Binary Options Correlation Trading

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Contents

Trading Binary Options with Currency Pairs Correlation

Correlation is a statistical measure of the relationship between any two assets (currency pairs, commodities, stocks, etc.). The correlation between any two currency pairs in a Forex market can be either positive or negative. If two currency pairs share a positive (direct) correlation between them, then the direction of price movement will be the same at any given point of time. Likewise, if two currency pairs share a negative (inverse) correlation between them, then the direction of price movement will be mutually opposite at any given time. A trader should also remember that in most cases, no correlation exists between two currency pairs.

A trader will be able to take two trades at the same time by monitoring the price movement of one currency pair, which shares a correlation with another. So far, we had spoken about trading currencies in markets. A binary market is a bit different considering the fact that time plays a rather important part than the quantum of price movement in a particular direction. Let’s study how far correlation between two currency pairs can be used successfully in binary trading.

Positive and negative correlation coefficient

Two currency pairs with a positive correlation may move in the same direction. However, the quantum of movement may not be the same. For example, the EUR/USD and GBP/USD pairs share a positive correlation. This means that when the EUR/USD pair is trending upwards, the GBP/USD pair will also be in an uptrend. However, for every pip movement seen in the EUR/USD pair, there may not be a pip movement in the GBP/USD pair. If the GBP/USD pair moves by one pip for every pip movement seen in the EUR/USD pair then both currency pairs are said to have a perfectly positive correlation coefficient of +1. In percentage terms, it is expressed as 100%.

Similarly, the EUR/USD pair shares a negative (inverse) correlation with the USD/CHF pair. This means that if the EUR/USD pair is an uptrend, then the USD/CHF pair would be in a downtrend. However, for every pip movement seen in the EUR/USD pair, there may not be a pip movement in the USD/CHF pair. If the USD/CHF pair moves down by one pip for every upward pip movement seen in the EUR/USD pair, then both currency pairs are said to have a perfectly negative correlation coefficient of -1. In percentage terms, it is expressed as -100%.

Now, to apply correlation between two assets successfully in binary trading, a trader should know the following details:

  • Nature of correlation (positive or negative)
  • Correlation percentage (between +100 and -100)

Authentic correlation data sources

Fortunately, a binary trader need not go through the difficulties of calculating the correlation between various currency pairs, commodities and precious metals. Most of the finance related websites offer them for free. In this regard, there are two reputed sources of information that a trader can use to his advantage: Oanda and Mataf.

Oanda

Oanda enables a trader to study correlation between major currency pairs, exotics, metals, indices, commodities and even US/UK Treasuries. The checkboxes provided next to the asset categories enable a trader to narrow down the selection. Once the list is narrowed, a reference currency (or any other asset), indicated by a green underline, can be selected by the mouse. Now, the correlation between the reference currency and the assets belonging to the selected category are displayed for a period ranging from an hour to a year.

A trader can choose a standard table format or other forms of visual presentations (bubble, heatmap) to study the correlation, as shown in the images below. A detailed popup message indicating the level of correlation is shown when the mouse pointer is moved over the bubble or heatmap. The greater the bubble size, the higher is the correlation. Dark red and blue colors indicate the strongest positive and negative correlation, respectively, between currency pairs (or any other assets). Grey color indicates lack of correlation.

We chose to find the EUR/USD pair’s correlation with its major rivals through Oanda.com and received the details (table, bubble, and heatmap) as shown below.

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Mataf

The financial website Matef.net visually displays correlation as a graph instead of a bubble or heatmap. It is needless to say that the table format is readily available as well. Two tabular columns (named ‘excluded’ and ‘included’) are provided for the selection of currency pairs. Notably, only the most common currency pairs are offered for study.

Once the currency pairs are selected, as shown below, the page displays the correlation coefficient in the form of a table along with a graph. The correlation data is provided for a period of 5 minutes, one hour, four hour, and one day. The graph enables a trader to identify the periods of increasing and decreasing correlation even with a given timeframe. It would be useful for a binary trader to time the entry.

