.2 NL0.88 NL0.37 NL0.61 NL0.53 NLNANA
Crude oil0.4 H0.880.15 NL−0.2 NL−0.3 NLNANA
XAU0.73 H0.96 CL0.12 NL0.00−0.3 NL−0.3 NL−0.070
GOLD
−0.2 NL0.81 H0.22 NL0.17 NL0.06−0.06−0.09
CRB
−0.83−0.77 NL−0.2 NL0.09 HNANANA
Dollar index
−0.3 H0.3 H0.3 NL−0.3 NLNANANA
YEN
Table 5.5 Long- and short-term nonparametric correlation between the S&P, commodities, Forex and the VIX as of 31.12.2007.TNX is the CBOE 10-year Treasury Yield Index, XOI is the AMEX Oil Index, XAU is the Philadelphia Gold/Silver Stock Index,GOLD is the World Gold Index (XGLD), CRB is the Commodity Research Bureau Index and YEN is the JPY/USD exchange rate.Abbreviations: H = heteroscedasticity present (non homogeneous data), CL = curvilinear relationship, NL = non linear, NA =correlation coefficient not available because of insufficient data.
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Intermarket Trading Strategies
Table 5.6 Nonparametric correlation between S&P and major commodity and financialweekly percentage returns.Years
VIX
TNX
XOI
Crude oil
XAU
GOLD
CRB
Dollar index
YEN
251015
−0.81−0.75−0.77−0.70
0.180.200.190.02
0.560.510.440.46
0.02−0.100.030.01
0.470.340.150.12
0.200.100.02−0.01
0.150.080.070.03
−0.17−0.080.060.08
−0.15−0.02−0.02−0.04
avg.
−0.76
0.15
0.49
−0.01
0.27
0.08
0.08
−0.03
−0.06
The positive correlation between the S&P and oil for the last 20 years was alsoa surprise, although this was not consistent over time as their relationship deviatedconsiderably from linearity. This was also the case with the majority of the correlationsin Table 5.5 as the scatterplots revealed that most of them deviated from linearity orhomogeneity. The correlation coefficients of weekly percentage returns, displayed inTable 5.6, however, were more reliable as none deviated excessively from the linearityassumption.A comparison of the scatterplots of the CBOE Volatility Index vs. the S&P in Figs.5.3 and 5.4 makes this more obvious.Inconsistent or nonlinear relationships were also the reason that the high correlations depicted in Table 5.5 have faded away when taking weekly yields (inTable 5.6). A notable exception to the rule was the correlation between the S&P andthe VIX which actually increased when using weekly yields.Activities stemming from the “carry trade”, which have emerged only recently,also managed to reverse the traditional weak positive correlation between the S&Pand the yen to negative during the last three years (Table 5.5 and Fig. 5.6). You canfind more information on the carry trade and the yen in Chapter 17.The first step in running a correlation analysis is to decide on the best time interval for calculating price changes. To understand the basic financial factors behind a market’s course over longer periods of time, more than 10 days of percentage returns should be used generally. On the other hand, weekly percentage yieldsare more suitable for shorter-term trading. The best time period for taking percentagedifferences can also vary from market to market. The objective is to take a variablethat you’re interested in (in this case the S&P 500) and see to what degree its changescan be explained by changes in some other potentially relevant variables for a rangeof different time spans.Table 5.7 lists the correlation between the S&P and related markets using dailyto quarterly percentage returns. As you can see, the correlation peaked at the threeweek interval for some markets (e.g. XOI), while a longer-term time span wasmore appropriate for calculating the correlation with other markets like interest rates(TNX). The VIX was an exception to the rule, as it actually correlated better with
The S&P 500
81
Figure 5.6 Weekly chart of the S&P 500 (in bold) and the Yen (Yen USD thin line) from1992 until February 2008. Notice the alternating periods of positive (gray background)and negative (white background) correlation with the S&P 500 and how, in the beginning of 2005, the unwinding of the “carry trade” reversed the correlation from positive tonegative.
