, Washington, DC. Update of Hutchins Center Working Paper 32, published in 2017 with the same title.

### Macroprudential policy, regulation, and spillovers
- Agenor, Pierre-Richard, and Luiz Pereira da Silva. 2018. “Financial Spillovers, Spillbacks, and the Scope for International Macroprudential Policy Coordination.” BIS Paper 97, Bank for International Settlements, Basel.  
- Aikman, David, Jonathan Bridges, Stephen Burgess, Richard Galletly, Iren Levina, Cian O’Neill, and Alexandra Varadi. 2018. “Measuring Risks to Financial Stability.” Bank of England Working Paper 738, London.  
- Akinci, Ozge, and Jane Olmstead-Rumsey. 2018. “How Effective Are Macroprudential Policies? An Empirical Investigation.” Journal of Financial Intermediation 33 (C): 33–57.  
- Cerutti, Eugenio, Stijn Claessens, and Luc Laeven. 2015. “The Use and Effectiveness of Macroprudential Policies: New Evidence.” IMF Staff Discussion Note 15/61, International Monetary Fund, Washington, DC.  
- Cerutti, Eugenio, Ricardo Correa, Elisabetta Fiorentino, and Esther Segalla. 2016. “Changes in Prudential Policy Instruments—A New Cross-Country Database.” International Finance Discussion Paper 1169, Board of Governors of the Federal Reserve System.  
- De Nicolo, Gianni, Giovanni Favara, and Lev Ratnovski. 2012. “Externalities and Macroprudential Policy.” IMF Staff Discussion Note 12/05, International Monetary Fund, Washington, DC.  
- International Monetary Fund (IMF). 2014. Staff Guidance Note on Macroprudential Policy, Washington, DC.  
- International Monetary Fund (IMF). 2018. The IMF’s Annual Macroprudential Policy Survey–Objectives, Design, and Country Responses. Washington, DC: International Monetary Fund.

### Monetary policy, financial conditions, and unconventional policy
- Ang, Andrew, Monika Piazzesi, and Min Wei. 2006. “What Does the Yield Curve Tell Us about GDP Growth?“ Journal of Econometrics 131 (1–2): 359–403.  
- Bernanke, Ben S., and Alan S. Blinder. 1992. “The Federal Funds Rate and the Channels of Monetary Transmission.” American Economic Review 82 (4): 901–21.  
- Bernanke, Ben, and Mark Gertler. 1989. “Agency Costs, Net Worth, and Business Fluctuations.” American Economic Review 79 (1): 14–31.  
- Bernanke, Ben, and Simon Gilchrist. 1999. “The Financial Accelerator in a Quantitative Business Cycle Framework.” In Handbook of Macroeconomics, vol. 1, edited by J. B. Taylor and M. Woodford, 1341–93. Amsterdam: Elsevier Science.  
- Gertler, Mark, and Peter Karadi. 2010. “A Model of Unconventional Monetary Policy.” Journal of Monetary Economics 58 (1): 17–34.  
- Gertler, Mark, and Nobuhiro Kiyotaki. 2009. “Financial Intermediation and Credit Policy in Business Cycle Analysis.” In Handbook of Monetary Economics, vol. 3, edited by B. M. Friedman and M. Woodford, 547–99. Amsterdam: Elsevier Science.  
- Morris, Stephen, and Hyun Song Shin. 2014. “Risk-Taking Channel of Monetary Policy: A Global Game Approach.” Unpublished working paper, Princeton University, Princeton, NJ.  
- Adrian, Tobias, Tomaso Mancini-Griffoli, and Federico Grinberg. 2018. “Financial Conditions.” In Advancing the Frontiers of Monetary Policy Making, edited by T. Adrian, D. Laxton, and M. Obstfeld, Washington, DC: International Monetary Fund.  
- Adrian, Tobias, Federico Grinberg, Nellie Liang, and Sheheryar Malik. 2018. “The Term Structure of Growth at Risk.” IMF Working Paper 18/180, International Monetary Fund, Washington, DC.  
- Prasad, Ananthakrishnan, Selim Elekdag, Phakawa Jeasakul, Romain Lafarguette, Adrian Alter, Alan Xiaochen Feng, and Changchun Wang. 2019. “Growth at Risk: Concept and Application in IMF Country Surveillance.” IMF Working Paper 19/36, International Monetary Fund, Washington, DC.  
- Adrian, Tobias, and Domenico Giannone. 2017. “Vulnerable Growth.” Federal Reserve Bank of New York Staff Report 794, American Economic Review, forthcoming.

