used options on the S&P 500 index (SPX)rather than the S&P 100 (OEX).For the sake of consistency the CBOE has created a historical record for the newVIX dating back to 1986 and continued the calculation of the original OEX VIX, butunder the new ticker symbol “VXO”.Generally VIX values greater than 30 are associated with a large amount of volatility as a result of investor fear or uncertainty, while values below 20 generally correspond to less stressful, even complacent, times in the markets. Traders who want totrade the VIX directly can do so by purchasing futures or options on the VIX whichtrade actively on the CBOE. The contract size is $1000 the VIX and the symbol isVX.Futures traders are most familiar with the fair value of stock index futures derivedfrom the cost-of carry relationship between the futures and the underlying stock index.Since there is no carry between VIX and a position in VIX futures, the fair valueof VIX futures cannot be derived by a similar relationship. Instead the fair value isderived by pricing the forward 30-day variance which underlies the settlement priceof VIX futures. The fair value of VIX futures is the square root of this expectedvariance less an adjustment factor.The VIX has ranged from a high of 48 in 1998 to a low of about 9 in 1993 (Fig.4.4). The VIX generally declines during periods of orderly market advances andrises during periods of market declines. The steeper the decline, the greater the VIX.
International Indices and Commodities
65
Figure 4.4 Monthly chart of the S&P 500 (top) and the VIX (bottom) from June 1990 toFebruary 2008. Extreme tops in the VIX are marked H1 and H2 and extreme bottoms by L1, L2and L3. Keep in mind that this is a line chart and consequently only closing prices are plotted,which are considerably higher or lower than the extreme intraday highs and lows respectively.
Certain levels of the VIX may predict turning points in the market. It is useful toexamine selected periods and the relationship of the VIX and the market. For example,Figure 4.4 details some extremes in the VIX for the most recent 10-year period from1998 to 2008.Notice that the 2000 and 2002 bottoms in the VIX (marked L1 and L2 respectivelyin Fig. 4.4) coincide with tops in the S&P 500. The VIX usually forms extreme topsor bottoms at the same time as the S&P forms bottoms or tops but sometimes itmay lead the S&P 500. Notice that in January 2007 the VIX declined as low as 10(marked L3 in Figure 4.4) which preceded a major market decline by more than sixmonths.
International Indices and Commodities
67
indicated a sell signal when it fell below a specific level (e.g. 30) and then rose abovethat level.According to the interpretation above, the oscillator issued seven buy signalsand nine sell signals during the period from May 2006 to February 2008. These areoverlaid in the chart in Fig. 4.5. The signals marked H2, H3 and H5 correctly identifiedmarket bottoms while the other four buy signals were early by approximately twoweeks on average. Only two signals (marked L7 and L9) correctly predicted markettops while the rest of the sell signals were premature. In these cases the time periodthat the VIX was early in predicting a market top ranged from two weeks to fivemonths.In view of the above we can conclude that the VIX, in spite of correctly predictingsome market turning points, was early in predicting more than half of the market topsor bottoms. It is not, therefore, the “Holy Grail” that some people believe it is andshould only be used together with other indicators for more accurate market timing.
66
Intermarket Trading Strategies
Figure 4.5 2-day chart of the S&P 500 (top) and the VIX (bottom) from May 2006 toFebruary 2008. The 75 bar (150-day) intermarket oscillator is plotted in the middle chart.Extreme tops of the IM momentum oscillator (over 80) are marked H1–H7 and extremebottoms (below 30) by L1–L9.
The absolute level of the extremes in the VIX is not very important and varies overtime. The first two bottoms (L1 and L2) occurred with the VIX around 17, but duringthe third extreme bottom in 2007 the VIX declined as far as 10.Tops in the VIX indicate market bottoms. The VIX rose above 40 in August1998 (marked H1 in Figure 4.5) and in October 2002 (marked H2) which coincidedwith the end of the correction in 1998 and the end of the bear market in 2002respectively.To determine whether the VIX is useful in predicting short-term market topsor bottoms in real-time trading, let’s examine the 2-day chart in Fig. 4.5. To helpcorrectly identify the tops and bottoms in the VIX, I used the 150 day (75 bar ina 2-day chart) intermarket momentum oscillator, described fully in Chapter 9. Theinterpretation of this indicator is similar to the Williams’ %R Oscillator. Buy signalswere considered when the oscillator rose above a specific level (e.g. 80), formed apeak and subsequently reversed direction and fell below that level. The oscillator
5The S&P 500
Interpretation is the revenge of the intellect upon art.–Susan SonntagThe calculation of the correlation coefficient between the index to be predicted andother related markets is the first step of a complete and thorough intermarket analysis.Of course, the final challenge comes from interpretation. Intermarket analysis relieson the premise that relationships in the past will be the same in the future so beforemaking any conclusions it is always prudent to assess the stability of the correlationcoefficient over time.I chose to start this correlation analysis with a broad market index like the S&P500 which is widely regarded as the best single gauge of the US equities market.