The first thing a trader can notice is that the correlation between two currency pairs is not the same across all the timeframes. For example, the Oanda’s correlation map shows high positive correlation between the EUR/USD pair and NZD/USD pair in a 1-month timeframe. However, in a 3-month timeframe, the correlation remains neutral (no correlation).

As it can be seen in Oanda’s table, in a weekly timeframe, the EUR/USD pair shares a positive correlation with the GBP/USD pair, and a negative correlation with the USD/CHF and USD/JPY pairs. However, the EUR/USD shares a higher negative correlation of 95% with the USD/CHF pair, compared with only 23% negative correlation with the USD/JPY pair. This means that while betting on an uptrend in the EUR/USD pair, a binary trader should choose to bet on a downtrend in the USD/CHF pair, rather on the USD/JPY pair. This would increase the probability of success in the trade as long as the contract expiry period is one hour. It should be remembered that correlation percentage between any two pairs is not a constant. So, a binary trader should regularly keep himself updated on the changes in the correlation ratio.

Using correlation in trading

After collecting correlation details between different currency pairs, commodities, and metals, a binary trader can open trades as described further.

Low positive correlation

A binary trader should first look for two currency pairs with low positive correlation in a timeframe less than or equal to one hour. Once the currency pairs are selected, a trade should be opened in the simplest of option contracts, i.e. a call or put option (or their equivalent). Even though both currency pairs may move in the same direction, the extent of movement will not be the same since the currency pairs have a low positive correlation. Thus, trading a option or ladder option may prove futile. Furthermore, the trader should make sure that the expiry period of the option matches the timeframe used to study the correlation.

Once an uptrend is confirmed, a trader should purchase a call option in both the currency pairs. This means that a trader who has identified a low positive correlation between two currency pairs in a 1-hour timeframe should choose to trade contracts which expire in an hour and not later. If the trader spots a downtrend, then a put option should be bought in both the currency pairs as described above. Again, the expiry period of the option should match the correlation timeframe.

High positive correlation

To begin with, a binary trader should choose two currency pairs with high positive correlation in a timeframe of not less than a day. Once that is done, the trader should select a contract which offers a greater return on investment. A call (or put) option or even a ladder option can be selected. Furthermore, it is better to select and trade currency pairs with high positive correlation during major economic news announcements. Since both currency pairs would more or less rise or fall to the same extent, it is enough to spot the trend of a single currency pair and enter into two trades with confidence. Since major economic events drive a currency pair in a particular direction for several days, a trader can choose a contract with an expiry period ranging from one day to even up to a week. However, the trader should make sure that the expiry period of the option matches the correlation timeframe.

Low negative correlation

In this case, two currency pairs which have a low negative correlation in a timeframe of one hour should be selected. Once an uptrend is identified in a currency pair, a call option should be bought. Simultaneously, a put option should be bought in the currency pair which shares a low negative correlation with the currency pair in reference. It is better to stick with simple contracts (call and put option combo) having expiry periods that match the correlation timeframe.

High negative correlation

It is similar to trading currency pairs with a high positive correlation. Initially, two currency pairs with a high negative correlation in a timeframe not less than a day should be selected. This is followed by the selection of an option contract which offers high returns. The contract should be preferably traded during major economic news releases. Once an uptrend is confirmed in a currency pair, a option or a ladder option (depending on the impact level of the news) can be bought. Simultaneously, a similar contract with target levels in the opposite direction can be bought in the currency pair which has a high negative correlation with the currency pair in reference. The trader should also make sure that the chosen expiry period matches the correlation timeframe.

A binary trader can also select a option contract for a currency pair and a option contract for the other correlated currency pair, as long as the target levels are in the same direction. Since the actual movement of currency pairs would be in a mutually opposite direction, both trades would expire in the money. Care should be taken to select an expiry period that matches with the correlation timeframe.

Once a binary trader learns to set up trades based on the basic techniques explained above, complex trades can be constructed to generate returns from multiple trades by simply concentrating on the price movement of a single currency pair. Choosing a broker with multiple currency pairs available is recommended if you want to benefit from correlation trading.

