Does Forex evolve towards efficiency?

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Written by Forex Automaton   
Sunday, 25 March 2012 16:11

During the past 10 years, liquidity in the FX markets is known to have been growing. One might expect that various non-commercial participants, including those with capabilities to research, fund and execute systematic and algorithmic trading strategies, exhaust the alpha-generating potential of this market and drive it towards efficiency. Do spot foreign exchange markets really evolve towards efficiency? I use the ForexAutomaton CERPI 1H.1M inefficiency index (hourly scale, monthly data accumulation period) to look at the trend of the past 10 years (2003-2012), and compare the picture with that of the evolution in the instantaneous ("non-predictive") correlation strength (CERCSI, pronounced "Sir-see") and volatility.


Evolution of CERPI since 2003 1.1 Evolution of CERCSI since 2003 1.2 Evolution of FX volatility since 2003 1.3

Fig.1. Monthly data from 2003-2012, using the 14 currency exchange rates: AUD/JPY, AUD/USD, CHF/JPY, EUR/AUD, EUR/CHF, EUR/GBP, EUR/JPY, EUR/USD, GBP/CHF, GBP/JPY, GBP/USD, USD/CAD, USD/CHF, USD/JPY. 1.1: evolution of CERPI 1H.1M, 1.2: evolution of CERCSI 1H.1M, 1.3: evolution of hourly logarithmic volatility. Source of data: ForexAutomaton monthly correlation analysis reports.

Definitions of CERPI (Currency Exchange Rate Predictability Index) and CERCSI (Currency Exchange Rate Correlation Strength Index) are given in the separate articles. In brief, CERPI measures the strength (by absolute value) of the 1-hour lagged Pearson correlation coefficients among the individual time series (intermarket correlations) as well as inside each time series (autocorrelations).

As an estimator of logarithmic volatility, a simple average of the square root of the autocorrelation peak values over the 14 currency pairs is taken. As with CERPI and CERCSI (also logarithmic measures), the time scale of this measurement is hourly and the time interval is month.

The time evolution plots of CERPI, CERCSI and volatility are shown in Fig.1. I will start the discussion with volatility as the most familiar and intuitive of the three.

As Fig. 1.3 shows, the "old good" days of relatively tame volatility ended in August 2007 with the Fed starting to cut interest rates in response to the sub-prime crisis. Volatility culminated during the selling climax of 2008. After that, the distinct peaks are the Flash Crash of 2010 and the US sovereign credit rating downgrade of 2011. Fig. 1.2 shows that these bouts of panic are characterized not only by the high volatility, but by unusually high degree of correlation among the instruments (CERCSI). Portfolio diversification breaks down as the universe of financial instruments degenerates into risky assets and safe haven ones. This degeneration, even though it may be linked with high volatility via investor psychology and via the mechanics of the over-leveraged markets, does not follow from a rise in volatility mathematically, and represents an independent aspect in the quantitative description of the panic phenomenon.

Importantly, Fig 1.2 shows how a rise in CERCSI preceded the rise in volatility and the market crash of 2008. From the CERCSI point of view, the markets never fully recovered after the crash. Right now another divergence between CERCSI and volatility appears to be forming as the present level of CERCSI is in the higher end of its range while the volatility is at its lowest level since Summer 2008 amid investor complacency.

Finally, CERPI, the predictability index, Fig. 1.1, is in a long-term declining trend. The long-time peak of volatility (October 2008) is followed by local maxima in CERCSI and CERPI (November 2008). But overall, events of the financial crisis did very little to leave any outstanding imprint on CERPI. In contrast with Fig. 1.2 and 1.3, Fig. 1.1 shows no clear evidence of any tectonic shift triggered by these events. Curiously, the historic minimum of predictability took place in March 2009: apparently, the markets exhausted by panic were the closest to the academic extreme of the efficient market.

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