**Correlation Trading: Exploiting Relationships In Forex Pairs** – Understanding price relationships between various currency pairs allows you a deeper look at how to develop high probability Forex trading strategies. Awareness of currency correlation can help reduce risk, improve hedging and diversify trading instruments. In this article, we will introduce you to Forex trading using intermarket correlations.

Correlation is a statistical measure of the relationship between two trading assets. Currency correlation shows the extent to which two currency pairs have moved in the same, opposite or completely random directions within a particular period.

## Correlation Trading: Exploiting Relationships In Forex Pairs

Analysis of two asset relationships using past statistical data has predictive value. Using the correlation coefficient, we can understand the relationship between two values and help manage risk. The coefficient is measured in decimal form from -1 to +1.

### What Is Statistical Arbitrage And Pair Trading?

Naturally, the stronger the positive or negative correlation, the higher predictive value is extracted from the analysis. More extended time frames used for technical analysis show more accurate information compared to relationships longer than one minute, which have some value. Monthly and annual data generally provide the most reliable insight.

Be aware that currency correlations are constantly changing over time due to various economic and political factors. These often include diverging monetary policies, commodity prices, changes in central bank policies and more. Given that strong correlations can change over time, it highlights the importance of staying up-to-date on changing currency relationships. We recommend checking long-term correlations to gain a deeper perspective.

All in all, currency correlations could be a powerful tool that you can use to develop high probability trading strategies. You will also help with risk management, especially if you track the correlation coefficients over daily, weekly, monthly and yearly time frames. Multi-Cloud Costs Take your FinOps to the next level FinOps for MSPs Deliver value and grow your business.

Finding connections between disparate events and patterns can reveal a common thread, an underlying cause of occurrences that, on a surface level, may appear unrelated and inexplicable.

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The process of discovering the relationships between data metrics is known as correlation analysis. For data scientists and those tasked with monitoring data, correlation analysis is incredibly valuable when used for root cause analysis and reducing time to resolution. The longer it takes to understand and fix the root cause, the greater the cost to the organization. The applications are numerous, however in this series we concentrate on the commercial sphere.

Earlier in this series on correlation analysis, we evaluated applications in various business use cases, specifically in the context of promotional marketing and telco. Similarly, we will explore here technical considerations for better using correlation analysis in fintech analytics, specifically in the algorithmic trading space.

The algorithmic trading market is expected to register a CAGR of 11.23 percent in the forecast period of 2020 to 2025. The global market for algorithmic trading is projected to be around $19.2 billion by 2027, growing at a CAGR of 8.7 percent during the period 2020- 2027. Exchanges which account for most of the trading volume include, but are not limited to CME Group, CBOE, NASDAQ and NYSE (CFTC acts as the regulator).

One of the key aspects of algorithmic trading is early detection of arbitrage opportunities. This can manifest itself in various flavors – one of particular interest is volatility arbitrage, a situation when there is a difference between implied volatility and realized volatility of an option (more here for an overview of different types of arbitrage). Exploiting such arbitrary opportunities critically depends on, among other things, data fidelity, which in turn can be assessed through correlation analysis as discussed later in the article.

### A Behavioral Approach To Instability Pathways In Financial Markets

2020 was a tumultuous year on several fronts. The following plot overlays the S&P 500 and VIX for the year. We notice from the plot that at the beginning of the pandemic, the index fell materially and since then gradually recovered and, in fact, exceeded the previous highs. Although both the S&P 500 and VIX cannot be traded directly, the anti-correlation trend provides insight into how to structure trades using, for example, ETFs based on these indices.

The plots below show the time series – at the second granularity – of VXX, IWM and NFLX for the period from February 3 to May 29, 2020, during which the market corrected and rebounded. Note that on a daily basis, the key indices such as DJI and S&P 500 have corrected by more than a whopping 10 percent. From the plots below we note that, not unexpectedly, VXX is anti-correlated with the rest. However, the spiciness varies across the typewriters – for example, IWM is much spicier than others.

