## 1. Financial conditions play a significant role in shaping business cycle fluctuations. They reflect

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---

### Overview and purpose
- Financial conditions indices (FCIs) aggregate domestic and external financial variables to capture the feedback of current and past economic conditions and markets’ expectations about the economic outlook.
- FCIs extend monetary conditions indices (MCIs) by including financial variables such as asset prices, long-term interest rates and liquidity indicators, improving detection of financial stress episodes (example: GFC and Covid-19 where monetary rates were low but financial conditions remained tight).
- In Qatar, an FCI supports the Third Financial Sector Strategy (FSS3) objectives by assessing financial health, gauging impacts of financial market deepening initiatives, and evaluating links between financial indicators and future growth distribution.

### Key methodological approaches
- Two methods used to construct Qatar’s FCI:
  - Principal components approach (PCA): extracts common factor(s) from a set of financial indicators; Bai and Ng (2002) selection criteria used to determine optimal number of common factors. The extracted factor F_t is regressed on current and lagged non-hydrocarbon GDP growth to purge past economic influences and isolate exogenous financial-condition developments.
  - Weighted sum VAR (WSA-VAR): weights derived from cumulative impulse-response functions of real non-oil GDP growth to a one-standard deviation shock to each variable, producing a weighted-average FCI from k standardized financial variables. Weights reflect relative importance measured from a recursive VAR framework using the cumulative 18 - 24 months impulse response for the period 2009 to 2023.
- Data treatment and model specifics:
  - Monthly conversion via Chow and Lin (1971).
  - Nominal variables deflated with GDP deflator.
  - Identification via Cholesky decomposition with assumptions: domestic financial conditions do not have contemporaneous effects on growth; domestic developments do not contemporaneously affect external variables.
  - Except for interest rates, variables entered as growth rates.
  - Lag length of one selected based on Schwartz Bayesian Criterion (SBC).
  - Stationarity confirmed with Augmented Dickey-Fuller tests.

### Variables included
- External factors: VIX (global financial market uncertainty), nominal effective exchange rate (NEER), oil prices.
- Domestic factors: real policy deposit rate, real market interest spreads (difference between real short-term lending and deposit rates), growth rates of credit to private sector, money supply proxied by broad money (M2), 5-year CDS spread (sovereign risk premia), stock price index, real estate price index.

### Findings on methodology performance
- Correlation between FCIs from WSA-VAR and PCA: about 0.86.
- WSA-VAR produced more consistent signs and better historical prediction of shocks (notably oil price shock in 2015, Covid-19 in 2020, post-pandemic easing, and a more distinct tightening in 2023) than PCA.
- PCA indicators contradicting priors were removed; retained indicators had smaller magnitudes.

### FCI behavior and decomposition
- Interpretation: an increase in the index = tightening of financial conditions; a decrease = loosening.
- Prior relationships:
  - Positive correlation (tightening) expected: real policy interest rate, real market spread, risk premium, NEER appreciation (given Qatar’s peg), VIX increases tighten conditions.
  - Negative correlation (easing) expected: money supply (M2), private sector credit, domestic stock and real estate price indices, higher oil prices for a hydrocarbon exporter.
- Decomposition (WSA-VAR) highlights main contributors over 2009–2023:
  - Significant contributors: real policy deposit rate, monetary conditions (credit and broad money), and external factors driven mainly by oil prices.
  - Example: 2008–09 GFC saw tightened financial conditions via equity declines, wider sovereign spreads, currency depreciation and tighter external conditions despite easy domestic monetary policy.

### Historical pattern for Qatar (selected years and phases)
- 2010–2014: trend of eased financial conditions aligned with post-GFC recovery.
- 2014: oil price collapse disrupted recovery and tightened financial conditions.
- 2017: regional blockade widened sovereign risk spreads and tightened conditions.
- 2020: Covid-19 shock compounded tight conditions through 2020.
- 2021–2022: brief post-pandemic easing as elevated hydrocarbon prices increased liquidity; nominal policy deposit rate increases in 2022 offset by higher inflation related to the World Cup.
- 2023: financial conditions tightened as the real policy deposit rate increased (with lower inflation), hydrocarbon prices softened, and global financial conditions deteriorated.

### Statistical relationships and predictive properties
- Correlation between FCI and real non-hydrocarbon GDP growth: about -0.66, indicating the FCI is a relatively strong negative correlate and potential leading indicator.
- Credit conditions component of the FCI closely mirrors Qatar Central Bank (QCB) bank lending survey index.
- Impulse response evidence: tightening FCIs negatively affect inflation and non-hydrocarbon GDP growth.
  - Estimated quantitative impacts:
    - Tightening FCIs can reduce inflation on average by 0.3 and up to 0.5 percentage points after 2 years.
    - Tightening FCIs can lead to a contraction in output by 0.8 to 1.0 percentage points.

