Now we know what correlation means for traders, let’s look at ways in which it’s employed in everyday trading:
The main reason any trader would want to know the correlation between two variables is ultimately to inform their investing.
An interesting example of this is the correlation between stocks and bonds, particularly those of the S&P 500 and US Treasury bonds. Since the turn of the century, these two asset classes have been almost consistently negatively correlated.
Two decades of negative stock-bond correlation have given rise to the assumption that as stocks increase, bonds will always drop as investors seek to free up capital to take advantage of bull markets. The reverse is also deemed to be true; in a bear market, investors can look to bonds as a way of protecting their portfolio (this is sometimes referred to as a ‘flight to quality’ or ‘flight to safety’ – more on this below).
However, it’s worth bearing mind that during the 1980s and 1990s, the correlation between stocks and bonds was almost exclusively positive. This was largely due to inflation rates both expected and realised. Therefore, investors can see that there is a correlation between stocks and bonds, but its nature can change over time due to outside economic factors (remember that correlation cannot show causality).
Traders want to guard their assets as much as possible against systematic risk – i.e. factors that affect a large section of a market, if not the whole market. Popular ways of doing this are portfolio diversification and portfolio protection.
Both of these employ correlation by including securities which have either low or no correlation to equities. Low and non-correlated assets are also referred to as alternative investments and can include private equity, precious metals and options. The thinking is that by balancing a portfolio in this way then, should one set of equities suddenly depreciate in value, the low correlated securities won’t be as badly affected as the equities and therefore help hedge any losses.
Pairs trading looks for two securities which are historically highly correlated (a coefficient of 0.8 or above) and seeks to capitalise on any diversion from their correlation.
The strategy was developed at Morgan Stanley in the 1980s.
It involves taking a short position on the over-performing stock (A) and a long position on the under-performing stock (B) once they deviate a certain distance from their correlation. The trader then simultaneously sells A and buys B. The theory is that as correlation tends to be mean reverting, profit is made from going short on A and long B.
Correlation swaps are over-the-counter (OTC) financial derivatives. Essentially a correlation swap is a contract which promises a return for every increase in the correlation coefficient between two products. As with pairs trading, an ideal correlation swap would be based on two highly-correlated securities that have deviated a distance from their mean.
These are also known as rainbow options or correlation options. Multi-asset options have many different variations but, at base level, are derivatives based on more than one underlying asset and pay out on the best (or worst) performing of them. Such option prices are sensitive to the correlation between the underlying assets, hence the term correlation options.
Dispersion trading is a complex trading strategy. It works on the idea that the difference between the implied and realised volatility of an index tends to be greater than that of its component stocks. In theory, profit is made by selling index options and buying options in its individual parts.
Correlation is involved in dispersion trading in two ways: Firstly because trades can be more profitable when component stocks are not highly correlated. Secondly, part of the formula for calculating a dispersion trading strategy involves working out the implied correlation of an index, also known as the ‘dirty correlation’.