## wpiea2020141-print-pdf

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---

### Background and motivation
- Bond markets play an increasingly central role in emerging financial systems; understanding drivers of market liquidity shortages is central to policy debates.
- A lack of liquidity can give rise to disorderly market conditions and magnify shocks; a deep and robust market can help absorb disturbances and safeguard stability.
- Little is known about drivers of bond market liquidity in emerging markets; this paper develops a comprehensive measure of market liquidity for the Mexican government bond market using transaction- and quote-level data.
- Two main questions:
  - What domestic and external developments accompany liquidity shifts?
  - Which investor types contribute to selling pressure during liquidity depletion, and which stabilize the market?

### Data and methodology
- Construct a daily time series of liquidity indicators from transaction- and order-flow data and combine them into a composite liquidity index.
- Unique feature: ability to identify end-investors in each transaction, allowing analysis of investor-type behavior during liquidity squeezes.
- Empirical approach:
  - Part 1: Daily regressions correlating the composite index with indicators of funding liquidity, price volatility in domestic and U.S. financial markets, surprises in economic news releases, and U.S. bond market conditions to identify spillovers.
  - Part 2: Identify the largest liquidity declines and compare each investor type’s net purchases in different market segments to a counterfactual.

### Market structure and key statistics
- Outstanding securities: about 300 billion U.S. dollars.
- Security composition of local government debt securities:
  - Mbonos: around 45 percent
  - Cetes: 13 percent
  - Udibonos: 23 percent
  - Bondes D: 19 percent
- Issuance and auction schedule:
  - Short Cetes tenors (182 days and less): issued every week
  - Bondes D: every two weeks
  - Long Cetes tenors, Udibonos and medium Mbono tenors (3-5 years): every four weeks
  - Long Mbono tenors (10-30 years): every six weeks
- Primary dealers and market intermediation:
  - 7 primary dealers for Mbonos and 4 for Udibonos who also invest on their own account.
  - Secondary market: roughly half of transactions between primary dealers and half between primary dealers and their clients.
  - Market open from 7:00 a.m. to 2:30 p.m. local time; higher concentration from 1pm to 2pm.
- Data coverage and sample:
  - Transaction dataset covers on-the-run and off-the-run Cetes, Mbonos, Udibonos and Bondes D since 2004; end investor entities grouped into: authorities, domestic banks, domestic brokerage houses, domestic development banks, domestic insurance companies, domestic mutual funds, domestic pension funds, other domestic investors, and foreign investors.
  - Transaction data estimated to account for 98 percent of total transactions on the Mexican local currency government bond market.
  - Quote dataset covers instruments since 2011 from domestic electronic platforms; estimate that from 2011 to 2019, some 30-40 percent of domestic secondary market transactions were executed via these platforms.
  - Focus sample period: January 2013-March 2020.
- Investor shares (2013–Mar2020, transactions):
  - Foreign investors: 11 percent
  - Domestic mutual funds: 4 percent
  - Pension funds: 3 percent
  - Development banks: 2 percent
  - Insurance funds: 1 percent
- Holdings:
  - Foreign investors account for more than half of total holdings of government securities; foreign investors hold 56 percent of Mbonos outstanding as of September 2019, concentrated in 5-10Y sector.

### Key findings (summary)
- Liquidity in the Mexican government bond market remained relatively stable during the sample period despite rising foreign ownership and regulatory changes.
- Economic news and financial market volatility are important drivers of bond market liquidity.
  - Close correlation between bond market liquidity and volatility in bond, equity, and foreign exchange markets.
  - Significant evidence that stress in U.S. bond markets translates into lower bond market liquidity in Mexico (spillover from external markets).
- Investor behavior during major liquidity squeezes:
  - Foreign investors consistently account for much of the selling pressure and tend to switch from less liquid segments into on-the-run securities when liquidity dries up.
  - Domestic banks are more likely to absorb pressures during liquidity squeezes, consistent with their role as primary dealers.
  - Domestic mutual funds contributed to market stability during most episodes.
  - Pension and insurance funds behaved opportunistically: at times supporting liquidity and at other times contributing to selling pressure.

### Market microstructure and trading activity (on-the-run bonds)
- Trade volume (means, billions of pesos per day):
  - 2-Year: 3.8
  - 3-Year: 5.9
  - 5-Year: 5.6
  - 10-Year: 10.5
  - 20-Year: 2.7
  - 30-Year: 4.4
- Trade frequency (means, number of trades per day):
  - 2-Year: 49.2
  - 3-Year: 190.0
  - 5-Year: 165.7
  - 10-Year: 448.2
  - 20-Year: 97.4
  - 30-Year: 148.7
- Trade size (means, millions of pesos):
  - 2-Year: 118.9
  - 3-Year: 86.9
  - 5-Year: 66.7
  - 10-Year: 44.3
  - 20-Year: 42.5
  - 30-Year: 38.4
- Key observations:
  - The 10-year note is the most widely traded (10.5 billion pesos/day), driven by high frequency rather than large average trade size.
  - The 2-year has low trading volume but the largest average trade size (118.9 million pesos).
  - During the COVID-19 crisis, the short end (2-year and 3-year notes) traded in large volumes.
- Data notes:
  - Trade volume standard deviations reported in source; trade volume shown in billions of pesos per day; trade size shown in millions of pesos.

