{"id":259,"date":"2024-11-11T14:58:56","date_gmt":"2024-11-11T14:58:56","guid":{"rendered":"https:\/\/50yeartrader.com\/blog\/leading-lagging-currency-pairs-part-1\/"},"modified":"2026-09-30T21:26:30","modified_gmt":"2026-09-30T21:26:30","slug":"leading-lagging-currency-pairs-part-1","status":"publish","type":"post","link":"https:\/\/50yeartrader.com\/blog\/leading-lagging-currency-pairs-part-1\/","title":{"rendered":"Leading and Lagging Currency Pairs, Part 1: Cross-Correlations"},"content":{"rendered":"\n

This article is Chapter 8 in The Currency Companion<\/em>, Supplement #1.<\/p>\n\n\n\n

Overview<\/h2>\n\n\n\n

In general, arbitrage is the purchase or sale of a financial instrument with the simultaneous taking of an equal and opposite position in a related market, in order to take advantage of small price differentials between markets. Arbitrage opportunities arise when currency prices go out of sync with each other. There are numerous forms of arbitrage involving multiple markets, future deliveries, options and other complex derivatives. A simple example of a two-currency, two-location arbitrage follows.<\/p>\n\n\n\n

Bank ABC offers 170 Japanese yen for one US dollar and Bank XYZ offers only 150 yen for one dollar. Go to Bank ABC and purchase 170 yen. Next go to Bank XYZ and sell the yen for $1.13. In a little more than the time it took to cross the street separating the two banks, you earned a 13% return on your original investment. If the anomaly between the two banks’ exchange rates persists, repeat the transactions. After exchanging currencies at both banks six times, you will have more than doubled your investment.<\/p>\n\n\n\n

Within the FOREX market, triangular arbitrage is a specific trading strategy involving three currencies, their correlation and any discrepancy in their parity rates. There are no arbitrage opportunities when dealing with just two currencies in a single market. Their fluctuations are simply the trading range of their exchange rate.<\/p>\n\n\n\n

We mention this because in many ways the following analysis of the USD as a leading indicator resembles polygonal arbitrage, that is, numerous USD currency pairs and cross rates examined at the same time.<\/p>\n\n\n\n

Cross Correlations<\/h2>\n\n\n\n

We can tell from simple observation that numerous currency pairs move in cadence with each other. For example, examine the following two OHLC charts:<\/p>\n\n\n\n

\"First
First currency pair chart.<\/figcaption><\/figure>\n\n\n\n
\"Second
Second currency pair chart.<\/figcaption><\/figure>\n\n\n\n

Both charts clearly show the same overall upward trend. However, the first appears to start an abrupt upward breakout in early May that does not appear in the second chart until June. It is exactly these temporal shifts between currency pairs that we wish to explore, to determine which pairs are leaders and which are laggers.<\/p>\n\n\n\n

Preliminary Tests<\/h2>\n\n\n\n

Before testing for leading and lagging properties of USD currency pairs, we want to perform one preliminary operation on the major USD currency pairs: a cross-correlation to determine how closely various pairs match each other’s price movements.<\/p>\n\n\n\n

For this we use daily closes for the period 1\/1\/02 through 12\/31\/02. Because of the diversity in the range of parity rates (1 GBP = 1.56 USD while 1 NZD is only 0.49 USD), we must first massage the raw data into normalized Z-statistics:<\/p>\n\n\n\n

Z-Stat (x) = (Price(x) \u2013 Mean) \/ StdDev<\/p>\n\n\n\n

This transformation molds the raw data into a sequence ranging from +4 to \u20134 while breakouts and reversals are left intact temporally. Additionally, where the USD is the base currency in the pair, the inverse reciprocal of the daily close must be used prior to normalizing the data. An ordinary least squares regression is then used to calculate the coefficient of correlation between selected USD currency pairs.<\/p>\n\n\n\n

\"Table
Correlation coefficients between major USD currency pairs, 1\/1\/2002 to 12\/31\/2002.<\/figcaption><\/figure>\n\n\n\n

This table shows how closely the closing prices of one USD currency pair match the closing prices of another over the same period. For example, the highest coefficient of correlation between any two pairs in the table is 99.62% (row 1, column 3), for EURUSD and USDCHF. This is confirmed by the fact that the corresponding cross rate, EURCHF, has the lowest coefficient of variation (standard deviation) of all the major cross rates (see Part 2).<\/p>\n\n\n\n

Very unexpected is the coefficient of correlation of 32.36% for GBPUSD and USDCAD (row 2, column 5). In fact, the average correlation for the fifth row (or column) shows that the Canadian dollar marches to the beat of a different drummer.<\/p>\n\n\n\n

Testing Approach<\/h2>\n\n\n\n

We hope to develop some trading rules that can be applied while monitoring live data in the dealer’s trading platform. So we start with the smallest non-streaming, equi-spaced time interval: one-minute data. We target a single currency around which the analysis will revolve. Since the USD is the most heavily traded currency, the target currency is the USD.<\/p>\n\n\n\n

Our hypothesis is that certain USD currency pairs will lead other USD currency pairs in price movement. This information can be very profitable on a trading platform that allows the trader to display multiple charts simultaneously and to execute orders in less than 5-10 seconds.<\/p>\n\n\n\n

Next we must select which foreign currencies to include. We wish to test, for example, whether, if the GBPUSD begins a sharp downward reversal, the AUDUSD will exhibit the same price movement after 1 minute, after 2 minutes, or as a gradual move after 10 minutes. Or is the AUDUSD totally unaffected by sharp changes in the GBPUSD?<\/p>\n\n\n\n