5.1
CORRELATION WITH INTERNATIONAL INDICES
The first step in a correlation or regression analysis is to decide whether you want toanalyze the price on a daily, weekly, monthly or quarterly basis. Using long time scaleshas the added apparent theoretical benefit that averaging over long periods suppresseswindowing errors and daily noise, revealing the underlying long term correlations.The choice of time periods is therefore very important and should depend on atrader’s time horizon. This is illustrated in Tables 5.3 and 5.4 where the correlationrises dramatically when weekly returns replace daily data. As the time segment isreduced, noise becomes a bigger factor in the calculation and the correlation dropsdue to an abundance of change which is unfolding in the short term.To better understand the evolution of correlations over time in Table 5.1 I havecalculated price correlations between the S&P 500 and major world indices yearlyfrom 1992 to 2007, in Table 5.2 up to 25 years long- and short-term nonparametric price correlations and in Table 5.3 nonparametric correlations between daily
70
Intermarket Trading Strategies
Table 5.1 Yearly correlation between daily prices of the S&P and major world indices.Symbols: DJ-30 is the Dow Jones Industrials, RUT is the small cap Russell 2000 index, NDXis the Nasdaq 100, TSX is Canada’s S&P TSX Composite Index and Euro Stoxx is the DowJones Euro Stoxx 50 Index which tracks the 50 most important stocks in the Euro zone. The10th percentile row shows the yearly correlations below which certain percentages of casesfall. So for example, if the 10 % percentile of the correlation between the S&P 500 and theDJ-30 is 0.61, it means that the correlation was below 0.61 only 10 % of the time. The reverseis true for the 90th percentile row. Notice the significant improvement in correlationconsistency during the latest 10-year period from 1998–2007 (bottom four rows).
Year
DJ-30 RUT NDX
EuroTSX Stoxx
DAX
2007200620052004200320022001200019991998199719961995199419931992
0.930.970.770.840.990.980.920.590.870.920.970.990.990.950.930.24
0.920.810.820.860.950.980.960.570.890.380.940.970.900.720.84−0.24
0.840.760.950.920.830.800.910.830.970.950.960.980.950.950.500.610.840.830.610.760.930.900.950.890.780.020.040.340.890.87−0.52 −0.34
0.690.850.950.960.980.960.860.460.830.280.930.730.960.750.920.49
0.700.750.920.940.980.850.940.670.810.800.910.970.950.770.730.52
0.900.940.840.870.930.980.950.590.870.66NANANANANANA
CAC FTSE Nikkei
HangSeng
0.860.870.840.760.970.980.950.640.860.690.960.850.980.680.880.45
0.210.460.520.900.730.760.020.750.840.870.760.850.840.930.350.590.810.89−0.020.20−0.22 −0.04−0.090.930.230.92−0.180.700.150.83−0.180.14
0.820.860.150.530.450.98
0.300.670.220.800.400.311.060.97−0.22 −0.040.840.93
1992–2007AverageMedianStd DevRange10th Per90th Per
0.870.930.200.750.490.99
0.790.860.210.700.400.97
0.830.830.130.460.520.97
0.770.870.311.21−0.240.97
NANANANANANA
0.710.690.870.830.410.371.491.32−0.52 −0.340.960.96
1998–2007AverageMedianStd DevRange10th Per90th Per
0.880.920.120.400.610.99
0.780.850.240.700.300.98
0.840.830.110.310.670.98
0.810.870.190.600.400.98
0.850.890.130.390.600.98
0.840.870.160.470.510.97
0.840.830.120.370.620.98
0.840.860.120.330.650.98
0.500.620.340.860.000.84
0.720.800.240.730.230.93
The S&P 500
71
Table 5.2 Long- and short-term Spearman’s ρ nonparametric correlation between the S&Pand major world indices. High correlations are in bold. Abbreviations: H = heteroscedasticitypresent (non homogeneous data), CL = curvilinear relationship, NL = non linear, NA =correlation coefficient not available because of insufficient data.