### Growth-at-Risk (GaR) analysis: linking financial conditions to GDP growth risks
- Purpose: quantify how macro-financial conditions affect the entire probability distribution of future GDP growth, capturing downside and upside risks.
- GaR estimation steps:
  1. Partition financial condition indicators into subgroups using linear discriminant analysis (LDA).
  2. Estimate future output growth as a function of current conditions and partitioned indicators via quantile regressions.
  3. Fit a skewed t distribution to conditional quantile function to obtain a probability density and quantify downside tail risks.
- Partitioning and subcomponents:
  - Domestic conditions: real policy deposit rate, real market interest spread, money supply, stock prices, real estate prices, sovereign risk premia.
  - Credit conditions: credit growth and credit-to-non-hydrocarbon GDP gap.
  - External conditions: VIX, oil prices, NEER.
- GaR FCI alignment:
  - The GaR-generated FCI aligns closely with the WSA-VAR and PCA FCIs, with a bias toward the WSA-VAR FCI.
  - Domestic and external conditions dominate the FCI over the sample period.

### Methodology: quantile regressions and GaR fitting
- Quantile regression specification (future GDP growth on current conditions):
  - Regressors: current growth (yt), domestic conditions (dom_cond), credit conditions (credit_cond), external conditions (ext_cond).
  - Quantile levels: 0.10, 0.25, 0.50, 0.75, 0.90.
  - Forecast horizons: 4, 8 and 12 quarters.
- Credit to non-hydrocarbon GDP gap:
  - Defined as the deviation of the credit-to-non-hydrocarbon GDP ratio from trend.
  - Trend computed using the Hodrick-Prescott filter.
  - Results robust to using the credit impulse, defined as the ratio between the annual change in credit and the nominal non-hydrocarbon GDP of the previous year.
- Tail-risk quantification:
  - A t-skewed distribution was fitted to the empirical conditional quantile function for each forecast horizon (methodology per IMF 2017b).
  - Distributions calibrated so the mode aligns with IMF staff baseline for non-hydrocarbon growth: [1.5] percent for 2024 and [1.9] percent for 2025.
  - Probability density functions derived for 4 and 8 Quarters ahead (i.e., 2024 and 2025).

### Quantile regression findings: which conditions matter and when
- Domestic conditions:
  - Tight domestic conditions—primarily driven by high short-term interest rates—have a pronounced effect on non-hydrocarbon growth.
  - Result: decline in the lower quantiles of the GDP growth distribution over the next 4 to 8 quarters, shifting the distribution left more around the lower tail than around the median.
  - Effect diminishes over longer horizons as conditions improve.
- Credit conditions:
  - Deterioration of credit conditions (high funding costs and subdued demand) has led to credit growth falling below potential.
  - Effect is relatively minor and contributes to short-term risks; effects are anticipated to lessen in the medium term.
- External conditions:
  - Tight external conditions—mainly driven by commodity price fluctuations—have a significant negative impact on overall non-hydrocarbon growth outlook.
- Estimation detail:
  - Domestic, credit, and external financial conditions are included separately to assess relative significance for signaling near- and medium-term risks.

### Growth-at-Risk (GaR) distributional results and baseline risk assessment
- FCIs and approaches:
  - FCIs derived from WSA-VAR, PCA, and GaR approaches are closely aligned and exhibit a high correlation.
- Baseline downside risk (GaR model):
  - Under current IMF staff baseline distribution, the maximum expected non-hydrocarbon growth rate in a severely adverse scenario (GDP growth below the 5th percentile) would be:
    - 0.5 percent for 4 Quarters ahead.
    - 0.1 percent for 8 Quarters ahead.
  - Interpretation: relatively mild short- to medium-term downside risk to the baseline non-hydrocarbon growth projection.