Table 5.7 10-year correlation between the S&P and major commodity and financial indicesusing daily, weekly, 10 trading day (2-week), 15 trading day (3-week), monthly and quarterlyreturns for the 10-year period from the beginning of 1998 to the end of 2007.Timesegment
VIX
TNX
XOI
Oil
XAU
GOLD
CRB
DollarIndex
YEN
DailyWeekly10 day15 dayMonthlyQuarterly
−0.78−0.75−0.74−0.74−0.75−0.70
0.230.240.260.240.240.32
0.480.460.460.480.460.37
−0.02−0.01−0.02−0.03−0.03−0.13
0.080.140.130.130.130.12
−0.040.00−0.02−0.02−0.03−0.01
0.040.060.030.020.03−0.11
0.140.080.080.060.050.13
−0.09−0.06−0.03−0.010.03−0.02
The S&P 500
83
Table 5.9 Nonparametric correlation between the S&P 500 and the 25 worst correlatedstocks. The 5-year Spearman’s correlation coefficient is shown in Column 3 and the stock’sunderlying index in Column 4.Company Name
Symbol
Correlation
PRG-Schultz Int.Tenet Healthcare Corp.European Diamonds PlcSuperior Industries Int.Entercom CommunicationsRamco Oil Services PlcFarmer Bros Co.Trimeris Inc.Fibernet Telecom GroupMolins PlcFifth Third BancorpLibbey Inc.BlockbusterNinety Nine Only StoresNTT Docomo ADSEnzon PharmaceuticalFred’s Inc.Cray Inc.La-Z-Boy Inc.Ballard Power SystemsUtstarcom Inc.Jardine Lloyd ThompsonGoAmerica Inc.Ariba Inc.Cabot Microelectronics Cp
PRGXTHCEPD.LSUPETMROS.LFARMTRMSFTGXMLIN.LFTBLBYBBINDNDCMENZNFREDCRAYLZBBLDPUTSIJLT.LGOAMARBACCMP
−0.907−0.864−0.841−0.833−0.804−0.8−0.791−0.791−0.78−0.779−0.758−0.757−0.756−0.751−0.697−0.696−0.688−0.686−0.679−0.678−0.661−0.658−0.657−0.643−0.639
Index
S&P 500Russell 2000Russell 2000Russell 2000Russell 2000
S&P 500S&P 600Russell 2000Russell 2000NikkeiRussell 2000Russell 2000Russell 2000Russell 2000TSXRussell 2000
Russell 2000Russell 2000
that did not follow the bullish trend during the period under study because of variouscompany specific problems.I wouldn’t advise investing in these stocks as a means of diversification. On theother hand, some of them might recover and catch up with the S&P 500 providedthat their problems are resolved.
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Intermarket Trading Strategies
the S&P 500 on a short-term basis (daily yields). This is probably because theVIX is a sentiment indicator and not a fundamental factor affecting changes in theS&P 500.