Years DJ-30
RUT
NDX
TSX
EuroStoxx DAX CAC FTSE
Nikkei
HangSeng
234510152025
0.910.970.990.990.57 H0.87 HNANA
0.930.930.950.970.93 CL0.98 CL0.99 CLNA
0.960.980.990.990.71 CL0.91 CL0.94 CL0.94 CL
0.980.990.990.990.89NANANA
0.580.850.900.94 H0.81 NL−0.3 NLNANA
0.920.960.980.98 CL0.87 CL0.82 CL0.92 CLNA
0.980.980.970.980.880.960.990.99
0.970.980.990.990.890.96NANA
0.960.980.980.990.910.96NANA
0.960.980.980.990.900.960.98NA
percentage returns of the S&P 500 and major world indices. The data used to derivethe correlation coefficients are up to the end of 2007. You can find up-to-date valuesof nonparametric price correlations at http://www.csidata.com.Care should be taken before using any of the numbers, especially the long-termprice correlations in Table 5.2, as some of the relationships were not linear. I havemarked these to indicate deviations from linearity, but in any case you should checka scatterplot or a chart of the related index before making a significant investmentdecision based on these numbers. For example, in Tables 5.1 and 5.2 the correlationcoefficient between the S&P 500 and Russell 2000 in 2004 and 2005 is indicatedhigher than the corresponding correlation with the DJ-30. However, looking at therelevant scatterplots in Figs. 5.1 and 5.2, we can see that higher values of the Russell2000 (Fig. 5.2, top right) are not distributed evenly around the linear regression lineTable 5.3 Long- and short-term Spearman’s ρ nonparametric correlation between the S&PDaily % returns and major world indices. The 15-year correlation of the DJ Euro Stoxx 50was not calculated as only nine years of data were available for this index.
Years
DJ-30
RUT
NDX
TSX
EuroStoxx DAX
CAC
FTSE
Nikkei
HangSeng
2451015
0.950.950.960.930.92
0.890.840.840.810.77
0.880.880.880.840.80
0.650.620.620.650.60
0.480.440.460.44NA
0.490.430.480.490.40
0.510.430.450.440.39
0.500.420.420.420.39
0.120.110.110.130.11
0.130.100.100.130.13
avg.
0.94
0.83
0.86
0.63
0.46
0.46
0.44
0.43
0.12
0.12
72
Intermarket Trading Strategies
1500
1400
S&P-500
1300
1200
1100
1000
900R Sq Linear = 0.9638007000
8000
9000
10000
11000
12000
13000
DJ-30
Figure 5.1 Scatterplot of 2-year closing prices of the S&P 500 vs. the Dow Jones Industrials.Although the homogeneity of variance around the regression line is not perfect, it is still betterthan the plot in Fig. 5.2.
and, as discussed in Chapter 2, this could distort the correlation coefficient betweenthe two indices.This is not usually a problem when transforming price data to differences orpercentage yields. For example consider the scatterplots in Figs. 5.3 and 5.4 whichare both between the S&P and the VIX. In Fig. 5.4 price data was converted to weeklyyields.As can be seen from Table 5.1 and also the graph in Fig. 1.4 (Chapter 1), thecorrelations are not constant over time, sometimes (although rarely) alternating fromweak negative to strong positive.In order to examine more closely the volatility of these correlations I have calculated at the bottom of Table 5.1 the average, median, standard deviation and the
The S&P 500
73
1500
1400
S&P-500
1300
1200
1100
1000
900
800300
400
500
600
700
800
RUSSELL2000
Figure 5.2 Scatterplot of 2-year closing prices of the S&P 500 vs. the Russell 2000 smallcap index. Notice the increase in variance at the top right of the plot.
maximum range (highest minus lowest correlation value over each time span). Thestandard deviation, however, may not be the best measure of variance as most valueswere concentrated at the far right of the histogram (negative skew) and the correlationdistribution deviated significantly from the normal Gaussian curve.To deal with this problem I divided the yearly correlations in percentiles accordingto their level. So for example, if the 10 % percentile of the correlation between theS&P 500 and the DJ-30 is 0.61, it means that only 10 % of the yearly correlations werebelow 0.61. The correlation was more stable over time with high captalization indiceslike the DJ-30 and the FTSE. Small capitalization stocks, represented by the Russell2000, had the most unstable relationship. In fact, correlation with European stockswas less volatile than with its US compatriot. Their correlation with the Euro Stoxxbroke down during the most recent 10-year period only twice, in 2000 and 1998.