### Composite liquidity index construction and components
- Market liquidity defined as the ability to rapidly buy or sell a sizable volume of securities at low cost with limited price impact.
- Three indicators constructed for on-the-run fixed rate securities:
  - Bid-ask spread (cost measure; daily average with equal tick weight; shown in basis points).
  - Order book depth (sum of quantities at best three bids and asks; daily averages with equal tick weight; shown in million pesos per day).
  - Price impact (Amihud ratio per one million peso of trading; daily maximum value used).
- Composite index methodology:
  - Average individual liquidity indicators across maturities to obtain market-level indicators.
  - Standardize each of the three indicators to mean zero and variance one.
  - Composite liquidity index for each market segment = simple average of bid-ask spread, inverse of order book depth, and price impact.
  - Overall liquidity index = simple average across market segments.
- Interpretation:
  - Higher values of individual indicators and the overall index indicate lower liquidity.

### Liquidity summary statistics (Jan 2013–Mar 2020)
- Bid-ask spread (means, basis points):
  - 2-Year: 2.6 (1.6)
  - 3-Year: 1.8 (1.4)
  - 5-Year: 1.8 (1.0)
  - 10-Year: 1.4 (0.7)
  - 20-Year: 2.2 (1.1)
  - 30-Year: 1.7 (1.1)
- Depth (means, million pesos per day):
  - 2-Year: 106.2 (93.3)
  - 3-Year: 138.8 (86.8)
  - 5-Year: 121.6 (65.4)
  - 10-Year: 167.3 (96.1)
  - 20-Year: 85.8 (33.7)
  - 30-Year: 104.0 (33.7)
- Price impact (means, per one million peso of trading):
  - 2-Year: 1.6 (20.5)
  - 3-Year: 4.0 (37.3)
  - 5-Year: 2.5 (13.3)
  - 10-Year: 3.9 (17.4)
  - 20-Year: 4.7 (23.3)
  - 30-Year: 5.9 (19.1)
- Cross-maturity correlations:
  - Average correlations (levels) of bid-ask spread across tenors: Avg. 0.37 (2-Year), 0.53 (3-Year), 0.49 (5-Year), 0.44 (10-Year), 0.47 (20-Year), 0.53 (30-Year).
  - Correlations are generally highest for the bid-ask spread and lowest for the price impact indicator.
- Overall finding:
  - Liquidity remained broadly stable over the sample period; liquidity squeezes were temporary except for the COVID-19 episode.

### Regression evidence: cyclical drivers of liquidity shifts
- Dependent variable: overall liquidity index (Regressions 1–9) and probability of low-liquidity state from a Markov regime-switching model (Regression 10).
- Key statistically significant explanatory variables (baseline results, coefficient and significance):
  - MEX Bond Market Volatility: 1.845*** (Regression 1)
  - MEX Equity Market Volatility: 0.490** (Regression 1)
  - MEX FX Market Volatility: 2.610*** (Regression 1)
  - U.S. Bond Market Volatility (MOVE): 0.243*** (Regression 1)
  - Latam Citi Surprise Index: -0.092*** (Regression 1)
- Alternative stress indicators:
  - U.S. TED spread: 26.191*** (significant individually, loses significance when included with MOVE)
  - U.S. High Yield spread: 3.916*** (significant individually, loses significance when included with MOVE)
- Funding liquidity measures:
  - MEX TED Spread and MEX Swap Spread not significant in joint specifications (Regressions 8 and 9).
  - No robust evidence that funding liquidity measures drive market liquidity in Mexico in the sample.
- Model fit:
  - Regression 1 R-squared = 0.175
  - Regression 10 R-squared = 0.270
- Interpretation:
  - Domestic financial market volatility (bond, equity, FX) and U.S. bond market volatility are robust correlates of reduced Mexican bond market liquidity.
  - Positive macroeconomic surprises for Latin America associated with increased liquidity.

