Scientists Analyzed 2.4 Million Options Trades, What They Found Changes Everything

The Big Picture: What Is Correlation?

Think of correlation like a dance between stocks. Sometimes they move in perfect sync (high correlation), sometimes they move completely independently (low correlation), and sometimes one zigs while another zags (negative correlation).

A simple example: During calm markets, Apple and Microsoft might move somewhat independently, Apple could be up 2% while Microsoft is down 1%. But during a market crash, they often fall together, both might drop 8% on the same day.

This changing relationship creates opportunities for informed traders, but also significant risks that must be carefully managed.

The Research: What Scientists Discovered

In 2019, researchers Bernard and Bondarenko analyzed 2.4 million options trades from 2007-2017, studying how the S&P 500 and its nine sectors actually moved together compared to what options prices suggested they would do.

Key Findings

The Fear Premium: During market stress, investors consistently overpaid for protection against stocks falling together:

• Down correlation premium: -11.8% (investors overpaid for crash protection)

• Up correlation premium: +15.7% (investors underpaid for upside participation)

• Net effect: -1.5% average overpayment for correlation insurance

What this means in plain English: When people get scared about market crashes, they pay too much for options that protect against "everything falling at once." When markets are rising, they don't pay enough for options that benefit from "everything rising together."

Why This Happens: The Psychology Behind the Numbers

Loss Aversion: People hate losing money more than they enjoy making it. During scary times, they'll overpay for insurance.

Recency Bias: After witnessing a crash where everything fell together, investors assume it will happen again and overpay for protection.

Diversification Illusion: Many believe their diversified portfolio will protect them, but during real crises, diversification often fails as correlations spike.

Practical Applications: Three Strategic Approaches

Strategy 1: Cash-Secured Puts During Fear Spikes

What it is: Selling put options while holding enough cash to buy the stock if assigned.

When it works best: During periods when investors panic about "everything falling together."

Simple example: If Apple stock trades at $150 and you sell a $140 put for $3, you collect $300 per contract. If Apple stays above $140, you keep the premium. If it falls below $140, you buy 100 shares at $140 (your net cost: $137 after the premium).

Why the research supports this: During fear periods, these put premiums often exceed the statistical probability of the stock reaching those levels.

Strategy 2: Sector Rotation Based on Correlation Patterns

The insight: Different sectors show different correlation patterns during stress.

Historical patterns from the research:

• Utilities: Often maintain independence longer, then spike in correlation during peak fear

• Technology: Shows dramatic correlation swings

• Financials: Tend to move together consistently during crises

Practical application: Focus put-selling on defensive sectors (utilities, consumer staples) during correlation spikes, as their fundamentals often remain stable while option premiums inflate due to market-wide fear.

Strategy 3: Timing-Based Premium Collection

The pattern identified:

1. Initial shock: Correlations drop as investors think "this sector is different"

2. Fear amplification: Correlations spike as reality sets in

3. Premium explosion: Options become expensive across the board

4. Mean reversion: Correlations return to normal, premiums collapse

Implementation: Be most aggressive selling options during phases 2-3, most conservative during phases 1 and 4.

Risk Management: What Can Go Wrong

When These Strategies Fail

March 2020 Example: During the COVID crash, correlations spiked to near 1.0 as everything fell together. Put sellers who were over-leveraged faced massive losses and assignment on multiple positions simultaneously.

2008 Financial Crisis: The research period includes this crisis, showing that while premiums were indeed elevated, actual losses still occurred for many option sellers.

Key lesson: High premiums don't eliminate risk—they compensate for it.

Essential Risk Controls

Position Sizing: Never risk more than 2-5% of your portfolio on any single position, and limit total options exposure to 10-20% of your portfolio.

Diversification Limits: Don't sell puts on more than 3-5 highly correlated positions simultaneously.

Cash Management: Always maintain the full cash amount for assignment. Never sell "naked" puts expecting to close before expiration.

Stop-Loss Rules: Have predetermined exit points if the underlying stock moves against you by certain percentages.

Implementation Guide: Getting Started Safely

Step 1: Education and Paper Trading

• Practice these strategies with virtual money for at least 3 months

• Study correlation patterns in your target stocks during different market conditions

• Learn to read options chains and understand implied volatility

Step 2: Start Small and Simple

• Begin with 1-2 positions maximum

• Choose large, stable companies you'd be comfortable owning

• Focus on 30-45 day expiration periods

• Start with puts 5-10% out of the money

Step 3: Track and Learn

• Keep detailed records of entry/exit points, premiums collected, and outcomes

• Note market conditions when you enter positions

• Review quarterly to identify patterns in your success/failure rates

Step 4: Gradual Scaling

• Only increase position sizes after demonstrating consistent success

• Add complexity (more positions, different sectors) gradually

• Always maintain strict risk management rules

Real-World Expectations: What Success Actually Looks Like

Realistic Returns

• Experienced practitioners: Often target 1-3% monthly returns on deployed capital

• Beginners: Should expect lower returns initially while learning

• Bad years happen: Even skilled practitioners have losing years

Time Commitment

• Initial learning: 10-20 hours of study before first trade

• Ongoing management: 1-2 hours per week monitoring positions

• Active periods: During market stress, may require daily attention

Success Metrics

• Win rate: Good practitioners often win on 70-80% of trades

• Average win vs. average loss: Wins are typically smaller but more frequent

• Annual performance: Successful strategies often generate 8-15% annual returns with managed risk

Common Mistakes to Avoid

Overconfidence from Early Success: Many beginners get lucky in their first few months and dramatically increase position sizes, leading to major losses.

Ignoring Assignment Risk: Not having a plan for when you're assigned shares during market stress.

Chasing High Premiums: The highest premiums often come with the highest risks—not always a good trade-off.

Inadequate Diversification: Selling puts on multiple correlated stocks thinking you're diversified.

The Academic Perspective: Limitations and Caveats

Study Limitations

• Time Period: 2007-2017 may not represent all market conditions

• Market Structure: Algorithmic trading and market making have evolved since then

• Survivorship Bias: The study focuses on major indices and sectors, not smaller or failed companies

Ongoing Research Questions

• How do these patterns hold in different interest rate environments?

• What impact do passive investing flows have on correlation patterns?

• How might regulatory changes affect these opportunities?

Conclusion: A Balanced Approach to Correlation Trading

The Bernard and Bondarenko research provides valuable insights into correlation risk premiums, but it's not a guarantee of future profits. Like any investment strategy, success requires:

Proper Education: Understanding both the opportunities and risks

Disciplined Implementation: Following predetermined rules even when emotions suggest otherwise

Realistic Expectations: Accepting that losses will occur and sizing positions accordingly

Continuous Learning: Adapting to changing market conditions

The correlation risk premium appears to be a persistent feature of options markets, driven by fundamental human psychology around loss aversion and portfolio insurance. However, capturing this premium safely requires careful risk management, proper position sizing, and realistic expectations about both returns and potential losses.

Remember: the goal isn't to get rich quickly, but to systematically capture small premiums while managing downside risk over long periods. The research suggests this is possible, but only for those who approach it with the proper respect for the risks involved.

Want to learn more: The Income Foundation

Probabilities over predictions,

Andy Crowder

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