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Figure 1: Correlation Matrix for Commodity Markets (one year)[/caption]
The correlation that does exist is mainly tied to intrinsic similarity and shared utility. WTI Crude Oil correlates highly with Brent Oil, which serves as the main benchmark for oil prices because both commodities can be refined into gasoline. Supply shocks also cause similar reactions across both markets, even though WTI Crude is sourced in U.S. oil fields, while Brent is mostly sourced through offshore oil rigs.
Another pair of high volume commodities, Gold and Silver, showcase similar price action profiles for two main reasons. Gold and silver were both used commonly throughout history as a form of currency. Some societies standardized the value of gold and silver, others would peg their own currency to a fixed stock, and some even governed the mint ratio (the price of an ounce of gold divided by the price of an ounce of silver).
Industrial uses for both gold and silver remain abundant, and the two assets share many supply dynamics, which lends to the assets to a correlation.
Figure 2: Correlation Matrix for World Equity Index Futures (one year)[/caption]
However, there are still equity markets that exhibit little correlation. It is expected that equity markets should all correlate to a certain extent, exhibiting slightly positive correlations. Investors view equity markets through comparable scopes, and financial market interdependence causes a spillover of systematic risk. For instance, FTSE 100 futures correlate highly with European STOXX 50 futures, which is to be expected since UK and EU businesses are subject to very similar economic conditions. Conversely, the S&P 500 and FTSE 1000 futures have almost no correlation.
Interestingly enough, indices provide investors with so much economic insight that many have started tracking the cross-correlation of multiple index-based securities, including futures and ETFs.
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Figure 3: S&P 500 Tracking Security Correlations[/caption]
The theory is that a combination of analyzing futures, and index prices, can yield insight into market price-action. Both ETFs and Futures tend to deviate from the underlying S&P 500 - with the former only deviating by a matter of basis points.
Figure 4: 30-Day Correlation of Selected US Stocks[/caption]
Often enough competing companies will showcase a negative correlation around the time of an important announcement. For example, in the case of competing semi-conductor companies Nvidia (NVDA) and Advance Micro Devices (AMD), when one reports positive results the stock price of the other falls.
Figure 5: Foreign Exchange Markets Correlation Matrix[/caption]
The chart above outlines the 30-day correlation between various currency pairs. One of the most noticeable properties of these assets is the inverse relationship of certain assets; EURUSD and USDJPY. Switching quote currencies will instantly yield a negative correlation, as the rate is expressed in reverse as a reciprocal currency rate. The reality is that the only way to asses the relative value and cross-asset correlation of a currency is to build a weighted index and then plot its correlation against another currency (a la U.S. Dollar Index).
Figure 6: 30-Day Price Action Correlation Across Asset Classes[/caption]
The table above outlines the cross-correlation properties of all of the aforementioned securities, showing how prices across multiple asset classes are able to move synchronously.
"Gaining an understanding of how prices move together across global financial markets is imperative to managing a diverse portfolio of assets."
Depending your goals as an investor you should also consider the data you use in calculating the correlation. If you're a position trader and have a relatively short holding time, consider using a rolling correlation of 5 days. However, if your holding time usually spans several months consider upping it to 30 days, or even 90 days. Correlations change fast depending on the rolling period that is employed. The chart below plots the correlation between two assets as the sample size increases.
This is an example of two positively correlating assets that are both denominated in U.S. Dollars, correlation is endemic to this pair by design.
Examining the SP500 and Brent Oil price correlation depth chart shows that with a larger observable period, the lower the correlation.
The first half of the chart caters to day traders and position traders, while the latter periods are more important to longer-term traders.
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Ivan Struk is an equity and foreign exchange trader with a background in emerging markets and asset management. He currently works in Sales & Trading at Morpher, a decentralised trading platform for virtual futures built on the Ethereum blockchain. Ivan’s research interests lie in equity market efficiency within emerging markets, behavioural finance, and big data.