### Liquidity resilience and episodes of large liquidity declines
- Episode identification:
  - Largest 5-day declines identified by comparing the composite liquidity index to a counterfactual from an ARMA(2,1) with calendar effects; the top 10 days with largest cumulative deviations selected (Episode 0–9; Episode 10 = COVID-19 handled separately).
  - Calendar effects include day-of-week, month dummies, last/first 5 working days, day before/after holiday, and auction day dummies.
- Episodes classified by driver:
  - External: Episode 0 (September 15, 2008, Lehman), Episode 1 (May 21, 2013, Taper Talk), Episode 2 (December 9, 2015, Trump poll), Episode 4 (November 8, 2016, Trump election).
  - Domestic: Episode 5 (December 16, 2020, Fuel-price concerns), Episode 6 (December 30, 2016, Fuel-price increases), Episode 7 (October 26, 2018, Airport cancellation), Episode 9 (January 17, 2019, Pipeline explosion).
  - Unclear/mixed: Episode 3 (September 8, 2018, resignation of Finance Secretary), Episode 8 (December 19, 2018, Remain in Mexico policy and U.S. MOVE increase).
- Magnitude of liquidity squeezes:
  - Across Episodes 1–9, the liquidity index peak increase averaged 1.9, equal to about 4.4 standard deviations.
  - Maximum increase: 3.2 (7.4 standard deviations) in Episode 2.
  - Minimum increase: 1.0 (2.4 standard deviations) in Episode 6.
  - The three externally driven shocks among the largest peak liquidity declines.
- Component heterogeneity:
  - Different episodes driven by different components: bid-ask spread important in Episodes 4, 7, 8; price impact crucial in Episodes 2, 3, 9; price impact negligible in Episodes 5, 6, 8.
- Investor behavior during episodes (5-day cumulative net purchases relative to counterfactual):
  - Foreign investors:
    - Contributed significantly to liquidity depletion in almost all episodes.
    - Often exhibited net purchases of benchmark fixed rate securities relative to counterfactual in about half the cases, indicating rotation into more liquid benchmarks.
  - Domestic banks:
    - More likely to increase holdings of government securities during shocks, especially benchmark fixed rate securities; often largest buyers relative to counterfactual.
  - Mutual funds:
    - Bought more than projected in about half the episodes and often shifted out of more liquid benchmark securities—suggesting price-sensitive, possibly stabilizing behavior.
  - Pension and insurance funds and development banks:
    - Long-term investors; behavior opportunistic—sometimes adding positions when pricing attractive, other times selling.

### COVID-19 episode (March 2020)
- Episode start: March 1; analysis through end of March.
- Liquidity index peaked at 2.7 on March 23.
- Index remained above 2 for 10 days and then hovered around 1 despite securities exchange auctions on March 13, 19 and 25.
- Investor flows:
  - Foreign investors sold substantial amounts of both benchmark fixed rate securities and the market as a whole.
  - Domestic banks provided liquidity to facilitate orderly exits for foreign investors.
  - Mutual funds contributed liquidity, primarily for non-benchmark securities.
  - Pension funds sold in the market as a whole and shifted into more liquid benchmark securities.
- Correlations:
  - Liquidity developments during COVID-19 correlated with volatility in Mexican financial markets; U.S. fixed income volatility contained by policy support.

### Appendix figures and selected table statistics
- Appendix Figure 1: Net Securities Purchases of Foreigners vs. IIF Bond Market Flow Data (time axis Jan 09 to Apr 20; vertical axis values -10,000; -5,000; 0; 5,000; 10,000; 15,000).
- Appendix Figure 2: Decomposing Liquidity Squeezes (episodes 1–9; t=-1 to t=5; series: "Liquidity index", "Price impact norm", "Bid ask norm", "Depth inverse norm"; panel ranges vary).
- Appendix Table 1: selected entries (preserving values exactly):
  - Market liquidity index
    - Obs: 1,600
    - Mean: -5.0
    - Std Dev: 36.2
    - Min: -69.4
    - Max: 430.3
    - Definition: Index multiplied by 100; higher values imply less liquidity
  - Probabiliy of high vol. state
    - Obs: 1,600
    - Mean: 0.1
    - Std Dev: 0.2
    - Min: 0.0
    - Max: 0.7
    - Definition: Probability calculated based on Markov regime-switching model on daily data of liquidity index, allowing for three states that differ in mean and variance.
  - MEX bond market volatility
    - Obs: 1,599
    - Mean: 4.8
    - Std Dev: 3.7
    - Min: 0.2
    - Max: 48.7
    - Definition: Standard deviation of daily 10-year bond yield between t-1 and t-5
  - US bond market volatility
    - Obs: 1,590
    - Mean: 65.2
    - Std Dev: 13.5
    - Min: 42.5
    - Max: 117.9
    - Definition: MOVE index
  - MEX TED spread
    - Obs: 1,591
    - Mean: 39.1
    - Std Dev: 12.3
    - Min: -25.0
    - Max: 91.5
    - Definition: 28-day TIIE interbank rate minus 28-day Cetes yield
  - Latam Citi surprise index
    - Obs: 1,592
    - Mean: -10.9
    - Std Dev: 36.4
    - Min: -152.6
    - Max: 59.6
    - Definition: Citi index of economic surprises in Latam

### Key policy-relevant findings and implications
- Market resilience:
  - Overall liquidity remained relatively stable in the sample despite rising foreign ownership and regulatory changes, with temporary squeezes before COVID-19.
- Role of foreign investors:
  - Foreign investor selling tends to drive aggregate selling pressure during stress episodes, but foreigners frequently rotate into more liquid benchmark securities rather than uniformly exiting benchmark fixed rate bonds.
  - High foreign ownership (56 percent of Mbonos) concentrated in the 5–10Y segment increases potential for synchronized flows in stress episodes.
- Domestic intermediaries as shock absorbers:
  - Domestic banks and primary dealers play a key role in absorbing selling pressure and providing liquidity during episodes.
- Heterogeneity of liquidity shocks:
  - Different liquidity episodes are driven by different components (bid-ask spread, depth, price impact); monitoring a single indicator can miss important aspects of stress.
- External spillovers:
  - U.S. bond market volatility (MOVE) and other U.S. stress indicators are significant correlates of Mexican bond market liquidity, implying external spillover channels matter.
- Funding liquidity:
  - No robust evidence that Mexican funding liquidity measures (MEX TED spread, MEX swap spread) drove market liquidity during the sample period.
- Crisis management tools:
  - Securities exchange auctions by Mexican authorities during COVID-19 correlated with later stabilization in liquidity, indicating market interventions can support liquidity in extreme episodes.

