Ryan McRandal, Portfolio Manager from One River Asset Management and Nitin Saksena head of US Equity Derivatives Research at Bank of America Merrill Lynch teamed up for a discussion titled How to Improve Directional Trading Using Correlation Information.

Saksena started things off noting that even highly correlated markets may diverge in performance over time.  One example of this is the S&P 500 and Emerging Markets Index.  He noted that 75% of weeks from 2013 to 2015 both markets moved in line with each other but their annual performances diverged greatly.

McRandal took over to demonstrate two trades that follow the theme of the presentation.  The first trade is a version of a carry trade using the correlation between Oil and the Ruble.  The carry trade for Oil and the Ruble both individually show potentially profitable trades, but you could combine a short Oil and long Ruble carry trades to enhance returns, but also since the two markets are correlated could reduce volatility.  A second trade combined the iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) implied versus realized volatility.  He notes that the biggest driver behind LQD realized volatility is rate / credit spread correlations.  The takeaway from this second trade is that it is possible to take a view on the correlation of an asset’s return drivers to trade volatility directionally.

Saksena finished up discussing tail risk with a focus on the FANG stocks (Facebook, Amazon, Netflix, and Google).  He noted that a shift in leadership would be negative for these stocks and there may be investors who are exposed to these stocks, traders with equity market exposure, or traders who would like a cheap way to speculate on the drop for these stocks.