5.3
CORRELATION BETWEEN THE S&P 500 AND STOCKS
The 25 stocks with the best (positive) and the worst (negative) correlation with theS&P 500 are depicted in Tables 5.8 and 5.9 respectively, over a 5-year period untilFebruary 2007. Surprisingly, the five best correlated stocks are not S&P componentsbut European stocks or ETFs. The 25 worst correlated stocks were mostly companies
Table 5.8 Nonparametric correlation between the S&P 500 and the 25 best correlatedinternational stocks during the 5-year period from February 2002 to February 2007.Column 3 shows the 5-year Spearman’s correlation coefficient and Column 4 shows thestock’s underlying index.Company Name
Symbol
Correlation
AxaNetherlands Index ETFSage GRPSweden Index ETFEuropean Equity FundEmerson ElectricTri-Continental Corp.Lincoln National Corp.Exxon MobilUnited TechnologiesBoeing Co.Altria GroupChevron Corp.American ExpressPearson PlcPepsicoBP PlcCaterpillar Inc.Public Service EnterpriseAlltel Corp.COGECOMerril Lynch & Co.Dover Corp.CitigroupProcter & Gamble
AXAEWNSGE.LEWDEEAEMRTYLNCXOMUTXBAMOCVXAXPPSOPEPBPCATPEGATCGO.TOMERDOVCPG
0.9860.980.9780.9770.9760.9750.9730.9720.9670.9610.960.9590.9580.9560.9560.9550.950.9490.9470.9460.9410.9340.930.9080.905
IndexCAC40AEXFTSEOMXSPIESTXS&PNAS&PS&PS&PS&PS&PS&PS&PFTSES&PFTSES&PS&PS&PNAS&PS&PS&PS&P
6European Indices
A little uncertainty is good for everything.–Henry Kissinger
6.1
THE DAX
Trading in DAX futures has recently become very popular among US traders asit provides an additional liquid market to trade. The main advantages are currencydiversification and a wider range of trading times. The German futures exchange(Eurex) is open 14 hours a day from 8:00 to 22:00 CET (02:00–16:00 US EST) and thelonger trading times are convenient for both Australian and US-based traders. You canfind an intermarket system for trading DAX futures in Chapter 13, but in order to makemodifications or design your own system you will need a thorough understanding ofits correlation with other international stock indices, currencies and commodities.Correlations between the DAX and major international indices, forex and commodities are depicted in Table 6.1 and correlations with other European indices inTable 6.2. The DAX correlated best, with the Euro Stoxx 50. This was not surprisingas both indices had a number of common component stocks. The second best correlation was with the French CAC 40 index, as both nations–whether they like it ornot–are joined in the economic community. American indices had to be shifted backone day in time in order for the correlation values to have some predictive usefulnessfor the DAX, as all American exchanges closed 41/2 hours later. An alternative approach would be to correlate the DAX opening price with the previous day’s closingprice of the corresponding American index. This improves the correlation coefficientfrom 0.134 to 0.422 when comparing the last day’s S&P 500 daily change with theDAX change between the current day’s open and last day’s close. Similar correlation analysis can be used to predict opening prices of lagging (in the time zone)international indices. For example the Nikkei opening price can be predicted based
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Intermarket Trading Strategies
Table 6.1 Nonparametric correlation between the DAX and major world indices for the5-year period until 31 December 2006. The 5-year Spearman’s price correlation coefficient isshown in column 3 and the correlation of daily and weekly yields (% changes) in column 4.All US indices were shifted one day back since the current day’s closing price is not known atthe time of the DAX close.Yield %Index/CommoditySP 500 (lag 1 day)DJ-30 (lag 1 day)NDX (lag 1 day)TSX (lag 1 day)Euro Stoxx 50Stoxx 50TecdaxCAC 40FTSEAthens GeneralUtility index (lag 1 day)NikkeiHang Seng10 Yr tr. yld (lag 1 day)BundGoldOil (Brent)EuroDollar index (lag 1 day)Yen
Daily prices
Daily
Weekly
0.9130.8840.8470.8810.9920.9710.8330.9950.9890.9800.9120.9000.8520.7150.3720.6600.0400.404−0.250.030
0.1340.1250.1670.0970.8780.8640.7140.8620.7240.3850.0850.2310.2450.062−0.338−0.15−0.039−0.210.0800.082
0.6610.6520.6140.5050.9280.8870.7640.9040.7670.5510.2420.5350.5080.375−0.377−0.113−0.055−0.2130.1700.074
on the last day’s closing price of the S&P 500, or a European index opening can bepredicted based on both the last day’s closing price of the S&P and the current day’sclosing of Asian exchanges using a multiple regression model.Notice that correlations between DAX and all other non equity index assets (belowthe Hang Seng in Table 6.1) broke down and even reversed sign when taking daily orweekly yields. This is because their relationship with the DAX was non linear thusinvalidating the correlation analysis.6.2
CORRELATION WITH STOCKS
In Table 6.3 you can see 15 US stocks (including foreign ADR) with the strongestcorrelation with the DAX over a 5-year period. An analysis by industry reveals thatthe DAX correlated best with interest rate sensitive groups like Utilities, Financialsand Building Material stocks.