*Source: wpiea2020141-print-pdf - INTRODUCTION*

### INTRODUCTION

### INTRODUCTION

### Background and motivation
- Bond markets play an increasingly central role in emerging financial systems; understanding drivers of market liquidity shortages is central to policy debates.
- A lack of liquidity can give rise to disorderly market conditions and magnify shocks; a deep and robust market can help absorb disturbances and safeguard stability.
- Little is known about drivers of bond market liquidity in emerging markets; this paper develops a comprehensive measure of market liquidity for the Mexican government bond market using transaction- and quote-level data.

### Data and methodology
- Construct a daily time series of liquidity indicators from transaction- and order-flow data and combine them into a composite liquidity index.
- Unique feature: ability to identify end-investors in each transaction, allowing analysis of investor-type behavior during liquidity squeezes.
- Two main questions:
  - What domestic and external developments accompany liquidity shifts?
  - Which investor types contribute to selling pressure during liquidity depletion, and which stabilize the market?
- Empirical approach:
  - Part 1: Daily regressions correlating the composite index with indicators of funding liquidity, price volatility in domestic and U.S. financial markets, surprises in economic news releases, and U.S. bond market conditions to identify spillovers.
  - Part 2: Identify the largest liquidity declines and compare each investor type’s net purchases in different market segments to a counterfactual.

### Market structure and key statistics
- Outstanding securities of about 300 billion U.S. dollars, making the Mexican government bond market one of the largest emerging bond markets.
- Security composition of local government debt securities:
  - Mbonos: around 45 percent
  - Cetes: 13 percent
  - Udibonos: 23 percent
  - Bondes D: 19 percent
- Issuance and auction schedule highlights:
  - Short Cetes tenors (182 days and less): issued every week
  - Bondes D: every two weeks
  - Long Cetes tenors, Udibonos and medium Mbono tenors (3-5 years): every four weeks
  - Long Mbono tenors (10-30 years): every six weeks
- Primary dealers and market intermediation:
  - Currently 7 primary dealers for Mbonos and 4 for Udibonos who also invest on their own account.
  - Secondary market: roughly half of transactions between primary dealers and half between primary dealers and their clients.
  - Market open from 7:00 a.m. to 2:30 p.m. local time; higher concentration from 1pm to 2pm.
- Data coverage and sample:
  - Transaction dataset covers on-the-run and off-the-run Cetes, Mbonos, Udibonos and Bondes D since 2004 and includes end investor entities grouped into: authorities, domestic banks, domestic brokerage houses, domestic development banks, domestic insurance companies, domestic mutual funds, domestic pension funds, other domestic investors, and foreign investors.
  - Estimate that the transaction data account for 98 percent of total transactions on the Mexican local currency government bond market.
  - Quote dataset covers on-the-run and off-the-run instruments since 2011 from domestic electronic platforms; estimate that from 2011 to 2019, some 30-40 percent of domestic secondary market transactions were executed via these platforms.
  - Focus sample period: January 2013-March 2020 (order flow and pricing data appear lower quality prior to that).
- Investor shares (2013–Mar2020, transactions):
  - Foreign investors: 11 percent
  - Domestic mutual funds: 4 percent
  - Pension funds: 3 percent
  - Development banks: 2 percent
  - Insurance funds: 1 percent
- Holdings:
  - Foreign investors account for more than half of total holdings of government securities, despite trading share being 11 percent, indicating relative inactivity compared to domestic banks and brokerage firms.

### Key findings
- Liquidity in the Mexican government bond market remained relatively stable during the sample period despite rising foreign ownership and regulatory changes.
- Economic news and financial market volatility are important drivers of bond market liquidity.
  - Close correlation between bond market liquidity and volatility in bond, equity, and foreign exchange markets.
  - Significant evidence that stress in U.S. bond markets translates into lower bond market liquidity in Mexico (spillover from external markets).
- During the ten most dramatic liquidity squeezes (including the COVID-19 shock, the most dramatic and persistent in the sample), common patterns emerge:
  - Foreign investors consistently account for much of the selling pressure.
  - Foreign investors tend to switch from less liquid market segments to on-the-run securities when liquidity dries up.
  - Domestic banks are more likely to absorb pressures during liquidity squeezes, consistent with their role as primary dealers to provide orderly exits for non-resident investors.
  - Domestic mutual funds contributed to market stability during most episodes.
  - Other long-term oriented domestic investors (pension and insurance funds) behaved opportunistically: at times supporting liquidity and at other times contributing to selling pressure.

