AUD/JPY and EUR/CHF 2002-2008: leader-follower correlations

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Written by Forex Automaton   
Monday, 30 June 2008 16:57

Australian Dollar/Japanese Yen and Euro/Swiss Franc form a remarkable combination: both quote currencies are well known low interest carry-trade funding currencies. In addition, both pairs are, so to speak geographically localized: AUD/JPY in the Asia-Pacific and EUR/CHF in Europe. The intermarket correlation analysis shows that these cross-rates have a leader-follower relation, with Aussie being the leader.

AUD/JPY and EUR/CHF volatility comparison

Fig.1: comparing volatilities of hour-by-hour logarithmic returns in AUD/JPY and EUR/CHF during the American trading session.

As Fig.1 shows, AUD/JPY is more than three times more volatile by RMS, of hourly logarithmic returns, compared to EUR/CHF.

Table: Pearson correlation coefficient for the time series of logarithmic returns in AUD/JPY and EUR/CHF in various trading sessions in 2002-2008. Time frames of the sessions are shown in New York time.

time scale Asia-Pacific session European session American session
hour 0.14 0.17 0.19

AUD/JPY and EUR/CHF are weakly positively correlated on average for the period. Looking at the 0-hour time lag, which is what the table represents, the correlation is the least pronounced in the Asia-Pacific session, most pronounced in the European and American session.

AUD/JPY and EUR/CHF intermarket correlation 1 hour time-lag bin

Fig.2: Cross-correlation of AUD/JPY and EUR/CHF, derived from the hour-by-hour logarithmic returns, for the three trading sessions. Time frames of the sessions are shown in New York time.

The fact that most of the correlation is concentrated at the 0 lag means that the correlation (reported in the table) works out mostly on the time scale of up to 1 hour. For the purpose of the forex trading system development, correlations with non-zero time lag are of particular importance. It is these correlations that allow us to make forecasts, and they are visible in the figure: the peak around the 0 time lag bin is asymmetric and extends to the left. This statement is quantified and supported by comparison with statistical noise in Fig.3.

AUD/JPY and EUR/CHF intermarket correlation 1 hour time-lag bin with uncertainty estimate

Fig.3: Cross-correlation of AUD/JPY and EUR/CHF for the American trading session shown against the backdrop of statistical noise (red). The noise is obtained from martingale simulations based on the recorded volatilities of AUD/JPY and EUR/CHF in this trading session for the period under study. The noise is presented as mean plus-minus 1 RMS, where the RMS characterizes the distribution of the correlation value obtained for each particular bin by analyzing 20 independent simulated pairs of uncorrelated time series.

We inspect significance of the predictive correlation in the AUD/JPY and EUR/CHF exchange rates by comparing with the expected statistical fluctuations (noise) in Fig.3, as explained in the figure caption. The signal in the -1 and -2 hour time lag bin looks quote comfortably significant. In our convention, the time lag is

t1-t2

where "1" denotes AUD/JPY and "2" denotes EUR/CHF.

Therefore, the interpretation of the peak's tail extending to the left into the area of negative lags is as follows: AUD/JPY leads and EUR/CHF follows in the same direction within 0 to 3. hours. Again we see a pair with the stronger interest rate differential show the way to a pair with a weaker interest rate differential, despite the fact the AUD represents a lot smaller economy compared to the European Union. Likewise, in the AUD/JPY and EUR/USD analysis we see that it is the AUD/JPY who leads, because of the greater interest rate differential and despite the the fact the EUR represents the far greater economic power of the European Union. A similar conclusion is made about AUD/USD and USD/CAD. Interest rates dominate the forex dynamics, if the statement needs another quantitative proof, but it's amazing that the adjustments happen slowly enough so they can be detected with one-hour time-bin analysis presented here.

The data used are from the period 2002-08-20 00:00:00 to 2008-02-01 00:00:00.

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