Global Market Stress And Liquidity Regimes

By shrijeetverma13 · February 25, 2026

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The Volatility Index exceeded 30 on 218 days between August 24, 2015 and April 22, 2025. During these high volatility periods, US equities averaged $319.28 compared to $340.78 during normal periods—a 6.3% decline. Meanwhile, gold averaged $170.91 versus $168.45 normally, showing a 1.5% increase.

The analysis examined gold's performance during different financial stress levels by comparing average daily returns across low, medium, and high stress periods.

The analysis compares how Tech and Emerging Market equities perform across different financial stress levels. A grouped bar chart and detailed table show average daily returns for both asset classes across four stress categories: Low (<0), Normal (0-1), Elevated (1-2), and High ( 2).

Oil prices and market volatility show a moderate negative relationship, with a correlation of -0.39. This means that when oil prices rise, market volatility tends to decrease slightly, and vice versa. The analysis reveals that when oil prices increase, the average VIX is 17.88, compared to 18.34 when oil prices fall—indicating markets are slightly calmer during oil price increases.

The average recovery time from major SPY drawdown events exceeding -15% is 196 days (approximately 6.5 months). The analysis identified 5 significant drawdown events in the dataset.

The correlation between Bitcoin and US Equities has evolved significantly over time, showing an overall increasing trend. The analysis reveals that Bitcoin has become more closely tied to traditional equity markets, with the average 90-day correlation rising from 0.301 in the early period to 0.404 in recent periods.

The analysis identified five distinct market stress regimes based on Financial Stress Index (FSI) levels: Low Stress (FSI < 0), Normal (0 to 0.5), Elevated (0.5 to 1.5), High Stress (1.5 to 3), and Extreme Stress (FSI ≥ 3). Each regime shows different duration characteristics, with detailed statistics captured in the generated data tables showing occurrences, average durations, median durations, and duration ranges for each stress level.

Asset class returns show distinct patterns between negative (inverted) and positive (normal) yield curve environments. The analysis compared average daily returns across 7 asset classes during 792 days of inverted yield curves versus 3,357 days of normal yield curves.

The analysis generated a comprehensive visualization showing the relationship between High Yield Spread and SPY Drawdown over time. The chart displays two key components: the first panel shows the time series of both SPY Drawdown percentages (in red) and High Yield Spread (in blue) with a threshold line marking major drawdown events at -10%. The second panel was designed to show lead time analysis for when spread widening precedes drawdown events.

The stock-bond correlation turns positive about 28% of the time (1,169 out of 4,150 days in your dataset). This breakdown of the traditional diversification benefit first occurred on May 11, 2015, and has continued through February 25, 2026. The maximum positive correlation reached was 0.435, indicating periods where stocks and bonds moved together instead of providing offsetting returns.

Bitcoin's 30-day rolling volatility has been tracked over time, showing a range from 0.14 to 1.74 with an average of 0.61. The analysis includes both the raw volatility data and a smoothed 90-day moving average trend line to help visualize long-term patterns.

I've calculated the annual returns for all four asset classes from 2014 through 2026. The analysis shows dramatic differences in performance patterns, with Crypto Bitcoin showing the highest average returns but also the most volatility, while traditional assets like US Equities and Bonds showed more moderate and stable performance.

Based on analysis of 23 major market selloffs (defined as SPY drawdowns exceeding 10%), most major market selloffs begin when the Financial Stress Index crosses approximately 0.18. The median stress level at the start of these selloffs is 0.178, with a mean of 0.160.

The analysis examined 153 oversold signals (RSI < 30) and 666 overbought signals (RSI 70) to measure how well they predict market reversals. Oversold conditions show strong predictive power, with the market moving up 72% of the time within 5 days and maintaining similar accuracy over 10 and 20-day periods. In contrast, overbought conditions perform poorly, with the market declining only 43% of the time after 5 days and dropping to just 30% after 20 days—worse than random chance.

When GLD's RSI drops below 25 (extreme oversold), gold shows a clear recovery pattern. The analysis found 153 such instances, revealing that while immediate 1-day returns are essentially flat (-0.00%), returns improve significantly over longer periods: 0.10% after 5 days, 0.52% after 10 days, and 1.64% after 20 days.

Elevated SPY volatility shows both persistence and mean reversion. When volatility is high (above the 75th percentile of 0.1486), it tends to remain elevated in the short term but gradually returns to normal levels over time.

The analysis identified 69 days when High Yield Spread, Volatility Index, and Financial Stress Index all spiked simultaneously (above 1.5 standard deviations from their means). These periods represent times of significant financial market stress when all three key indicators were elevated at the same time.

Long-term bonds and US equities move in opposite directions about 37% of the time, providing diversification benefits on roughly 1,548 out of 4,149 trading days analyzed. The visualization shows how this diversification pattern varies over time, with the green line tracking opposite movements and the red dashed line showing correlation trends.