## Disconnect between Emerging Market Hard Currency and Local Currency Bonds

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**Canonical URL:** [Disconnect between Emerging Market Hard Currency and Local Currency Bonds](https://www.imf.org/-/media/files/publications/gfs-notes/2021/english/gfsnea2021004.pdf)

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---

### Section 1 — Key findings on the disconnect and context
- Market events and recovery:
  - Emerging markets experienced record high portfolio outflows, credit rating downgrades, and sizable sell-offs at the beginning of the COVID-19 pandemic; subsequent global central bank actions led to a sharp revival in global risk sentiment and recovery in funding costs and portfolio flows.
  - Since the pandemic shock, recovery has been uneven:
    - Local currency bond yields rose rapidly in 2021 and are now higher than their pre-pandemic level (as of the end of 2019).
    - Hard currency bond yields (and spreads) declined significantly and have been relatively stable, especially among higher-rated issuers.
    - Portfolio flows: flows to emerging market hard currency bonds strengthened since their bottom in mid-2020, while flows to emerging market local currency bonds lagged significantly through the pandemic recovery period.
    - Exception: China — local currency bond flows remained very strong, driven in part by inclusion in global benchmark indices.
- Implications:
  - Local currency bond markets remain the major funding source for emerging markets amid elevated fiscal deficits.
  - Weak flows and local bond pressures increase reliance on central bank interventions (e.g., asset purchases), potentially exacerbating the financial–sovereign nexus.
- Contribution of the paper:
  - Constructs comparable fundamentals-based asset valuation models for hard currency spreads (EMBIG) and local currency yields (GBI-EM).
  - Deploys capital flows at-risk (quantile) forecasting to assess portfolio flow drivers and prospects.

*Source: IMF staff analysis in the presented note.*

### Section 2 — Asset valuation models for hard vs. local currency bonds

- Purpose:
  - Build fundamentals-based valuation models linking bond funding costs to domestic fundamentals and external financial conditions for both asset classes to allow consistent comparison and mispricing assessment.

- Model coverage and data spans:
  - Emerging Market Hard Currency Bonds:
    - Covers 65 emerging and frontier markets across five major regions.
    - Quarterly data spanning back almost 25 years to December 1996 (uneven entry into EMBIG).
  - Emerging Market Local Currency Bonds:
    - Covers 21 emerging and frontier markets.
    - Quarterly data spanning back to December 2001 (uneven entry into GBI-EM).

- Conceptual model specifications:
  - Hard currency:
    - Spread_i,t = c + Σ Fundamental_k,i,t * β_k + Σ GlobalRiskAppetite_j,t * α_j * Rating_j
    - i: countries (from 1 to 71 in description)
    - k: fundamental factors (from 1 to 7)
    - j: ratings (from 1 to 8; AAA, AA, A, BBB, BB, B, and CCC)
  - Local currency:
    - Yield_i,t = c + Σ Fundamental_k,i,t * β_k + Σ GlobalRiskAppetite_j,t * α_j * Rating_j
    - i: countries (from 1 to 21)
    - k: fundamental factors (from 1 to 7)
    - j: rating types (from 1 to 2; Investment grade vs High Yield)

- Fundamental determinants included (hard and local models; local model includes two additional factors):
  1. Domestic Real GDP growth — 1 year forward consensus forecasts
  2. Domestic CPI Inflation — 1 year forward consensus forecasts
  3. Current Account Balance (percent of GDP)
  4. External Debt (percent of GDP)
  5. Net Issuance of Foreign Currency Government Debt (percent of GDP) [hard currency model]
  6. Foreign Currency Reserves (percent of GDP)
  7. External Real GDP growth — 1 year forward consensus forecasts
  - Additional for local currency model:
    7. US Dollar Index (DXY Index)
    8. Foreign investors as a proportion of total ownership

- Other model details:
  - Global risk appetite proxy: US BBB-rated corporate spread.
  - Estimation approach: OLS on an unbalanced panel; “Rating” is a dummy indicating IG vs HY for local model (not an interaction).

### Section 2.3 — Main model results and comparative sensitivities

- Emerging Market Hard Currency Bonds:
  - Final model adjusted R^2: almost 50 percent.
  - Domestic fundamentals matter: higher real GDP growth and lower inflation reduce spreads; higher reserves and lower external debt compress spreads.
  - Global risk aversion impact by rating:
    - A 100-basis points increase in US BBB corporate bond spreads could widen spreads of B-rated emerging market bonds by more than 200 basis points, compared to only 50 basis points for A-rated issuers.
  - Rolling regressions:
    - Sensitivity of spreads to external conditions has risen; sensitivity factor for a B-rated issuer was about 1.5x in 2015, which has risen by almost 40 percent to 2x now.
  - Implications:
    - Global stress episodes can cause sudden repricing and swift investor exodus, cutting off market access for lower-rated borrowers.
    - COVID-19-related ratings downgrades imply the emerging market universe may be more sensitive to external shocks on average.

