Page Layout

The page has these main sections:

• 1. Header with Controls

  Page title with date range selector and download options.

• 2. ILLIQ Index Chart

  Main chart showing the composite index over time. Higher values indicate less liquid markets.

• 3. Historical Context

  Reference points for major market events (2008 crisis, 2020 COVID) to help interpret current levels.

Using the Chart

The ILLIQ chart shows market-wide liquidity over time:

• Index Line

  The main line shows the composite ILLIQ value each day.

• Identifying Spikes

  Sharp upward moves indicate liquidity stress. Compare spike height to historical events.

• Date Range

  Adjust the time period to focus on recent conditions or view long-term history.

Understanding ILLIQ Index Construction

The ILLIQ Composite Index follows a rigorous four-step construction methodology that ensures robustness, representativeness, and interpretability of market-wide liquidity conditions.

Step 1: Asset Selection

The index includes all U.S. equities with active ILLIQ-HIST models in V-Lab's database. This provides broad market coverage across different sectors and company sizes while ensuring each stock has reliable liquidity data.

We only include stocks with sufficient trading history and data quality. This filters out extremely illiquid stocks or those with unreliable data that could skew the composite. The universe of stocks changes over time as companies are listed or delisted, or as data availability changes, keeping the index relevant to current market conditions.

Step 2: Data Collection

For each stock, we collect three pieces of daily data: closing prices, market capitalization (shares outstanding times price), and the ILLIQ measure from V-Lab's ILLIQ-HIST models. Market capitalization determines how much weight each stock gets in the composite, with larger companies having more influence. The ILLIQ measure quantifies how much a stock's price moves per dollar of trading volume, capturing how difficult it is to trade that stock.

We align all three data series to the same trading dates, accounting for weekends, holidays, and any other non-trading days. This ensures we're comparing apples to apples when combining data across different stocks.

Step 3: Data Processing

When ILLIQ or market cap data is missing for a particular day, we use the most recent available value (called forward-filling). This assumes liquidity conditions stay roughly the same until we get new data, which is standard practice for financial indices. This approach maintains continuity in the time series without introducing bias, though it does create some autocorrelation in the data.

Step 4: Computation

First, we take the natural log of each stock's ILLIQ value (plus a tiny offset ε to handle zeros):

Why use logs? Because liquidity shocks multiply rather than add. When markets get stressed, illiquidity might double or triple, not just increase by a fixed amount. Taking logs converts these multiplications into additions, making the math work properly and reducing the impact of extreme values.

Next, we calculate a weighted average of these log-transformed values, where each stock's weight is its market cap divided by the total market cap of all stocks:

Larger companies get more weight because they're more important to overall market liquidity and reduce the effects of extreme outliers often seen in assets with very small market capitalizations. We then reverse the log transformation by exponentiating.

This gives us a market-cap-weighted geometric mean, which is more robust than a simple average and prevents a few illiquid small-cap stocks from dominating the index.

The final index value has the same interpretation as individual ILLIQ measures: it represents the approximate percentage price impact per dollar traded. But instead of showing one security's liquidity, it shows market-wide liquidity conditions. The index captures systemic liquidity risk and identifies stress periods across the market.

Index Value Interpretation

ILLIQ Composite values provide direct liquidity insights. Higher values indicate illiquid conditions with greater trading difficulty and larger price impacts from order execution. Conversely, lower values suggest liquid conditions with easier trading, minimal price impact, and better market depth. Temporal trend analysis allows market participants to track liquidity evolution over time, identifying stress periods during market dislocations and recovery patterns as conditions normalize.

ILLIQ Composite analysis helps identify market-wide liquidity conditions that affect transaction costs, price stability, and systemic risk, providing essential insights for risk management and market timing decisions.

Market Stress Identification

The index serves as a comprehensive market stress indicator for crisis period detection, as ILLIQ spikes often coincide with financial crises, market dislocations, and systemic stress events. Recovery pattern analysis reveals that declining ILLIQ values signal improving market conditions and restored investor confidence. Cross-sectional analysis provides sector-specific insights, revealing differential liquidity impacts across market sectors and helping identify which segments of the equity market are experiencing the most severe liquidity stress.

V-Lab's ILLIQ Composite employs sophisticated market-cap weighting with log-transformation methodology to create a robust, representative measure of U.S. equity market liquidity that accounts for both individual asset characteristics and market-wide conditions.

Methodological Framework

The index construction combines Amihud ILLIQ measures across all qualifying U.S. equities using market-capitalization weights and log-transformation techniques to ensure statistical robustness while maintaining intuitive interpretation of liquidity conditions.

Common Questions

Why did ILLIQ spike when markets seemed calm?

ILLIQ can spike due to reduced market depth or technical factors even when stock prices are stable. The index measures trading difficulty, not price direction.

How do I compare current levels to history?

Use percentile rankings rather than absolute values. Market structure changes over time, so a level that was 'high' in 2000 may be 'normal' today.

How do I download the data?

Click the download button after logging into your V-Lab account. Data exports as CSV.

Understanding the Data

Common questions about interpreting what you see:

Why does ILLIQ stay high for extended periods?

Extended high periods reflect structural issues: sustained risk-off sentiment, regulatory changes, or prolonged uncertainty. Unlike volatility, liquidity can take longer to normalize.

When should I use the composite vs individual stock ILLIQ?

Use the composite for systemic risk monitoring and market-wide conditions. Use individual ILLIQ measures for security-specific trading decisions.

How do market structure changes affect interpretation?

Electronic trading and HFT have generally improved baseline liquidity over time. Compare to recent history (5-10 years) rather than decades ago.

Here's how to quickly assess market liquidity conditions:

Interpreting ILLIQ Levels

High ILLIQ indicates reduced market liquidity—fewer trades absorb price impact, and bid-ask spreads tend to widen. Low ILLIQ indicates liquid conditions where larger trades can execute with minimal price impact. Comparing ILLIQ trends with volatility measures like VIX reveals whether liquidity stress is occurring alongside broader market stress.