### Contributions to the literature
- First to calculate a fairly complete set of high-frequency liquidity indicators based on transaction- and order-flow data for an emerging bond market.
- Quantifies correlations between bond market liquidity in an emerging market and volatility in domestic financial markets, and estimates spill-over effects from external markets.
- Fills a gap by examining roles of different investor types during episodes of large liquidity squeezes.

### Policy implications
- Strengthens understanding of drivers of bond market liquidity shortages in emerging markets and provides a tool for bond market surveillance.
- Findings help refine regulatory tools and supervisory policies to promote market stability by:
  - Recognizing that domestic and international investors react differently to different shocks.
  - Emphasizing that a diverse investor base is key to ensure that selling pressure by some investors does not lead to a complete drying up of market liquidity.

*Source: wpiea2020141-print-pdf - INTRODUCTION*

### Appendix Figure 1 illustrates that net transactions of foreigners across securities correlate highly with IIF

### Appendix Figure 1 illustrates that net transactions of foreigners across securities correlate highly with IIF statistics on net bond market inflows

### Market microstructure and trading activity (on-the-run bonds)
- Trade volume (means, billions of pesos per day):
  - 2-Year: 3.8
  - 3-Year: 5.9
  - 5-Year: 5.6
  - 10-Year: 10.5
  - 20-Year: 2.7
  - 30-Year: 4.4
- Trade frequency (means, number of trades per day):
  - 2-Year: 49.2
  - 3-Year: 190.0
  - 5-Year: 165.7
  - 10-Year: 448.2
  - 20-Year: 97.4
  - 30-Year: 148.7
- Trade size (means, millions of pesos):
  - 2-Year: 118.9
  - 3-Year: 86.9
  - 5-Year: 66.7
  - 10-Year: 44.3
  - 20-Year: 42.5
  - 30-Year: 38.4
- Key observations:
  - The 10-year note is the most widely traded (10.5 billion pesos/day), driven by high frequency rather than large average trade size.
  - The 2-year has low trading volume but the largest average trade size (118.9 million pesos).
  - During the COVID-19 crisis, the short end (2-year and 3-year notes) traded in large volumes.
- Data notes:
  - Trade volume standard deviations (in parentheses) reported in source; trade volume shown in billions of pesos per day; trade size shown in millions of pesos.

### Composite liquidity index construction and components
- Definition and indicators:
  - Market liquidity defined as the ability to rapidly buy or sell a sizable volume of securities at low cost with limited price impact.
  - Three indicators constructed for on-the-run fixed rate securities:
    - Bid-ask spread (cost measure; daily average with equal tick weight; shown in basis points).
    - Order book depth (sum of quantities at best three bids and asks; daily averages with equal tick weight; shown in million pesos per day).
    - Price impact (Amihud ratio per one million peso of trading; daily maximum value used).
- Composite index methodology:
  - Average individual liquidity indicators across maturities to obtain market-level indicators.
  - Standardize each of the three indicators to mean zero and variance one.
  - Composite liquidity index for each market segment = simple average of bid-ask spread, inverse of order book depth, and price impact.
  - Overall liquidity index = simple average across market segments.
- Interpretation:
  - Higher values of individual indicators and the overall index indicate lower liquidity.

### Liquidity summary statistics (Jan 2013–Mar 2020)
- Bid-ask spread (means, basis points):
  - 2-Year: 2.6 (1.6)
  - 3-Year: 1.8 (1.4)
  - 5-Year: 1.8 (1.0)
  - 10-Year: 1.4 (0.7)
  - 20-Year: 2.2 (1.1)
  - 30-Year: 1.7 (1.1)
- Depth (means, million pesos per day):
  - 2-Year: 106.2 (93.3)
  - 3-Year: 138.8 (86.8)
  - 5-Year: 121.6 (65.4)
  - 10-Year: 167.3 (96.1)
  - 20-Year: 85.8 (33.7)
  - 30-Year: 104.0 (33.7)
- Price impact (means, per one million peso of trading):
  - 2-Year: 1.6 (20.5)
  - 3-Year: 4.0 (37.3)
  - 5-Year: 2.5 (13.3)
  - 10-Year: 3.9 (17.4)
  - 20-Year: 4.7 (23.3)
  - 30-Year: 5.9 (19.1)
- Cross-maturity correlations:
  - Average correlations (levels) of bid-ask spread across tenors: Avg. 0.37 (2-Year), 0.53 (3-Year), 0.49 (5-Year), 0.44 (10-Year), 0.47 (20-Year), 0.53 (30-Year) as reported in correlation tables.
  - Correlations are generally highest for the bid-ask spread and lowest for the price impact indicator.
- Overall finding:
  - Liquidity remained broadly stable over the sample period despite rising foreign ownership and regulatory changes; liquidity squeezes were temporary except for the COVID-19 episode.