- Emerging Market Local Currency Bonds:
  - Final model adjusted R^2: almost 70 percent.
  - Findings:
    - High inflation increases local currency bond yields; better growth prospects lower yields.
    - Elevated vulnerabilities and lower buffers (higher external debt, lower FX reserves) increase funding costs.
    - Sensitivity of local yields to foreign exchange reserves has increased in recent years; sensitivity to external debt has declined somewhat.
    - Lower-rated issuers are more vulnerable to swings in global investor risk sentiment.
    - Greater foreign participation reduces local currency yields, reflecting investor confidence and the role of foreign investors in market development.
  - Quantified sensitivities:
    - Every percentage point rise in inflation increases local currency bond spreads by more than 70 basis points, but by only 20 basis points for hard currency spreads.

- Comparative summary:
  - Hard currency spreads (especially HY issuers) are about 60 percent more affected by global risk aversion shocks than local currency spreads.
  - Local currency spreads are more sensitive to domestic vulnerabilities, including external debt and reserve adequacy.
  - GDP growth affects hard currency spreads more; inflation disproportionately increases local currency spreads.

*Source: Model estimation and IMF staff analysis presented in Section 2.*

### Section 3 — Drivers of hard currency and local currency capital flows

- Analysis approach:
  1. Capital flows at-risk (quantile regressions) using a quarter-century of data to project the distribution of capital flows over next four quarters.
  2. Post-COVID-19 recovery focus using a simple OLS regression (details in the note).

- Rationale:
  - Different flow types have different volatility/reversibility; debt flows are typically the most volatile. Within debt flows, drivers differ between local and hard currency debt.

- Capital flows at-risk model — methodology and variables:
  - Quantile regressions project entire distribution of flows; regressions include dummies for pre-global financial crisis, global financial crisis, post-global financial crisis, and country fixed effects.
  - Global “push” factor proxied by global financial conditions (IMF 2018).
  - Country-specific “pull” factors (domestic variables lagged):
    1. Ratio of short-term external debt to foreign exchange reserves (proxy for external balance-sheet vulnerabilities)
    2. Year-over-year real GDP growth rate
    3. Financial market depth index (level of domestic financial market development)
    4. Capital account openness (severity of restrictions on cross-border transactions)
    5. GDP growth per capita (economic development/domestic wealth)
  - Sample and data:
    - 15 large economies with sufficiently long data on local currency debt flows: Brazil, China, Egypt, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Peru, Poland, Russia, South Africa, Turkey, and Ukraine.
    - Hard currency debt flows calculated as residual from overall portfolio debt flows.
    - Quarterly flows from Q1 2000 to Q1 2019 (unbalanced sample).

- Quantile regression output:
  - Coefficients averaged across quantile ranges to summarize effects on:
    - Lower tail (average of 5th to 30th percentiles)
    - Median (average of 40th to 60th percentiles)
    - Upper tail (average of 70th to 95th percentiles)
  - Approach distinguishes drivers during typical vs. extreme flow periods (surges or reversals).

*Source: Capital flows at-risk methodology and sample description in Section 3.*

### Section 3.2 — Results for the Capital Flows At-Risk Model

- Overview:
  - Analysis corroborates findings from the drivers of the funding cost analysis: better domestic fundamentals and economic prospects improve the outlook for capital flows (across both types of flows), while vulnerabilities weigh on the capital flow outlook.
  - Sensitivity of capital flows varies significantly across capital flow regimes (typical flows versus extreme flows).
  - Drivers differ across local currency and hard currency debt flows.