In a similar vein, the recovery also differs. For example, NFLX rebounded much stronger than the others; specifically the May 2020 level, which was higher than the levels before the market downturn.

Further, correlations vary across different time windows. As discussed later in this article, an analysis of window correlations can potentially reveal trading opportunities to make a profit.

#### Pound Sterling Price News And Forecast: Gbp/usd: The Pair Balances At Critical Levels

The plot below shows VIX and VVIX (VIX volatility index, also called volatility-of-volatility) for the same period, February 3 – May 29. From the plot we notice that the two indices are highly correlated. However, the ratio of the size of jumps varies significantly over time. For example, the ratio of the jumps captured by the two red boxes is 2.5x (left) versus 4x (right). Detecting and capitalizing on such jumps can potentially yield material benefits.

On the other hand, there are examples galore where different trading instruments move in tandem with each other; in other words, the respective time series are highly correlated. This is also observed on a global scale as exemplified by the plot below which corresponds to the crash in October 1987. As is evident from the plot, wild swings in daily returns across different geographies were highly correlated. In a similar scenario going forward, if there were geographies where returns were not correlated, it can serve as an opportunity to profit from such market dislocations.

Being able, for example, to anticipate a trend, detect an anomalous pattern, establish a break in correlation between two or more trading instruments can help surface trading opportunities and gain profit. The latter has been widely exploited in the sphere of derivatives trading, portfolio management, etc.

This is exemplified by the following trading strategies: (1) empirical correlation trading, (2) pair trading, (3) multi-asset options, (4) structured products, (5) correlation swaps, and (6) spread trading (refer to. to Meissner’s work for more details). In a similar vein, a large series of correlation models (refer to the figure below) have been developed to estimate financial correlation risk.

#### Forex Spreads: Low Spread Scalping Strategies

A further breakdown of one-factor and two-factor copulas (remember that copulas allow the joining of multiple univariate distributions to a single multivariate distribution) is shown in the figure below.

Considering the large daily turnover across different assets and different trading instruments, it is important to ensure that the input data to the aforementioned models is of high fidelity. Similar to the copulas, it is important to check for deviation from the expected data distribution. Deviations can appear in the form of excessive kurtosis (see References below) and the effect of outliers. In the case of the latter, methods such as pruning (see References) and robust correlation have been proposed, which can be exploited to drive sound decision-making.

Data in the fintech world has a high degree of stochasticity. Therefore, correlations between different trading instruments also vary over time. Guidolin and Timmermann reported large variations in the correlation between stock and bond returns across different market regimes defined as crash, slow growth, bull and recovery. This has direct implications on the analysis and subsequent structuring of business (whether it be for coverage or be speculative). To this end, rolling correlations are often used to surface the change in relationships over time. For example, the plot below illustrates the relationship between changes in the cross-currency basis (JPY-USD) and the dollar using rolling correlations.

From the above plot we notice that the longer the rolling window, the lower the proportion of positive correlation coefficients between changes in the base and dollar and the higher the proportion of negative correlation coefficients. Having said that, we see that the dynamic relationship seems to vary in direction, from negative to positive or vice versa. Overall, such a pattern has implications for optimal and profitable positioning in the cross-currency basis swaps in the current world of low rates, a narrower FX trading range behind depressed volatility, and persistent hedged interest parity deviations.

### Trade Network Centrality And Currency Risk Premia

A well-known trading strategy, called speculative convergence, exploits such deviations. Specifically, if two bases may be historically cointegrated (ie, they move together in levels in the long run), but it is identified that a shock occurred that temporarily broke their lockstep movements, causing them to wander in opposite directions, then a potentially profitable speculative convergence strategy would involve:

Advanced methods such as variations of multivariate generalized autoregressive conditional heteroskedasticity (GARCH) have been proposed to estimate correlation between financial variables. Upon estimating the GARCH(1, 1) model and using its resulting standardized residuals, time-varying correlation

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