### Regression evidence: cyclical drivers of liquidity shifts
- Dependent variable: overall liquidity index (Regressions 1–9) and probability of low-liquidity state from a Markov regime-switching model (Regression 10).
- Key statistically significant explanatory variables (baseline results, coefficient and significance):
  - MEX Bond Market Volatility: 1.845*** (Regression 1; t-statistic shown in table)
  - MEX Equity Market Volatility: 0.490** (Regression 1)
  - MEX FX Market Volatility: 2.610*** (Regression 1)
  - U.S. Bond Market Volatility (MOVE): 0.243*** (Regression 1)
  - Latam Citi Surprise Index: -0.092*** (Regression 1)
- Alternative stress indicators:
  - U.S. TED spread: 26.191*** (significant individually, loses significance when included with MOVE)
  - U.S. High Yield spread: 3.916*** (significant individually, loses significance when included with MOVE)
- Funding liquidity measures:
  - MEX TED Spread and MEX Swap Spread not significant in joint specifications (Regressions 8 and 9).
  - No robust evidence that funding liquidity measures drive market liquidity in Mexico in the sample.
- Model fit:
  - R-squared values reported: e.g., Regression 1 R-squared = 0.175; Regression 10 (probability of low-liquidity state) R-squared = 0.270.
- Interpretation:
  - Domestic financial market volatility (bond, equity, FX) and U.S. bond market volatility are robust correlates of reduced Mexican bond market liquidity.
  - Macroeconomic surprise index for Latin America (positive surprises) associated with increased liquidity.

### Liquidity resilience and episodes of large liquidity declines
- Episode identification:
  - Largest 5-day declines identified by comparing the composite liquidity index to a counterfactual from an ARMA(2,1) with calendar effects; the top 10 days with largest cumulative deviations selected (Episode 0–9; Episode 10 = COVID-19 handled separately).
  - Calendar effects include day-of-week, month dummies, last/first 5 working days, day before/after holiday, and auction day dummies.
- Episodes classified by driver:
  - External: Episode 0 (September 15, 2008, Lehman), Episode 1 (May 21, 2013, Taper Talk), Episode 2 (December 9, 2015, Trump poll), Episode 4 (November 8, 2016, Trump election).
  - Domestic: Episode 5 (December 16, 2020, Fuel-price concerns), Episode 6 (December 30, 2016, Fuel-price increases), Episode 7 (October 26, 2018, Airport cancellation), Episode 9 (January 17, 2019, Pipeline explosion).
  - Unclear/mixed: Episode 3 (September 8, 2018, resignation of Finance Secretary), Episode 8 (December 19, 2018, Remain in Mexico policy and U.S. MOVE increase).
- Magnitude of liquidity squeezes:
  - Across Episodes 1–9, the liquidity index peak increase averaged 1.9, equal to about 4.4 standard deviations.
  - Maximum increase: 3.2 (7.4 standard deviations) in Episode 2.
  - Minimum increase: 1.0 (2.4 standard deviations) in Episode 6.
  - The three externally driven shocks among the largest peak liquidity declines.
- Component heterogeneity:
  - Different episodes driven by different components: e.g., bid-ask spread important in Episodes 4, 7, 8; price impact crucial in Episodes 2, 3, 9; price impact negligible in Episodes 5, 6, 8.
- Investor behavior during episodes (5-day cumulative net purchases relative to counterfactual):
  - Foreign investors:
    - As a group, contributed significantly to liquidity depletion in almost all episodes (orange bars almost always negative).
    - Often exhibited net purchases of benchmark fixed rate securities relative to counterfactual (blue bars positive in about half the cases), indicating shifts from less liquid segments into more liquid benchmark securities.
    - Holding concentration: Foreign investors hold 56 percent of Mbonos outstanding as of September 2019; holdings concentrated in 5-10Y sector.
  - Domestic banks:
    - More likely to increase holdings of government securities during shocks, especially benchmark fixed rate securities; often largest buyers relative to counterfactual.
    - Behavior consistent with primary dealer obligations to provide liquidity.
  - Mutual funds:
    - Not uniformly selling; bought more than projected in about half the episodes and often shifted out of more liquid benchmark securities—suggesting price-sensitive, possibly stabilizing behavior.
  - Pension and insurance funds and development banks:
    - Long-term investors; behavior opportunistic—sometimes adding positions when pricing attractive, other times selling.
- COVID-19 episode (separate analysis):
  - Episode start: March 1 (analysis through end of March).
  - Liquidity index peaked at 2.7 on March 23.
  - Index remained above 2 for 10 days and then hovered around 1 despite securities exchange auctions on March 13, 19 and 25.
  - Investor flows:
    - Foreign investors sold substantial amounts of both benchmark fixed rate securities and the market as a whole.
    - Domestic banks again provided liquidity to facilitate orderly exits for foreign investors.
    - Mutual funds contributed liquidity, primarily for non-benchmark securities.
    - Pension funds sold in the market as a whole and shifted into more liquid benchmark securities.
  - Correlations:
    - Liquidity developments during COVID-19 correlated with volatility in Mexican financial markets; U.S. fixed income volatility contained by policy support.