- Key empirical findings (by driver and flow type):
  - Short-term debt and reserve adequacy:
    - Local currency debt flows appear to be more sensitive to domestic vulnerabilities than hard currency debt flows.
    - A higher level of short-term debt and weaker reserve adequacy significantly increase the likelihood of negative or weak inflows, especially for local currency flows.
    - Exception: local currency flows during surges, which reflects investor confidence in successful refinancing.
  - Domestic growth prospects:
    - Local currency debt flows are more sensitive to domestic growth prospects than hard currency debt flows, especially for the likelihood of extreme flows.
    - Higher growth boosts expected flows but affects the tails of the portfolio flow distribution twice as much.
    - Better growth prospects limit the likelihood of weak or negative inflows and amplify the likelihood of very large inflows.
    - The outlook for local currency flows is almost three times more sensitive to domestic growth than the outlook for hard currency flows.
    - Greater sensitivity of local currency bonds to domestic factors provides diversification for global investors (Miyajima, Mohanty, and Chan 2012).
  - Market depth (domestic financial markets):
    - Deeper domestic financial markets improve the outlook for both hard currency and local currency flows.
    - Deeper markets significantly limit the likelihood of negative or weak flows; local currency flows are more sensitive to market depth as well.
  - Global financial conditions (external risk sentiment):
    - Tighter global financial conditions decrease expected portfolio flows and have a disproportionately larger impact on the likelihood of extreme flows.
    - Hard currency flows are almost twice as sensitive as local currency flows to global financial conditions.
    - This likely reflects differences in the investor base: hard currency bonds are typically held by global investors, while local currency bond markets are typically dominated by domestic investors (benchmark-driven investors have a larger presence in hard currency than in local currency sovereign debt markets).

- Interpretation and implications:
  - A much weaker growth outlook for emerging markets due to the COVID-19 pandemic will significantly worsen the outlook for local currency flows.
  - The outlook for hard currency flows will be relatively more affected by sharp tightening in global financial conditions.
  - These implications align with observed trends in global financial markets since the COVID-19 sell-off (referenced to Section 1).

- Figures referenced:
  - Figure 4 presents coefficients for:
    1. Coefficient of Short-Term Debt/Reserves for Hard Currency and Local Currency Flows
    2. Coefficient of Domestic Growth for Hard Currency and Local Currency Flows
    3. Coefficient of Market Depth for Hard Currency and Local Currency Flows
    4. Coefficient of Global Financial Conditions for Hard Currency and Local Currency Flows
  - Sources cited for the figures: Bloomberg Finance L.P.; Haver Analytics; JPMorgan Chase & Co; IIF; and IMF staff calculations. IMF 2020 plots the detailed coefficients along with the standard errors.

*Source: Section 3.2, "Results for the Capital Flows At-Risk Model," gfsnea2021004*

### Section  1  of  this  note  discusses  the  disconnect  between  hard  currency  and  local  currency  bonds  and  portf

### Disconnect between Emerging Market Hard Currency and Local Currency Bonds

### Section 1 — Key findings on the disconnect and context
- Emerging markets experienced record high portfolio outflows, credit rating downgrades, and sizable sell-offs at the beginning of the COVID-19 pandemic; subsequent global central bank actions led to a sharp revival in global risk sentiment and recovery in funding costs and portfolio flows.
- Since the pandemic shock, recovery has been uneven:
  - Local currency bond yields rose rapidly in 2021 and are now higher than their pre-pandemic level (as of the end of 2019).
  - Hard currency bond yields (and spreads) declined significantly and have been relatively stable, especially among higher-rated issuers.
  - Portfolio flows: flows to emerging market hard currency bonds strengthened since their bottom in mid-2020, while flows to emerging market local currency bonds lagged significantly through the pandemic recovery period.
  - Exception: China — local currency bond flows remained very strong, driven in part by inclusion in global benchmark indices.
- Implications:
  - Local currency bond markets remain the major funding source for emerging markets amid elevated fiscal deficits.
  - Weak flows and local bond pressures increase reliance on central bank interventions (e.g., asset purchases), potentially exacerbating the financial–sovereign nexus.
- Contribution of this paper:
  1. Constructs comparable fundamentals-based asset valuation models for hard currency spreads (EMBIG) and local currency yields (GBI-EM).
  2. Deploys capital flows at-risk (quantile) forecasting to assess portfolio flow drivers and prospects.

*Source: IMF staff analysis in the presented note.*

### Section 2 — Asset valuation models for hard vs. local currency bonds

- Purpose: Build fundamentals-based valuation models linking bond funding costs to domestic fundamentals and external financial conditions for both asset classes to allow consistent comparison and mispricing assessment.

- Model coverage and data spans:
  - Emerging Market Hard Currency Bonds:
    - Covers 65 emerging and frontier markets across five major regions.
    - Quarterly data spanning back almost 25 years to December 1996 (uneven entry into EMBIG).
  - Emerging Market Local Currency Bonds:
    - Covers 21 emerging and frontier markets.
    - Quarterly data spanning back to December 2001 (uneven entry into GBI-EM).