### Scenario analyses: policy and external shocks
- Impact of monetary policy easing:
  - Assumption: a 100 basis-point reduction in the policy deposit rate by end of 2024, holding other factors constant.
  - Context: policy rates in Qatar generally follow US Fed rates given the peg to the US dollar.
  - Observed QCB action: The QCB reduced the policy rate by 0.55 bp following the 0.5bp cut in the US Fed rate in September 2024.
  - Effects:
    - 100 basis-point reduction improves average future growth with a rightward shift in the peak of the future growth distribution.
    - Significant reduction in GaR at the 5% percentile.
    - Maximum accommodative impact realized in the near term (around 0.4 percentage points higher); effects dissipate over the long term.
  - Note: shocks propagate non-linearly because beta coefficients differ by quantile.
- Impact of external conditions (oil prices and VIX):
  - One-standard-deviation negative shock to oil prices:
    - Aggravates risks to non-hydrocarbon growth, shifting the peak of the distribution left.
    - Magnitude: could cost about 0.3-0.4 percentage points of non-hydrocarbon growth for Qatar.
    - GaR at 5 percent could decline to about -0.1, implying increased downside risk.
  - One-standard-deviation increase in VIX (global financial market uncertainty):
    - Leads to a leftward shift in the peak of the future growth distribution and a slight worsening of GaR.
    - Overall impact is marginal, attributed to relatively less developed financial markets in Qatar and the VIX capturing only global financial market uncertainty.

### Conclusions and implications for surveillance and policy
- FCI utility and signals:
  - FCI is an important leading indicator of Qatar’s non-hydrocarbon growth and closely follows QCB’s bank lending survey.
  - Credit conditions component aligns with QCB’s bank lending survey, supporting consistency with other domestic indicators.
- Relative importance of condition types:
  - Domestic conditions provide the strongest short-term signal for non-hydrocarbon GDP growth.
  - External conditions are significant in both short and medium term, with oil prices being a primary driver.
  - Global financial market uncertainty (VIX) has minimal effect on non-hydrocarbon growth relative to oil price movements.
- Policy-relevant insights:
  - Current downside risks to Qatar’s baseline non-hydrocarbon growth projections are relatively mild.
  - Monetary easing (e.g., a 100 basis-point policy rate cut) could improve near-term non-hydrocarbon growth and materially reduce downside tail risks.
  - Commodity-price shocks (notably oil) pose meaningful downside risk to non-hydrocarbon growth and warrant monitoring.

*Source: sipea2025015.*

### 1. Financial conditions play a significant role in shaping business cycle fluctuations. They reflect

### 1. Financial conditions play a significant role in shaping business cycle fluctuations. They reflect

### Overview and purpose
- Financial conditions indices (FCIs) aggregate domestic and external financial variables to capture the feedback of current and past economic conditions and markets’ expectations about the economic outlook.
- FCIs extend monetary conditions indices (MCIs) by including financial variables such as asset prices, long-term interest rates and liquidity indicators, improving detection of financial stress episodes (example: GFC and Covid-19 where monetary rates were low but financial conditions remained tight).
- In Qatar, an FCI supports the Third Financial Sector Strategy (FSS3) objectives by assessing financial health, gauging impacts of financial market deepening initiatives, and evaluating links between financial indicators and future growth distribution.

### Key methodological approaches
- Two methods used to construct Qatar’s FCI:
  - Principal components approach (PCA): extracts common factor(s) from a set of financial indicators; Bai and Ng (2002) selection criteria used to determine optimal number of common factors. The extracted factor F_t is regressed on current and lagged non-hydrocarbon GDP growth to purge past economic influences and isolate exogenous financial-condition developments.
  - Weighted sum VAR (WSA-VAR): weights derived from cumulative impulse-response functions of real non-oil GDP growth to a one-standard deviation shock to each variable, producing a weighted-average FCI from k standardized financial variables. Weights reflect relative importance measured from a recursive VAR framework using the cumulative 18 - 24 months impulse response for the period 2009 to 2023.
- Data treatment and model specifics:
  - Monthly conversion via Chow and Lin (1971).
  - Nominal variables deflated with GDP deflator.
  - Identification via Cholesky decomposition with assumptions: domestic financial conditions do not have contemporaneous effects on growth; domestic developments do not contemporaneously affect external variables.
  - Except for interest rates, variables entered as growth rates.
  - Lag length of one selected based on Schwartz Bayesian Criterion (SBC).
  - Stationarity confirmed with Augmented Dickey-Fuller tests.

### Variables included
- External factors: VIX (global financial market uncertainty), nominal effective exchange rate (NEER), oil prices.
- Domestic factors: real policy deposit rate, real market interest spreads (difference between real short-term lending and deposit rates), growth rates of credit to private sector, money supply proxied by broad money (M2), 5-year CDS spread (sovereign risk premia), stock price index, real estate price index.

### Findings on methodology performance
- Correlation between FCIs from WSA-VAR and PCA: about 0.86.
- WSA-VAR produced more consistent signs and better historical prediction of shocks (notably oil price shock in 2015, Covid-19 in 2020, post-pandemic easing, and a more distinct tightening in 2023) than PCA.
- PCA indicators contradicting priors were removed; retained indicators had smaller magnitudes.

