Different market environments often require different trading tactics, or at the very least, a heightened awareness of the changing risks to your portfolio.

One important concept to be aware of is correlation, and how the dynamics of correlation can change when fear enters the market.

At a high level, correlation measures the degree in which two securities move in relation to each other.

For example, when two underlying securities/assets are positively correlated, that means they move in the same direction (to varying degrees). When two securities are negatively correlated, that means they move in opposite directions (to varying degrees). If no correlation exists between two securities, than the relationship is described as "zero" correlation.

Negative and positive correlations can range between -1 and +1. If the correlation is exactly -1 or +1, this is called "perfect negative correlation" or "perfect positive correlation."

If correlations are between one and zero, or between negative one and zero, then they are usually described as “weak”, “semi-strong”, and “strong” - depending on the actual number.

For example, 0.15 might be considered a "weak positive correlation," whereas 0.75 might be considered a "strong positive correlation." On the other hand, -0.20 might be considered "weak negative correlation," while -0.80 might be referred to as "strong negative correlation."

One thing to keep in mind is that when fear enters the market, correlations tend to converge. That’s because when panic grips the financial markets, the differentiation between Exxon Mobil Corporation (XOM) and Apple Inc. becomes more negligible - they become assets that need to be sold, with other considerations thrown out the window.

If you are looking for more information on how correlations behave during selloffs, a recent episode of Market Measures is a great place to start.

The focus of the show is a new study which examines how different asset prices tend to behave during selloffs - information that  can help traders understand how their portfolios might perform under such conditions, as well as what trading tactics might be attractive during these periods.

As you can see in the slides below, which are excerpted from Market Measures, indices (like SPY and QQQ) tend to exhibit positive strong correlation, while indices versus gold tend to exhibit semi-strong inverse correlation:

When Correlations Converge
When Correlations Converge

The above helps illustrate why selling puts in SPY and QQQ is effectively like doubling down on the same trade, because these underlyings frequently move together.

On the other hand, the second slide above, illustrates why selling puts in SPY and GLD isn't the same type of exposure. That’s because gold is usually moving in the opposite direction of the indices during a selloff, or at least in a more “neutral” manner in terms of correlation.  

In order to put more context around the behavior of correlations during selloffs, the Market Measures team devised a study which backtested historical data in indices, interest rates, precious metals, foreign currencies, and commodities - all of which was intended to better understand how these groups moves in relation to each other.

The findings from this analysis, presented on the show, did reveal that the correlations between indices definitely increases during selloffs. The data also showed that bonds (TLT) and gold (GLD) were two particular underlyings that shared either a semi-strong inverse correlation (bonds), or a neutral correlation (gold) with stock market indices.

We hope you’ll take the time to review the complete episode of Market Measures focusing on the behavior of correlations during selloffs when your schedule allows.

 If you’ve observed other aspects of correlation you want to share, or have any questions about the material, don’t hesitate to leave a message in the space below, or reach out to @tastytrade on Twitter or send an email to support@tastytrade.com.

Thanks for reading!

Sage Anderson has an extensive background trading equity derivatives and managing volatility-based portfolios. He has traded hundreds of thousands of contracts across the spectrum of industries in the single-stock universe.

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