European Indices
87
Table 6.2 Nonparametric correlation between the DAX and major Europeanindices for the preceding 5 and 10 year period as of 31 May 2007.Index
5 Year
10 Year
Euro Stoxx 50Stoxx 50CAC 40Euronext 100FTSESSMI (Swiss)MIB-30 (Italian)BEL-20 (Belgian)Athens GeneralIrish Stock Index
0.9920.9710.9950.9960.990.990.9870.9860.980.987
0.9660.9440.94N/A0.9250.936N/A0.7340.823N/A
Table 6.3 Nonparametric correlation between the DAX and the 15 best correlated USstocks (including ADRs) during the 5-year period from 1 June 2003 to 1 June 2007. The5-year Spearman’s correlation coefficient is shown in column 3 and the stock’s industrygroup in column 4. As was expected, the DAX has the strongest correlation with EuropeanADRs like Holland’s Akzo and the Greek Telco OTE.Company name
Symbol
Correlation
Akzo Nobel NVHellenic TelecommING Group NVOrient ExpressMexico FundChubb Corp.General DynamicsCleco Corp.DPLManitowocBe AerospaceIconix BrandLaboratory Corp.NucorCemex
AKZOYOTEINGOEHMXFCBGDCNLDPLMTWBEAVICONLHNUECX
0.9850.9790.9760.9760.9760.9750.9750.9750.9750.9730.9730.9720.9720.9720.971
6.3
IndustryChemicalsTelecommFinancialLodgingCountry fundFinancialDefenseUtilitiesUtilitiesFarm MachineryDefenseApparelDrugsSteelBuilding materials
EUROPEAN FUTURES
European futures trade long after the closing of equity trading in the correspondingexchanges so a more realistic analysis (for trading purposes) would be to correlateEuropean index futures with the corresponding US futures. A comparison of the daily
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Intermarket Trading Strategies
Table 6.4 Nonparametric correlation matrix between international futures for the 5-yearperiod from 1 January 2002 to 31 December 2006. The FDAX is the DAX contract trading inthe Eurex; the ES, YM, NQ and NKD are the S&P 500 e-mini, Dow, Nasdaq 100 and Nikkeicontracts trading in the CME Globex; the ESTX is the Euro Stoxx 50 contract trading in theEurex; the 6E is the Euro contract trading also in the Globex; the GC is the gold contracttrading in the Comex; and the ZN is the 10-year Treasury note futures contract trading in theGlobex.Symbol FDAX ESYMNQNKDFCEFESX6Eindex DAX SP 500 DJ-30 Nasdaq Nikkei CAC 40 Eurostx EuroFDAXESER2YMNQNKDFCEFESXEUROGCZN
10.91 0.890.9110.980.850.98 0.960.890.98 10.850.96 0.920.900.92 0.920.990.93 0.900.990.88 0.860.410.68 0.710.660.86 0.85−0.81 −0.61 −0.60
0.850.900.960.920.930.920.920.9210.850.8510.860.910.820.860.630.550.810.80−0.54 −0.69
GCZNGold 10y TN
0.990.99 0.41 0.660.930.88 0.68 0.860.860.80 0.75 0.930.900.86 0.71 0.850.860.82 0.63 0.810.9120.86 0.55 0.8010.99 0.44 0.690.9910.36 0.590.440.36 10.820.690.59 0.82 1−0.80 −0.82 −0.09 −0.33
−0.81−0.61−0.53−0.60−0.54−0.69−0.80−0.82−0.09−0.331
yields in Tables 6.1 and 6.5 reveals a significant improvement of the correlations ofdaily changes which can only be attributed to the time lag factor between indices.This was most pronounced in the case of the DAX, whose correlation with theS&P improved dramatically from 0.13 (cash index) to 0.55 (futures). There was noTable 6.5 Nonparametric correlation matrix of daily % changes between internationalindex, currency and bond futures for the 5-year period from 1 January 2002 to 31 December2006. Significant correlations are depicted in bold. The ZN is the 10-year Treasury notefutures trading in the Globex.Symbolindex
FDAXDAX
ESSP 500
YMDJ-30
NQNasdaq
FCECAC 40
FESXEurostx
6EEuro
ZNTN
FDAXESYMNQFCEFESXEUROZN
10.550.520.490.810.89−0.22−0.29