74
Intermarket Trading Strategies
1600
1400
S&P-500
1200
1000
800
600
4000
10
20
30
40
50
60
VIX
Figure 5.3 Scatterplot of the CBOE Volatility Index (VIX) vs. the S&P 500 over a 15-yearperiod from 1993 to the end of 2007.
The correlation between the S&P 500 and other international and emerging market equities was highly erratic when considering yearly time segments, and over the16-year study (Table 5.1), the relationship also varied substantially. This is becauseinternational relationships can change as the forces that drive earnings in Europe, Asiaand elsewhere aren’t identical to what is unfolding in Chicago and Silicon Valley. Thecorrelation with British stocks was an exception because, although the correlation wasnot as strong as with the DJ-30, it was less volatile over time. The index with the weakest and most unstable correlation was the Nikkei 225 and this was evident in all timeframes.Rather than rely on historical correlations, a more comprehensive and dynamicapproach is needed when making trading decisions. The number and frequency ofthese changes can complicate the design of intermarket trading systems as they rely
76
Intermarket Trading Strategies
Table 5.4 Long- and short-term nonparametric correlation between S&P weekly % returnsand major world indices.
Years
DJ-30
RUT
NDX
TSX
EuroStoxx
DAX
CAC
FTSE
Nikkei
HangSeng
2451015
0.950.950.960.920.92
0.860.860.870.820.79
0.840.850.870.840.79
0.690.670.690.740.70
0.770.750.750.74NA
0.760.730.720.730.66
0.780.730.740.730.65
0.740.670.680.700.39
0.460.440.460.430.38
0.490.460.470.500.45
avg.
0.94
0.84
0.84
0.70
0.75
0.72
0.73
0.64
0.43
0.47
0.94 for the last two years (Table 5.1), up sharply from the 0.59 to 0.86 during theperiod from 1998 to 2000. Also the correlation of weekly yields with the FTSE hasimproved from 0.39 to 0.74 and with the DAX from 0.66 to 0.76 during the latest2-year period (Table 5.4).This trend is widely acknowledged for European indexes, but what is less acceptedis that correlations between US and many other non-European indexes have alsoincreased.A comparison between Tables 5.2 and 5.3 reveals a significant deterioration of thecorrelation between the S&P and all non-US exchanges when comparing correlationsbetween daily percentage yields instead of prices. This can be partly attributed (exceptfor the TSX) to different time zones. Most European exchanges close at 11:30 US ETtime, so only two hours of US trading is taken into account. This is a real problem incalculating the actual real-time correlation. One solution is to use Intraday 30-minutedata and correlate the S&P change between 11:30 the previous day and 11:30 thenext day but this will involve splitting a trading day in two. Obtaining 15 years ofintraday data is also a problem. Another possible alternative is to compare the S&Pwith European index futures data as most of them close long after regular hours.Conveniently, the DAX and Euro Stoxx futures closing time happens to coincidewith the US market closing time at 16:00 ET. Of course there is no overlap betweenthe US and Asian markets so in order to predict Nikkei daily changes based on S&Pchanges the S&P has to be shifted one day in the past.You should therefore take into consideration the trading hours of each index asnon-US indices (except Canada’s TSX and other American indices) trade duringdifferent time zones. The S&P is the last to close and therefore international indicescan only be used to predict the S&P (short term) and not the other way around. Forexample, to derive the daily correlation between Japan’s Nikkei and the S&P 500,the S&P 500 has to be shifted one day back in time as the current day’s closing priceis not known during Japanese trading hours. You can find trading hours for all theExchanges in Chapter 4 (Table 4.1).