### Key policy-relevant findings and implications
- Market resilience:
  - Overall liquidity remained relatively stable in the sample despite rising foreign ownership and regulatory changes, with temporary squeezes before COVID-19.
- Role of foreign investors:
  - Foreign investor selling tends to drive aggregate selling pressure during stress episodes, but foreigners frequently rotate into more liquid benchmark securities rather than uniformly exiting benchmark fixed rate bonds.
  - High foreign ownership (56 percent of Mbonos) concentrated in the 5–10Y segment increases potential for synchronized flows in stress episodes.
- Domestic intermediaries as shock absorbers:
  - Domestic banks and primary dealers play a key role in absorbing selling pressure and providing liquidity during episodes.
- Heterogeneity of liquidity shocks:
  - Different liquidity episodes are driven by different components (bid-ask spread, depth, price impact), so monitoring a single indicator can miss important aspects of stress.
- External spillovers:
  - U.S. bond market volatility (MOVE) and other U.S. stress indicators are significant correlates of Mexican bond market liquidity, implying external spillover channels matter.
- Funding liquidity:
  - No robust evidence that Mexican funding liquidity measures (MEX TED spread, MEX swap spread) drove market liquidity during the sample period.
- Crisis management tools:
  - Securities exchange auctions by Mexican authorities during COVID-19 correlated with later stabilization in liquidity, indicating market interventions can support liquidity in extreme episodes.

*Source: Authors' calculations and analysis in the IMF working paper (transactions and quote-level data, Jan 2013–Mar 2020; episode analysis including COVID-19 through March).*

### REFERENCES

### REFERENCES

### References
- Abreu, G., et al. "El Mercado de Valores Gubernamentales en México." Banco de México (2014).
- Adrian, T., M. J. Fleming, O. Shachar, and E. Vogt, 2017, “Market Liquidity After the Financial Crisis,” Annual Review of Financial Economics, 9(1), pp. 43-83.
- Adrian, T., M. J. Fleming, and E. Vogt, 2017, “An Index of Treasury Market Liquidity: 1991-2017”, Federal Reserve Bank of New York Staff Reports, 827.
- Amihud, Y., 2002, “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects”, Journal of financial markets, 5(1), pp.31-56.
- Arslanalp, S. and T. Tsuda, 2014, “Tracking Global Demand for Emerging Market Sovereign Debt", IMF Working Paper 14/39.
- Bank of International Settlements, 2007, “Financial Stability and Local Currency Bond Markets,” CGFS Papers, 28.
- Bank of International Settlements, 2019, “Sixteenth Progress Report on Adoption of the Basel Regulatory Framework,” May 2019.
- Christensen, J. H. E. and E. Fischer, 2019, “Bond Flows and Liquidity: Do Foreigners Matter?”, Federal Reserve Bank of San Francisco Working Paper 2019-08.
- Ebeke, C. and Y. Lu, 2015, “Emerging Market Local Currency Bond Yields and Foreign Holdings—a Fortune or Misfortune?” Journal of International Money and Finance, 59, 203-219.
- Engle, R. F., M. J. Fleming, E. Ghysels, and G. Nguyen, 2012: “Liquidity, Volatility, and Flights to Safety in the U.S. Treasury Market: Evidence from a New Class of Dynamic Order Book Models,” Federal Reserve Bank of New York Staff Report, 590, December 2012.
- Fleming, M. J., 2003, “Measuring Treasury Market Liquidity,” Federal Reserve Bank of New York Economic Policy Review, 9, 83–108.
- Gardia-Padilla, 2014, “Secondary Market,” in “The Mexican Government Securities Market,” Banco de Mexico.
- Grigorian, D. A., 2019, “Nonresident Capital Flows and Volatility: Evidence from Malaysia’s Local Currency Bond Market”, IMF Working Paper 19/23.
- Hameed, A., J. Helwege, R. Li and F. Packer, 2019, "Measuring Corporate Bond Liquidity in Emerging Market Economies: Price- vs Quantity-Based Measures," BIS Papers chapters, in: Bank for International Settlements (ed.), “Asia-Pacific Fixed Income Markets: Evolving Structure, Participation and Pricing, 102, pp. 45-62, Bank for International Settlements.
- International Monetary Fund, 2015, “Market Liquidity-Resilient or Fleeting”, Global Financial Stability Report, Chapter Two, October 2015.
- International Monetary Fund and World Bank, 2018, “Recent Developments on Local Currency Bond Markets in Emerging Economies,” Staff Note for the G20 IFAWG.
- Joint Staff Report, 2015, “The U.S. Treasury Market on October 15, 2014,” U.S. Department of the Treasury, Board of Governors of the Federal Reserve System, Federal Reserve Bank of New York, U.S. Securities and Exchange Commission, and U.S. Commodity Futures Trading Commission.
- Musto, D. K., G. Nini, and K. Schwarz, 2018, “Notes on Bonds: Illiquidity Feedback During the Financial Crisis,” The Review of Financial Studies, 31(8), pp. 2983-3018.
- Tapia-Rangel, C., 2014, “Investor Base,” in “The Mexican Government Securities Market,” Banco de Mexico.
- Zhou, J., F. Han, and J. Xiao, 2014, “Capital Flow Volatility and Investor Behavior in Mexico: Selected Issues Paper,” IMF Country Report No. 14/320.