### FCI behavior and decomposition
- Interpretation: an increase in the index = tightening of financial conditions; a decrease = loosening.
- Prior relationships:
  - Positive correlation (tightening) expected: real policy interest rate, real market spread, risk premium, NEER appreciation (given Qatar’s peg), VIX increases tighten conditions.
  - Negative correlation (easing) expected: money supply (M2), private sector credit, domestic stock and real estate price indices, higher oil prices for a hydrocarbon exporter.
- Decomposition (WSA-VAR) highlights main contributors over 2009–2023:
  - Significant contributors: real policy deposit rate, monetary conditions (credit and broad money), and external factors driven mainly by oil prices.
  - Example: 2008–09 GFC saw tightened financial conditions via equity declines, wider sovereign spreads, currency depreciation and tighter external conditions despite easy domestic monetary policy.

### Historical pattern for Qatar (selected years and phases)
- 2010–2014: trend of eased financial conditions aligned with post-GFC recovery.
- 2014: oil price collapse disrupted recovery and tightened financial conditions.
- 2017: regional blockade widened sovereign risk spreads and tightened conditions.
- 2020: Covid-19 shock compounded tight conditions through 2020.
- 2021–2022: brief post-pandemic easing as elevated hydrocarbon prices increased liquidity; nominal policy deposit rate increases in 2022 offset by higher inflation related to the World Cup.
- 2023: financial conditions tightened as the real policy deposit rate increased (with lower inflation), hydrocarbon prices softened, and global financial conditions deteriorated.

### Statistical relationships and predictive properties
- Correlation between FCI and real non-hydrocarbon GDP growth: about -0.66, indicating the FCI is a relatively strong negative correlate and potential leading indicator.
- Credit conditions component of the FCI closely mirrors Qatar Central Bank (QCB) bank lending survey index.
- Impulse response evidence: tightening FCIs negatively affect inflation and non-hydrocarbon GDP growth.
  - Estimated quantitative impacts (from note):
    - Tightening FCIs can reduce inflation on average by 0.3 and up to 0.5 percentage points after 2 years.
    - Tightening FCIs can lead to a contraction in output by 0.8 to 1.0 percentage points.

### Growth-at-Risk (GaR) analysis: linking financial conditions to GDP growth risks
- Purpose: quantify how macro-financial conditions affect the entire probability distribution of future GDP growth, capturing downside and upside risks.
- GaR estimation steps:
  1. Partition financial condition indicators into subgroups using linear discriminant analysis (LDA).
  2. Estimate future output growth as a function of current conditions and partitioned indicators via quantile regressions.
  3. Fit a skewed t distribution to conditional quantile function to obtain a probability density and quantify downside tail risks.
- Partitioning and subcomponents:
  - Domestic conditions: real policy deposit rate, real market interest spread, money supply, stock prices, real estate prices, sovereign risk premia.
  - Credit conditions: credit growth and credit-to-non-hydrocarbon GDP gap.
  - External conditions: VIX, oil prices, NEER.
- GaR FCI alignment:
  - The GaR-generated FCI aligns closely with the WSA-VAR and PCA FCIs, with a bias toward the WSA-VAR FCI.
  - Domestic and external conditions dominate the FCI over the sample period.

### Implications for monitoring and policy
- FCIs serve as tools to detect financial tightening or easing episodes and to assess their probable impacts on inflation and non-hydrocarbon growth.
- The WSA-VAR FCI, by linking financial variables to their cumulative impact on non-hydrocarbon GDP growth, provides superior historical prediction of significant shocks and clearer decomposition for policy analysis.
- GaR framework complements FCI monitoring by quantifying downside tail risks to future GDP growth across horizons, useful for macroprudential and macroeconomic policy stance assessments.

*Source: sipea2025015.*

### 19. Financial conditions indicators were mapped on a probability distribution of future growth

### 19. Financial conditions indicators were mapped on a probability distribution of future growth outcomes

### Methodology: quantile regressions and GaR fitting
- Quantile regression specification (future GDP growth on current conditions):
  - Regressors: current growth (yt), domestic conditions (dom_cond), credit conditions (credit_cond), external conditions (ext_cond).
  - Quantile levels: 0.10, 0.25, 0.50, 0.75, 0.90.
  - Forecast horizons: 4, 8 and 12 quarters.
- Credit to non-hydrocarbon GDP gap:
  - Defined as the deviation of the credit-to-non-hydrocarbon GDP ratio from trend.
  - Trend computed using the Hodrick-Prescott filter.
  - Results robust to using the credit impulse, defined as the ratio between the annual change in credit and the nominal non-hydrocarbon GDP of the previous year.
- Tail-risk quantification:
  - A t-skewed distribution was fitted to the empirical conditional quantile function for each forecast horizon (methodology per IMF 2017b).
  - Distributions calibrated so the mode aligns with IMF staff baseline for non-hydrocarbon growth: [1.5] percent for 2024 and [1.9] percent for 2025.
  - Probability density functions derived for 4 and 8 Quarters ahead (i.e., 2024 and 2025).