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Correlation

Correlation describes the mutual relationship between two independent values. The most basic use of correlation in trading is in finding out whether there’s a relationship between two variables and, if there is, what kind of relationship it is. The number is generally given as a figure between -1 and 1, where -1 implies a negative correlation, 0 represents no correlation whatsoever, and 1 implies a positive correlation.

For trading, correlation can have many different uses, as so much of what traders do is based on analysing the relationships between different stocks, currencies and markets etc. Here, we’re going to look at how we define and employ correlation in day-to-day trading.

Defining Correlation in Trading

To find whether there’s a relationship between two variables, we use the correlation coefficient. This is a scale which runs from -1 to 1 and provides the following information:

  • -1 implies negative correlation: as A increases in value, so B decreases and vice versa
  • 0 implies no correlation: the values of A and B have no relationship
  • 1 implies positive correlation: A and B increase or decrease in value in unison.

This being a scale, there are also in-between values although, in trading, a coefficient of more than -0.8 or less than 0.8 is not considered significant.

Pearson’s Correlation Coefficient

The formula used to calculate correlation coefficients is the Pearson Correlation Coefficient (PCC), a method of statistical analysis named after Victorian mathematician Karl Pearson.

Let’s say we want to find if there is any correlation between stock A and stock B: to find the correlation, we first need to know the covariance and standard deviation of our two stocks.
In trading, covariance is the measure of the relationship between A and B. This might sound the same as correlation, but it is different (see note below).

Covariance is found by taking a set of values for A and B from a given time period and calculating each one’s mean average. The mean is then subtracted from each A and B value. The subsequent totals for A are added together and likewise with B. The sum of these A and B values is multiplied and then divided by the number of values you started with minus 1 (so if you took the values of A and B once a day over a ten-day period, you’d divide by 9).

Standard deviation in trading is a measure of how far the value of a security varies from its mean average over a given period of time.

To find the standard deviation, you first need to know the variance (different from covariance): this is calculated by taking the values of one stock, A, over a given time period. As with covariance, the mean average of A is worked out and then subtracted from each value.

Each of these totals is then squared and added together. This total is then divided by the original amount of values minus 1 to give you the variance. The square root of the variance is the standard deviation.

The correlation coefficient is found by dividing the covariance of A and B by the sum of the standard deviations of A and B.

Difference Between Correlation and Covariance

The subtle but important difference for traders between correlation and covariance is thus: covariance only measures the rate at which two values change in tandem whereas correlation measures the extent to which these value changes are related. When these are seen plotted on the same graph, correlation will tend to stay closer to the mean than covariance.

Pros and Cons of Correlation

As with anything in trading, the correlation coefficient has its advantages and disadvantages:

  • Better than using covariance as it shows the strength of a relationship between two values.
  • Although not quick to calculate, the correlation scale of -1 to 1 is easily understandable, even to trading beginners.
  • A graph of a correlation coefficient can show trends that are helpful in determining the future direction of a value.
  • Pearson’s formula can accommodate large quantities of data, allowing traders to calculate correlation over as large a time period as desired.
  • Correlation shows the strength of a relationship, but cannot show whether the relationship is a cause-effect one. Even a strong correlation of -1 or 1 cannot show whether the movement of
  • A directly affects the movement of B or vice versa.
  • Whilst correlation can show trends based on historical data, it cannot predict the future. Even portfolio diversification based on low or non-correlated securities (see below) must work on the assumption that correlation coefficients will remain broadly the same.
  • As with any calculation based on mean averages, atypical values can skew the average and therefore the correlation results – especially with smaller data samples.
  • A strong correlation is best displayed when there is a linear relationship between the two values (as A moves up, so does B; as A moves up, B moves down etc.) However, two values that have a nonlinear relationship may still have a correlation that the coefficient calculation cannot display.

Using Correlation in Trading

Now we know what correlation means for traders, let’s look at ways in which it’s employed in everyday trading:

Measuring the Relative Performance of Two Variables

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).

Portfolio Protection and Diversification

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.

Other Trading Strategies Which Employ Correlation

Pairs Trading

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

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.

Multi-Asset Options

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

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’.

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  • Binomo
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