### Appendix Figures
- Appendix Figure 1: Net Securities Purchases of Foreigners vs. IIF Bond Market Flow Data
  - Time axis spans from Jan 09 to Apr 20 with labeled ticks: Jan 09, Jun 09, Nov 09, Apr 10, Sep 10, Feb 11, Jul 11, Dec 11, May 12, Oct 12, Mar 13, Aug 13, Jan 14, Jun 14, Nov 14, Apr 15, Sep 15, Feb 16, Jul 16, Dec 16, May 17, Oct 17, Mar 18, Aug 18, Jan 19, Jun 19, Nov 19, Apr 20.
  - Vertical axis values shown: -10,000; -5,000; 0; 5,000; 10,000; 15,000.
  - Series plotted: "IIF Net Inflow into Mexican Bond Market" and "Transactions Data on Foreigners' Net Purchases of Mexican Debt Securities".
- Appendix Figure 2: Decomposing Liquidity Squeezes
  - Episodes labeled: Episode 1, Episode 2, Episode 3, Episode 4, Episode 5, Episode 6, Episode 7, Episode 8, Episode 9.
  - Time axis for each episode: t=-1, t=0, t=1, t=2, t=3, t=4, t=5.
  - Series plotted in each episode: "Liquidity index", "Price impact norm", "Bid ask norm", "Depth inverse norm".
  - Vertical axis values vary across panels including ranges shown such as -1 to 6, -1 to 8, -1 to 4, and -1 to 9 depending on episode panels.

### Appendix Table 1: Summary Statistics and Definitions
- Table structure includes columns: Variable, Obs, Mean, Std Dev, Min, Max, Definition, Source.
- Selected variable entries (preserving values and text exactly as in source):
  - Market liquidity index
    - Obs: 1,600
    - Mean: -5.0
    - Std Dev: 36.2
    - Min: -69.4
    - Max: 430.3
    - Definition: Index multiplied by 100; higher values imply less liquidity
    - Source: Authors' calculations
  - Probabiliy of high vol. state
    - Obs: 1,600
    - Mean: 0.1
    - Std Dev: 0.2
    - Min: 0.0
    - Max: 0.7
    - Definition: Probability calculated based on Markov regime-switching model on daily data of liquidity index, allowing for three states that differ in mean and variance.
    - Source: Authors' calculations
  - MEX bond market volatility
    - Obs: 1,599
    - Mean: 4.8
    - Std Dev: 3.7
    - Min: 0.2
    - Max: 48.7
    - Definition: Standard deviation of daily 10-year bond yield between t-1 and t-5
    - Source: Bloomberg
  - MEX equity market volatility
    - Obs: 1,600
    - Mean: 13.1
    - Std Dev: 4.6
    - Min: 5.7
    - Max: 35.3
    - Definition: Bolsa Mexicana de valores 30-day volatility
    - Source: Bloomberg
  - MEX FX market volatility
    - Obs: 1,600
    - Mean: 11.9
    - Std Dev: 2.4
    - Min: 6.0
    - Max: 19.8
    - Definition: Volatility implied by 3-month on-the-money peso-dollar option
    - Source: Bloomberg
  - US bond market volatility
    - Obs: 1,590
    - Mean: 65.2
    - Std Dev: 13.5
    - Min: 42.5
    - Max: 117.9
    - Definition: MOVE index
    - Source: Bloomberg
  - US TED spread
    - Obs: 1,470
    - Mean: 30.8
    - Std Dev: 10.9
    - Min: 14.5
    - Max: 68.9
    - Definition: 3-month libor minus 3-month T-bill rate
    - Source: Bloomberg
  - US High Yield Spread
    - Obs: 1,599
    - Mean: 4.0
    - Std Dev: 1.1
    - Min: 2.2
    - Max: 8.4
    - Definition: 10-year U.S. corporate high yield rate minus 10-year U.S. Treasury rate
    - Source: Bloomberg
  - MEX swap spread
    - Obs: 1,587
    - Mean: 5.8
    - Std Dev: 1.7
    - Min: 3.7
    - Max: 8.9
    - Definition: 26-month swap spread (28 day TIIE)
    - Source: Bloomberg
  - MEX TED spread
    - Obs: 1,591
    - Mean: 39.1
    - Std Dev: 12.3
    - Min: -25.0
    - Max: 91.5
    - Definition: 28-day TIIE interbank rate minus 28-day Cetes yield
    - Source: Bloomberg
  - Latam Citi surprise index
    - Obs: 1,592
    - Mean: -10.9
    - Std Dev: 36.4
    - Min: -152.6
    - Max: 59.6
    - Definition: Citi index of economic surprises in Latam
    - Source: Bloomberg
  - US Citi surprise index
    - Obs: 1,592
    - Mean: -3.0
    - Std Dev: 32.6
    - Min: -78.6
    - Max: 84.5
    - Definition: Citi index of economic surprises in the U.S.
    - Source: Bloomberg
- Table footer headings present: "Dependent Variables", "Volatility/Stress Variables", "Funding Liquidity Variables", "Macroeconomic Shock Variables".

*Source: wpiea2020141-print-pdf - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020141-print-pdf.pdf_