### Quantile regression findings: which conditions matter and when
- Domestic conditions:
  - Tight domestic conditions—primarily driven by high short-term interest rates—have a pronounced effect on non-hydrocarbon growth.
  - Result: decline in the lower quantiles of the GDP growth distribution over the next 4 to 8 quarters, shifting the distribution left more around the lower tail than around the median.
  - Effect diminishes over longer horizons as conditions improve.
- Credit conditions:
  - Deterioration of credit conditions (high funding costs and subdued demand) has led to credit growth falling below potential.
  - Effect is relatively minor and contributes to short-term risks; effects are anticipated to lessen in the medium term.
- External conditions:
  - Tight external conditions—mainly driven by commodity price fluctuations—have a significant negative impact on overall non-hydrocarbon growth outlook.
- Estimation detail:
  - Domestic, credit, and external financial conditions are included separately to assess relative significance for signaling near- and medium-term risks.

### Growth-at-Risk (GaR) distributional results and baseline risk assessment
- FCIs and approaches:
  - FCIs derived from WSA-VAR, PCA, and GaR approaches are closely aligned and exhibit a high correlation.
- Baseline downside risk (GaR model):
  - Under current IMF staff baseline distribution, the maximum expected non-hydrocarbon growth rate in a severely adverse scenario (GDP growth below the 5th percentile) would be:
    - 0.5 percent for 4 Quarters ahead.
    - 0.1 percent for 8 Quarters ahead.
  - Interpretation: relatively mild short- to medium-term downside risk to the baseline non-hydrocarbon growth projection.

### Scenario analyses: policy and external shocks
- Impact of monetary policy easing:
  - Assumption: a 100 basis-point reduction in the policy deposit rate by end of 2024, holding other factors constant.
  - Context: policy rates in Qatar generally follow US Fed rates given the peg to the US dollar.
  - Observed QCB action: The QCB reduced the policy rate by 0.55 bp following the 0.5bp cut in the US Fed rate in September 2024.
  - Effects:
    - 100 basis-point reduction improves average future growth with a rightward shift in the peak of the future growth distribution.
    - Significant reduction in GaR at the 5% percentile.
    - Maximum accommodative impact realized in the near term (around 0.4 percentage points higher); effects dissipate over the long term.
  - Note: shocks propagate non-linearly because beta coefficients differ by quantile.
- Impact of external conditions (oil prices and VIX):
  - One-standard-deviation negative shock to oil prices:
    - Aggravates risks to non-hydrocarbon growth, shifting the peak of the distribution left.
    - Magnitude: could cost about 0.3-0.4 percentage points of non-hydrocarbon growth for Qatar.
    - GaR at 5 percent could decline to about -0.1, implying increased downside risk.
  - One-standard-deviation increase in VIX (global financial market uncertainty):
    - Leads to a leftward shift in the peak of the future growth distribution and a slight worsening of GaR.
    - Overall impact is marginal, attributed to relatively less developed financial markets in Qatar and the VIX capturing only global financial market uncertainty.

### Conclusions and implications for surveillance and policy
- Financial conditions index (FCI) utility:
  - FCI is an important leading indicator of Qatar’s non-hydrocarbon growth and closely follows QCB’s bank lending survey.
  - Credit conditions component aligns with QCB’s bank lending survey, supporting consistency with other domestic indicators.
- Relative importance of condition types:
  - Domestic conditions provide the strongest short-term signal for non-hydrocarbon GDP growth.
  - External conditions are significant in both short and medium term, with oil prices being a primary driver.
  - Global financial market uncertainty (VIX) has minimal effect on non-hydrocarbon growth relative to oil price movements.
- Policy-relevant insights:
  - Current downside risks to Qatar’s baseline non-hydrocarbon growth projections are relatively mild.
  - Monetary easing (e.g., a 100 basis-point policy rate cut) could improve near-term non-hydrocarbon growth and materially reduce downside tail risks.
  - Commodity-price shocks (notably oil) pose meaningful downside risk to non-hydrocarbon growth and warrant monitoring.

*Source: IMF staff calculations and analysis as presented in the chapter "Financial conditions indicators were mapped on a probability distribution of future growth outcomes."*

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_Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2025/english/sipea2025015.pdf_