Swiss Franc (CHF) LIBOR: technical predictability overview

I’ve outlined the original motivation to study historical LIBOR data from predictability point of view in the USD LIBOR article. I continue with the logarithmic returns technique that proved useful in forex. Like the previous reports, this document begins with historical LIBOR charts for the Swiss Franc, continues with volatility analysis, and culminates with autocorrelations and correlations. You will see that predictable patterns in CHF LIBORs vary with duration term. Autocorrelations of short-term LIBORs show fast (about 4-day period) oscillation. For 3-month and 6-month terms, the main correlation pattern does not develop 70-day period waves on top of positive background, in contrast to USD and EUR LIBORs, but keeps oscillating between positive and negative autocorrelation values, with the oscillation period longer than that of the shorter terms. The autocorrelation of 12-month LIBOR remains similar to 6-month instead of becoming more uniformly positive as it does for JPY or more jittery as it does for USD, EUR and GBP.

LIBOR charts

History of s/n-o/n CHF LIBOR 2002-2008 History of 1 week CHF LIBOR 2002-2008 History of 1-month CHF LIBOR 2002-2008 History of 3-month CHF LIBOR 2002-2008 History of 6-month CHF LIBOR 2002-2008 History of 12-month CHF LIBOR 2002-2008

Fig.1: Historical CHF LIBOR rates charts, top to bottom: s/n-o/n, 1-week, 1-month, 3-month, 6-month and 12-month. Time axis is labeled in MM-YY format.

The evolution of visual features with maturity here resembles that of other currencies, except for the fact that for CHF, longer maturities show considerably less “initiative” in developing their own trends. Their history looks more like a dumb version of the shorter ranges. For the short maturities, the markets jump the gun trying to anticipate the course of events almost regularly, to the extent this nervousness must represent a regular and significant speculative opportunity, if the market instruments tied to the LIBOR rates have the same features. This will be seen qunatitatively in the correlation plots.

LIBOR volatility

Table 1: Day-by-day volatilities (RMS) for the time series of logarithmic returns in CHF LIBOR in 2002-2008, various maturities

durationtime scalevolatility (RMS)
s/n-o/nday7.3×10-2
weekday4.8×10-2
monthday2.6×10-2
3 monthsday2.2×10-2
6 monthsday2.2×10-2
12 monthsday2.4×10-2

Volatility of CHF LIBOR seems to go down with duration in a more reliable fashion than for other currencies, in particular, USD and EUR. Like JPY, logarithmic returns in CHF look very volatile — this is because the market think about interest rate variations in the “absolute”, not relative sense, and because JPY and CHF interest rates are low, therefore interest rate moves worthy of market’s attention are relatively large for these markets.

Distribution of logarithmic returns in s/n-o/n and 1-week CHF LIBOR rates Distribution of logarithmic returns in 3-month, 6-month and 12-month CHF LIBOR rates

Fig.2: Distributions of logarithmic returns in CHF LIBOR rates, top: s/n-o/n and 1-week, bottom: 1-month, 6-month and 12-month maturity. Volatility is a measure of the width of the return distribution.

The distribution of logarithmic returns look broader than power-law. Remember that with returns already containing logarithm and with the vertical axis explicitly logarithmic, we are looking at what is effectively a log-log plot, where any power law dependence would have looked linear, with different power law exponents resulting in different slopes.

LIBOR autocorrelations

As with some of the most volatile forex exchange rates and all LIBORs looked at so far, the most prominent feature of the s/n-o/n and 1-week CHF LIBOR autocorrelations is the “bipolar disorder” pattern seen from the bins with large negative signal surrounding the zero-time lag bin. (The expression “bipolar disorder” in relation to the market is credited to Benjamin Graham). Continuing with the psychiatric analogy, these indicate rapid (next day or two, depending on LIBOR term) changes in the mood of the credit market, a price action followed by an immediate correction.

CHF s/n-o/n LIBOR autocorrelation, 1 day time scale CHF 1-week LIBOR autocorrelation, 1 day time scale CHF 1-month LIBOR autocorrelation, 1 day time scale CHF 3-month LIBOR autocorrelation, 1 day time scale CHF 6-month LIBOR autocorrelation, 1 day time scale CHF 12-month LIBOR autocorrelation, 1 day time scale

Fig.3:Autocorrelation of logarithmic returns in the historical CHF LIBOR is shown against the backdrop of statistical “noise”. The noise is obtained from martingale simulations based on the historical volatilities of LIBOR for the period under study. The noise is presented as mean plus-minus 1 RMS, where RMS characterizes the distribution of the correlation value obtained for each particular time lag bin by analyzing 20 independent simulated uncorrelated time series. The RMS is a measure of accuracy in the determination of the correlation values, an irreducible uncertainty dependent on the amount of data and the time scale. Top to bottom: s/n-o/n, 1-week, 1-month, 3-month, 6-month, and 12-month data.

