Patterns of financial crisis: EUR/JPY in 2007-2008.

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Written by Forex Automaton   
Wednesday, 14 January 2009 14:34

This article is first in a series dedicated to the impact of the present financial crisis on the correlation patterns of the popular currency exchange rates. The acute phase of what began as the subprime mortgage crisis in 2007 has, among other things, challenged the ability of financial models (of the variety practiced by large institutions some of which no longer exist) to be of value to their users. Leaving CDOs and credit default swaps aside, with the forex and interest rate data at hand here on the Forex Automaton site, and with the same set of tools as before, I am going to approach the following questions. What exactly changed during the crisis? Did the markets become unpredictable? Less predictable than usual? More predictable in a new way? Was it a qualitative change in the set of predictability patterns? Or was it only a change in the amplitude of the fluctuations? I begin with EUR/JPY, an exchange rate which recently suffered some of the most dramatic changes seen in forex.

Evolution of EUR/JPY exchange rate during the financial crisis, hour.

Fig.1:EUR/JPY during the financial crisis, hour time scale. Time axis is labeled in MM-YY format and spans the interval from August 2, 2007 through December 31, 2008.

I define the visible phase of the present financial crisis to begin on August 16, 2007, the day of Countrywide Financial near-bankruptcy event, followed by an extraordinary half-percent Fed discount rate cut next day. This study covers 74 weeks from August 2, 2007 through December 31, 2008. The sub-range of extreme volatility (as will be seen in Fig.4) can be roughly defined as the last 18 weeks of this 74-week range. In this study, I only look at trading activity taking place from 1am to 1pm New York time, since the experience shows it to be the richest in non-trivial correlations.

EUR/JPY volatility change during the financial crisis, hour.

Fig.2:The histogram of logarithmic returns in EUR/JPY on the hour time scale demonstrates volatility change in the course of the financial crisis.

While the change in the volatility between the beginning of the crisis and its "phase of impact" is indeniable, single-point distributions like Fig.2 do not tell the whole story.

Autocorrelation of logarithmic returns in EUR/JPY,  European trading hours, hour scale, from August 2, 2007 through August 27, 2008. Autocorrelation of logarithmic returns in EUR/JPY,  European trading hours, hour scale, from August 28, 2008 through December 31, 2008.

Fig.3: Autocorrelation of logarithmic returns in EUR/JPY for the European (Eurasian) trading shown against the backdrop of statistical noise (red). Top panel: the measurement time range is for the relatively low volatility phase of the crisis, from August 2, 2007 through August 27, 2008. Bottom panel: same for the high volatility phase, from August 28, 2008 through the end of 2008. The noise is obtained from martingale simulations based on the recorded volatilities of EUR/JPY in the trading hours under study for the period. The noise is presented as mean plus-minus 1 RMS, where RMS characterizes the distribution of the correlation value obtained for each particular bin by analyzing 20 independent simulated uncorrelated time series of the same average volatility. From top to bottom, the shape of the autocorrelation in the vicinity of the zero-time lag peak undergoes a dramatic transformation.

The problem is not just that the volatility got high in the impact phase of the crisis (making the Fig.2 distribution wider and Fig.3 -- higher). The problem is the appearance of a correlation pattern new to EUR/JPY -- the pattern of "bipolar disorder". This pattern has been seen before in forex exchange rates with high interest rate differential at stake, and also in LIBOR time series analyses. The "bipolar disorder" feature, a tendency to form quickly alternating rises and falls on next-hour time scale, more pronounced than in a fully unpredictable time series of the same volatility, shows up as negative deeps surrounding the zero-time lag peak. These have significance of over two standard deviations in the time-integrated correlation of Fig.3, bottom panel. The practical implication of a feature like this is simple: since any movement is likely to be followed by the movement in the opposite direction, it is hard to make money on trend following. Rephrasing the Wall Street adage, bulls get slaughtered, bears get slaughtered, pigs get slaughtered, contrarians (and brokers) make money.

Evolution of EUR/JPY autocorrelation peak structure during the financial crisis, hour.

Fig.4: Evolution of EUR/JPY autocorrelation peak structure during the financial crisis, hour time scale. Time bin is two weeks wide. The peak structure is represented by three correlation values: the one for the zero lag (essentially a volatility measure) downscaled by 10 for easier visual comparison, the one at one hour lag (just discussed) and the one at two hour lag. Time axis is labeled in MM-YY format and spans the interval from August 2, 2007 through December 31, 2008. Only trading hours from 1am to 1pm New York time (European trading hours), usually rich in non-trivial correlations, are included.

The time period of dramatically higher volatility covers the last 9 bins in Fig.4, which contain 18 weeks, from August 28, 2008 through December 31, 2008 (naturally, the choice of dates is to a certain extent forced by the bin width). The magnitude of the 1-hour time lag correlation with respect to that of the zero lag is the highest in the last two week of September, the last two weeks of November, and the last two weeks of December. A decreased predictability? Crash of models? Certainly, if by "predictability" one means ability to follow the trend, and by "model" -- an extrapolation of a trend.

The data used are from the period 2002-08-02 00:00:00 to 2009-01-01 00:00:00, New York time.

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Last Updated ( Monday, 04 January 2010 12:42 )