- Hard currency model specification (conceptual):
  - Spread_i,t = c + Σ Fundamental_k,i,t * β_k + Σ GlobalRiskAppetite_j,t * α_j * Rating_j
  - i: countries (from 1 to 71 in description)
  - k: fundamental factors (from 1 to 7)
  - j: ratings (from 1 to 8; AAA, AA, A, BBB, BB, B, and CCC)
- Local currency model specification (conceptual):
  - Yield_i,t = c + Σ Fundamental_k,i,t * β_k + Σ GlobalRiskAppetite_j,t * α_j * Rating_j
  - i: countries (from 1 to 21)
  - k: fundamental factors (from 1 to 7)
  - j: rating types (from 1 to 2; Investment grade vs High Yield)

- Fundamental determinants included (hard and local models; local model includes two additional factors):
  1. Domestic Real GDP growth — 1 year forward consensus forecasts
  2. Domestic CPI Inflation — 1 year forward consensus forecasts
  3. Current Account Balance (percent of GDP)
  4. External Debt (percent of GDP)
  5. Net Issuance of Foreign Currency Government Debt (percent of GDP) [hard currency model]
  6. Foreign Currency Reserves (percent of GDP)
  7. External Real GDP growth — 1 year forward consensus forecasts
  - Additional for local currency model:
    7. US Dollar Index (DXY Index)
    8. Foreign investors as a proportion of total ownership

- Global risk appetite proxy: US BBB-rated corporate spread.
- Estimation approach: OLS on an unbalanced panel; “Rating” is a dummy indicating IG vs HY for local model (not an interaction).

Section 2.3 — Main model results and comparative sensitivities
- Emerging Market Hard Currency Bonds:
  - Final model adjusted R^2: almost 50 percent.
  - Domestic fundamentals matter: higher real GDP growth and lower inflation reduce spreads; higher reserves and lower external debt compress spreads.
  - Global risk aversion impact by rating:
    - A 100-basis points increase in US BBB corporate bond spreads could widen spreads of B-rated emerging market bonds by more than 200 basis points, compared to only 50 basis points for A-rated issuers.
  - Rolling regressions: sensitivity of spreads to external conditions has risen; sensitivity factor for a B-rated issuer was about 1.5x in 2015, which has risen by almost 40 percent to 2x now.
  - Implications:
    - Global stress episodes can cause sudden repricing and swift investor exodus, cutting off market access for lower-rated borrowers.
    - COVID-19-related ratings downgrades imply the emerging market universe may be more sensitive to external shocks on average.

- Emerging Market Local Currency Bonds:
  - Final model adjusted R^2: almost 70 percent.
  - Findings:
    - High inflation increases local currency bond yields; better growth prospects lower yields.
    - Elevated vulnerabilities and lower buffers (higher external debt, lower FX reserves) increase funding costs.
    - Sensitivity of local yields to foreign exchange reserves has increased in recent years; sensitivity to external debt has declined somewhat.
    - Lower-rated issuers are more vulnerable to swings in global investor risk sentiment.
    - Greater foreign participation reduces local currency yields, reflecting investor confidence and the role of foreign investors in market development.
  - Quantified sensitivities highlighted:
    - Every percentage point rise in inflation increases local currency bond spreads by more than 70 basis points, but by only 20 basis points for hard currency spreads.

- Comparative summary:
  - Hard currency spreads (especially HY issuers) are about 60 percent more affected by global risk aversion shocks than local currency spreads.
  - Local currency spreads are more sensitive to domestic vulnerabilities, including external debt and reserve adequacy.
  - GDP growth affects hard currency spreads more; inflation disproportionately increases local currency spreads.

*Source: Model estimation and IMF staff analysis presented in Section 2.*

### Section 3 — Drivers of hard currency and local currency capital flows

- Approach: Two complementary analyses
  1. Capital flows at-risk (quantile regressions) using a quarter-century of data to project the distribution of capital flows over next four quarters.
  2. Post-COVID-19 recovery focus using a simple OLS regression (details in the note).

- Rationale: Different flow types have different volatility/reversibility; debt flows are typically the most volatile. Within debt flows, drivers differ between local and hard currency debt.

- Capital flows at-risk model — methodology and variables:
  - Quantile regressions project entire distribution of flows; regressions include dummies for pre-global financial crisis, global financial crisis, post-global financial crisis, and country fixed effects.
  - Global “push” factor proxied by global financial conditions (IMF 2018).
  - Country-specific “pull” factors (domestic variables lagged):
    1. Ratio of short-term external debt to foreign exchange reserves (proxy for external balance-sheet vulnerabilities)
    2. Year-over-year real GDP growth rate
    3. Financial market depth index (level of domestic financial market development)
    4. Capital account openness (severity of restrictions on cross-border transactions)
    5. GDP growth per capita (economic development/domestic wealth)
  - Sample and data:
    - 15 large economies with sufficiently long data on local currency debt flows: Brazil, China, Egypt, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Peru, Poland, Russia, South Africa, Turkey, and Ukraine.
    - Hard currency debt flows calculated as residual from overall portfolio debt flows.
    - Quarterly flows from Q1 2000 to Q1 2019 (unbalanced sample).