The S&P 500
75
15.012.510.0
S&P %WEEKLY YIELD
7.55.02.50.0−2.5−5.0−7.5−10.0R Sq Linear = 0.508
−12.5−15.0−40 −30 −20 −10
0
10
20
30
40
50
60
70
80
VIX % WEEKLY CHANGE
Figure 5.4 Scatterplot of weekly % changes of the CBOE Volatility Index (VIX) vs. weekly% returns of the S&P 500 over the 15-year period from 1993 to the end of 2007. The relationshiphas become approximately linear when taking percent yields.
on the principle that relationships in the past would be the same in the future. Marketsmay decouple and a strategy based on that assumption might produce considerabledrawdown.Taking into account the predicted future correlation rather than historical valuescould be advantageous in intermarket system design or long-term forecasting. Oneway of making a prediction of what the correlation is going to be in the future isto adjust the correlation coefficient, in order to take into account the most recenttrend, by the trend or rate of change of the correlation during the most recent 5-yearperiod.The impact of globalization and free money flow across national borders can beseen in the rising correlations between the S&P 500 and international indices. TheS&P 500 and Euro Stoxx 50 have been posting correlations in the range of 0.9 to
The S&P 500
5.2
77
INTEREST RATES, COMMODITIES, FOREX AND THE VIX
The importance of changes in interest rates and their impact on equity markets haslong been recognized by astute Wall Street analysts and technicians. Martin Zweigin his classic Winning On Wall Street provided a stock market timing model which isbased on changes in the prime rate.The old “Don’t fight the Fed” maxim for years suggested that when the Fed startslowering rates, it is time to start buying stocks. Conversely, when the Fed beginsto raise rates, it is usually time to head for the exit. Unfortunately, investors whoresponded to this old canard and began buying stocks when the Federal ReserveBoard started lowering rates were burned badly twice: once in 2001, and again inAugust 2007.Over the past seven years we have been confronted with a reversal of the normalnegative correlation between interest rates and share prices. This proves that a systemrelying on the historical correlation alone is not the best approach and a broaderassessment of factors affecting the future relationship between stocks and interestrates is also required.In the search for factors influencing price correlation, market corrections, uncertainties or perceived risks play a fundamental part, as Fig. 5.5 illustrates. The1987 crash, the 1997 Asian financial crisis and the Long Term Capital Management crisis in conjunction with Russia’s bond default triggered a sharp slide in stockprices and a subsequent rally in bonds, as investors fled to safety. This resulted innegative correlation between stocks and bonds (positive between stocks and bondyields).An analysis of correlation at specific periods in the business cycle reveals a fundamental reason for the correlation reversal between stock prices and bond yields: Thecorrelation with interest rates switches to positive during economic recessions. During the 1981–1982 recession bonds rallied, pushing yields lower, while stock pricesdeclined. This pattern was repeated during the more recent recession in 2001–2003(see Fig. 5.5).Apart from the factors already discussed, inflation expectations or rising commodity prices can also induce a correlation reversal because rising inflation is negativefor bonds and therefore positive for yields. Rising oil and agricultural commodityprices induced a correlation reversal again at the beginning of 2008 as illustrated inFig. 5.5.The fluctuation of correlations, however, is hardly limited to stocks and bonds. Thecorrelation of weekly yields between the S&P 500 and the CRB index strengthenedfrom near zero or a non-existent historical correlation to 0.15 as commodities becameeveryone’s new favorite asset class.The increase may not seem a lot but the question is whether the trend will continue.Factors that may have caused the correlation upturn may be the dollar’s demise oreven the money still rolling into commodities by way of the growing number of
78
Intermarket Trading Strategies
Figure 5.5 Weekly chart of the S&P 500 and 10-year treasury yields (TNX) from 1978 untilMay 2008. The top chart is the 18-month correlation between the S&P 500 and the TNX.Notice how the traditional long-term negative correlation turns positive in times of financialturmoil like the 1987 market crash, the 1997 Asian crisis and the Russian bond and subsequentLong Term Capital Management crisis (shaded in grey). Notice also how, at the beginning of2000, the long-term correlation bias turned from negative to positive.
exchange-traded funds targeting this asset class. My opinion is that the correlationwill revert to the mean as soon as the dollar index stabilizes and commodity pricesreturn to their historical range.The correlation of the S&P 500 with gold has also reversed from weak negativeto moderate positive. If you are interested in gold a complete correlation analysis isincluded in Chapter 7 and a comparison of 14 intermarket systems in Chapter 11.
251015202530
Years
0.3 NL−0.5 H−0.3 NL0.23 HNANANA
VIX
XOI0.830.98 CL0.54 NL0.85 NL0.93 NLNANA
TNX
−0.25 H0.65 H0.5 NL−0.4 NL−0.8 NL−0.9 NL−0.8 NL0