### Asset prices, credit, leverage, and business cycles
- Campbell, John. 1999. “Asset Prices, Consumption, and the Business Cycle.” In Handbook of Macroeconomics, vol. 1, edited by J. B. Taylor and M. Woodford, 1231–303. Amsterdam: Elsevier Science.  
- Dell’Ariccia, Giovanni, and Robert Marquez. 2006. “Lending Booms and Lending Standards.” Journal of Finance 61 (5): 2511–46.  
- Geanakoplos, John. 2009. “The Leverage Cycle.” In NBER Macroeconomics Annual 2009, vol. 24, edited by Daron Acemoglu, Kenneth Rogoff, and Michael Woodford, 1–66. University of Chicago Press.  
- Gilchrist, Simon, and Egon Zakrajšek. 2012. “Credit Spreads and Business Cycle Fluctuations.” American Economic Review 102 (4): 1692–720.  
- He, Zhiguo, and Arvind Krishnamurthy. 2013. “Intermediary Asset Pricing.” American Economic Review 103 (2): 1–42.  
- Jordà, Òscar, Moritz Schularick, and Alan M. Taylor. 2013. “When Credit Bites Back.” Journal of Money, Credit and Banking 45 (2): 3–28.  
- Korinek, Anton, and Alp Simsek. 2016. “Liquidity Trap and Excessive Leverage.” American Economic Review 106 (3): 699–738.  
- López-Salido, David, Jeremy C. Stein, and Egon Zakrajšek. 2017. “Credit-market Sentiment and the Business Cycle.” Quarterly Journal of Economics 132 (3): 1373–426.  
- Minsky, Hyman P. 1986. Stabilizing an Unstable Economy. New Haven, CT: Yale University Press.  
- Philippon, Thomas. 2009. “The Bond Market’s q*.” Quarterly Journal of Economics 124 (3): 1011–56.  
- Pflueger, Carolin, Emil Siriwardane, and Adi Sunderam. 2018. “A Measure of Risk Appetite for the Macroeconomy.” NBER Working Paper 24529, National Bureau of Economic Research, Cambridge, MA.

### Measures, indices, and econometric methods
- Adrian, Tobias, and Markus Brunnermeier. 2016. “CoVar.” American Economic Review 106 (7): 1705–41.  
- Ang, Andrew, Monika Piazzesi, and Min Wei. 2006. “What Does the Yield Curve Tell Us about GDP Growth?“ Journal of Econometrics 131 (1–2): 359–403.  
- Koop, Gary, and Dimitris Korobilis. 2014. “A New Index of Financial Conditions.” European Economic Review 71 (C): 101–16.  
- Jin, Xisong, and Francisco de A. Nadal De Simone. 2014. “Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach.” Journal of Financial Stability 14 (October): 81–101.  
- Primiceri, Giorgio E. 2005. “Time Varying Structural Vector Autoregressions and Monetary Policy.” Review of Economic Studies 72 (3): 821–52.  
- Stock, James, and Mark Watson. 2003. “Forecasting Output and Inflation: The Role of Asset Prices.” Journal of Economic Literature 41 (3, September): 788–829.  
- Pflueger, Carolin, Emil Siriwardane, and Adi Sunderam. 2018. “A Measure of Risk Appetite for the Macroeconomy.” NBER Working Paper 24529, National Bureau of Economic Research, Cambridge, MA.

*sdnea2019006 - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/sdn/2019/sdnea2019006.pdf_