When selling volatility, exchange-traded-funds (ETFs) can be particularly appealing because they reduce stock-specific risk.

For example, if you sell a straddle in SNAP, one risk is that the stock makes a big move (up or down) during the time you hold the position. Hypothetically speaking, a big move could occur due to financial problems, legal problems, or a takeout.

ETFs reduce single-stock risks because they are composed of more than one stock, in most cases many stocks. Consequently, if there is a blow-up in one company within an ETF, the effect on a short volatility position is minimized (as compared to being short volatility in the single-stock alone).

You may already have a process for filtering the ETF universe for trading ideas, but if not, a recent episode of Market Measures may be of interest. This episode focuses on the sector ETFs, and their relationship to the broader S&P 500 ETF (SPY).

The sector ETFs are the 10 unique funds that divide up the S&P 500 by business category/focus. The sector ETFs include:

  • XLY - Consumer Discretionary

  • XLP - Consumer Staples

  • XLE - Energy

  • XLF - Financials

  • XLV - Health Care

  • XLI - Industrials

  • XLB - Materials

  • XLRE - Real Estate

  • XLK - Technology

  • XLU - Utilities

The existence of sector ETFs provides traders with additional choices when evaluating trades that may better express their view. For example, a trader looking to deploy risk related to the financial sector may feel that the SPY is too broad to express this view, while JPM (a single-stock) is too narrow - and ultimately decide that the XLF best fits his/her needs.

If the trade idea was even more specific, a niche ETF like IAT (regional bank focus) could also be a consideration.

Getting back to the episode, the Market Measures team analyzes correlations between SPY and its ten sector ETFs. Then, they discuss trade ideas related to the spread between SPY implied volatility and sector ETF implied volatility.

The assumption made is that if there exists a high correlation between the SPY and one of the sector ETFs, then there will also be a correlation between the implied volatility of the two.

They then use this criterion as an additional factor to consider when deciding which ETF might be the most appropriate to fit their investment goals.

For example, the XLI (Industrial ETF) showed a relatively high correlation of .95 with SPY. Digging a bit deeper, the team then looked at the spread between SPY implied volatility and XLI volatility, which is illustrated below:

etf-reversion-to-spy

As you can see from the above graphic, the spread between SPY implied volatility and XLI implied volatility is constantly moving.

Therefore, when considering trade ideas in sector ETFs, it may be worth referencing this data. Depending on your approach and risk profile, selling premium in a sector ETF may be more suitable when this spread is high relative to the average.

Certainly, evaluating this type of data to see which sector ETFs have the highest implied volatility relative to the historical spread with SPY may provide at least some insight into current market dynamics.

We recommend viewing the complete episode of Market Measures focusing on sector ETFs and their relationship to SPY when your schedule allows.

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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.