- Output summary for quantile regressions:
  - Coefficients averaged across quantile ranges to summarize effects on:
    - Lower tail (average of 5th to 30th percentiles)
    - Median (average of 40th to 60th percentiles)
    - Upper tail (average of 70th to 95th percentiles)
  - Approach helps distinguish drivers during typical vs. extreme flow periods (surges or reversals).

*Source: Capital flows at-risk methodology and sample description in Section 3.*

_Italic: IMF staff note as presented in the referenced section of the PDF._

### Section 3.2 Results for the Capital Flows At-Risk Model

### Section 3.2 Results for the Capital Flows At-Risk Model

### Overview
- Analysis corroborates findings from the drivers of the funding cost analysis: better domestic fundamentals and economic prospects improve the outlook for capital flows (across both types of flows), while vulnerabilities weigh on the capital flow outlook.
- Sensitivity of capital flows varies significantly across capital flow regimes (typical flows versus extreme flows), consistent with the literature.
- Drivers differ across local currency and hard currency debt flows.

### Key empirical findings (by driver and flow type)
- Short-term debt and reserve adequacy
  - Local currency debt flows appear to be more sensitive to domestic vulnerabilities than hard currency debt flows.
  - A higher level of short-term debt and weaker reserve adequacy significantly increase the likelihood of negative or weak inflows, especially for local currency flows (Figure 4, chart 1).
  - Exception: local currency flows during surges, which reflects investor confidence in successful refinancing.
- Domestic growth prospects
  - Local currency debt flows are more sensitive to domestic growth prospects than hard currency debt flows, especially for the likelihood of extreme flows.
  - Higher growth boosts expected flows but affects the tails of the portfolio flow distribution twice as much (Figure 4, chart 2).
  - Better growth prospects limit the likelihood of weak or negative inflows and amplify the likelihood of very large inflows.
  - The outlook for local currency flows is almost three times more sensitive to domestic growth than the outlook for hard currency flows.
  - Greater sensitivity of local currency bonds to domestic factors provides diversification for global investors (Miyajima, Mohanty, and Chan 2012).
- Market depth (domestic financial markets)
  - Deeper domestic financial markets improve the outlook for both hard currency and local currency flows (Figure 4, chart 3).
  - Deeper markets significantly limit the likelihood of negative or weak flows; local currency flows are more sensitive to market depth as well.
- Global financial conditions (external risk sentiment)
  - Tighter global financial conditions decrease expected portfolio flows and have a disproportionately larger impact on the likelihood of extreme flows.
  - Hard currency flows are almost twice as sensitive as local currency flows to global financial conditions (Figure 4, chart 4).
  - This likely reflects differences in the investor base: hard currency bonds are typically held by global investors, while local currency bond markets are typically dominated by domestic investors (benchmark-driven investors have a larger presence in hard currency than in local currency sovereign debt markets).

### Interpretation and implications
- The analysis implies that:
  - A much weaker growth outlook for emerging markets due to the COVID-19 pandemic will significantly worsen the outlook for local currency flows.
  - The outlook for hard currency flows will be relatively more affected by sharp tightening in global financial conditions.
- These implications align with observed trends in global financial markets since the COVID-19 sell-off (referenced to Section 1).

### Figures referenced
- Figure 4 presents coefficients for:
  - 1. Coefficient of Short-Term Debt/Reserves for Hard Currency and Local Currency Flows
  - 2. Coefficient of Domestic Growth for Hard Currency and Local Currency Flows
  - 3. Coefficient of Market Depth for Hard Currency and Local Currency Flows
  - 4. Coefficient of Global Financial Conditions for Hard Currency and Local Currency Flows
- Sources cited for the figures: Bloomberg Finance L.P.; Haver Analytics; JPMorgan Chase & Co; IIF; and IMF staff calculations. IMF 2020 plots the detailed coefficients along with the standard errors.

*Source: Section 3.2, "Results for the Capital Flows At-Risk Model," gfsnea2021004*

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_Source: https://www.imf.org/-/media/files/publications/gfs-notes/2021/english/gfsnea2021004.pdf_