Fig.3 and subsequent figures ascertain the significance of the patterns by comparing with the statistical noise estimate, based on simulations devoid of correlations, but with volatility of the actual data. 1-month and longer term LIBOR autocorrelations have broad peaks around zero. This is very different from forex exchange rates, and implies that in LIBOR, medium-range (several days) forecasting is straighforward for these maturities: betting on the continuation of a trend is the winning strategy. In other words, trend following is possible with medium-range LIBOR.

The predictive positive zero-lag peak of 1-month and longer maturities has to be contrasted with the opposite feature seen in shorter maturities, namely the “bipolar disorder”, a tendency to form patterns where the strategy of betting on the trend reveral is more likely to succeed. This tendency shows up in the negative correlation magnitude at the lag that corresponds to the time it takes for the trend reversal. In CHF LIBOR, s/n-o/n and 1-week data, the time is no more than 2-3 days. Trend following is not a viable strategy with s/n-o/n and 1-week LIBOR: here, betting on the next-day trend reversal or using longer range correlations, some of which just as sharp, seems to be the surest strategy.

Cross-correlations of LIBOR terms

Next, I am going to look at correlation between LIBOR rates of different maturities for various time lags. These help answer the question to what extent one LIBOR term can be predicted on the basis of any others. The zero-lag correlation magnitudes between different LIBOR terms are presented as Pearson correlation coefficents in the table. The figures focus on the correlation shapes at the time lags surrounding the zero-lag peak.

Table 1: Pearson correlation coefficients between CHF LIBOR in 2002-2008, various maturities

durations/n-o/n1-week1-month3-month6-month12-month
s/n-o/n10.730.510.320.250.17
1-week10.620.460.350.28
1-month10.770.660.51
3-month10.850.66
6-month10.85
12-month1

The correlation of different maturity terms (which is roughly the square root of the zero time-lag peak amplitude) is seen to go down as the difference in maturities grows; similar maturities are correlated tighter. Overall, the evolution with LIBOR maturity term is the same as described for autocorrelations.

Correlations between s/n-o/n and longer term LIBOR rates

Correlation between logarithmic returns in s/n-o/n and 1-week CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in s/n-o/n and 1-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in s/n-o/n and 3-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in s/n-o/n and 6-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in s/n-o/n and 12-month CHF LIBOR rates as a function of time lag, days

Fig.4: Correlation between logarithmic returns in s/n-o/n and, top to bottom: 1-week, 1-month, 3-month, 6-month and 12-month CHF LIBOR rates as a function of time lag, days, shown against the backdrop of statistical noise (red). The noise is obtained from martingale simulations based on the historical LIBOR volatilities for the period under study. 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.

Correlations between 1-week and longer term LIBOR rates

Correlation between logarithmic returns in 1-week and 1-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in 1-week and 3-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in 1-week and 6-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in 1-week and 12-month CHF LIBOR rates as a function of time lag, days

Fig.5: Correlation between logarithmic returns in 1-week and, top to bottom: 1-month, 3-month, 6-month and 12-month CHF LIBOR rates as a function of time lag, days, shown against the backdrop of statistical noise (red). The noise is obtained from martingale simulations based on the historical LIBOR volatilities for the period under study. 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.

Correlations between 1-month and longer term LIBOR rates

Correlation between logarithmic returns in 1-month and 3-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in 1-month and 6-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in 1-month and 12-month CHF LIBOR rates as a function of time lag, days

Fig.6: Correlation between logarithmic returns in 1-month and, top to bottom: 3-month, 6-month and 12-month CHF LIBOR rates as a function of time lag, days, shown against the backdrop of statistical noise (red). The noise is obtained from martingale simulations based on the historical LIBOR volatilities for the period under study. 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.

Correlations between 3-month and longer term LIBOR rates

Correlation between logarithmic returns in 3-month and 6-month CHF LIBOR rates as a function of time lag, days Correlation between logarithmic returns in 3-month and 12-month CHF LIBOR rates as a function of time lag, days

Fig.7: Correlation between logarithmic returns in 3-month and, top to bottom: 6-month and 12-month CHF LIBOR rates as a function of time lag, days, shown against the backdrop of statistical noise (red). The noise is obtained from martingale simulations based on the historical LIBOR volatilities for the period under study. 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.