0.5510.820.860.430.52−0.12−0.19
0.520.8210.670.410.50−0.09−0.16
0.490.860.6710.370.45−0.14−0.21
0.810.430.410.3710.84−0.19−0.23
0.890.520.500.450.841−0.22−0.26
−0.2−0.1−0.1−0.1−0.2−0.210.33
−0.29−0.19−0.16−0.21−0.23−0.260.331
European Indices
89
significant difference, however, when comparing the direct price correlation betweenfuture and cash prices in Tables 6.1 and 6.4 as US cash indices were shifted back oneday in time.6.4
TIME FACTOR
It is obvious from Table 6.1 that the correlation of weekly percentage yields isstronger than the corresponding daily one. But why stop there? What about highertime increments? I have calculated in Table 6.6 Pearson’s correlation of percentageTable 6.6 Correlation variation between DAX and S&P futures(e-mini) for different time segment percentage yields for the 5-yearperiod from 1 July 2002 to 30 June 2007. In the 3rd column is thecorrelation % change (improvement) from the previous time increment.Days
Pearson’s correlation
% change
123456789101112131415161718192025303540455075100
0.6170.7190.7630.7870.8030.8100.8100.8170.8160.8240.8250.8220.8250.8210.8220.8240.8230.8240.8230.8250.8310.8470.8530.8630.8720.8760.8900.893
16.46.23.12.10.80.00.8−0.11.10.1−0.40.3−0.40.10.20.00.10.00.20.81.90.71.30.90.51.60.3
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Intermarket Trading Strategies
Figure 6.1 Graph of correlation variation for time intervals ranging from 1 to 100 days ofpercent yields between DAX and S&P futures (e-mini) for the 5-year period from 1 July 2002to 30 June 2007.
yields for time intervals ranging from 1 to 100 days between DAX and S&Pe-mini futures (ES). They are also plotted graphically in Fig. 6.1. A visual inspection of the graph reveals that the correlation improved exponentially for thefirst 5-day period, made a short-term peak at the 10-day increment and actually decreased slightly up to the 15-day time increment. It then started sloping up slightlyto level off at 75 days. It is worthwhile noting that for longer than 10-day percentage differences, the correlation coefficient improved by only 8 %. Thereforea time increment of five to ten days, when taking percentage differences, mightbe the optimum to use when designing short to medium term intermarket tradingsystems.
6.5
INTRADAY
The correlation of intraday percentage changes follows a similar pattern to the dailyyields above.In Table 6.7 I list Pearson’s correlation for up to 1500 minutes (25 hours) ofpercentage yields between the DAX, Euro Stoxx 50 and S&P e-mini futures (ES).This was also plotted graphically in Fig. 6.2. The correlation between the DAX and
European Indices
91
Table 6.7 Intraday correlation of 1 to 1500 minute (25 hour) percent changesbetween the DAX , Euro Stoxx and S&P 500 futures for the 3-month periodfrom 1 January 2007 to 31 March 2007.Minutes
Hours
ESTX
ES
5101520253035404550556065707580859095100125150200250375500750100012501500
0.080.170.250.330.420.500.580.670.750.830.9211.081.171.251.331.421.501.581.672.082.503.334.176.258.3312.5016.6720.8325
0.8770.8880.8910.8930.8950.8970.8990.9000.9010.9020.9040.9050.9060.9070.9080.9080.9080.9090.9090.9080.9100.9100.9130.9150.9160.9180.9250.9280.9280.933
0.7240.7410.7540.7630.7680.7740.7800.7820.7870.7890.7930.7970.8020.8070.8100.8120.8130.8140.8150.8160.8230.8300.8360.8420.8500.8620.8840.8970.9000.910
Euro Stoxx improved exponentially for the first 10-minute period, made a short-termpeak at the 75-minute increment, making only marginal improvements for the next200 minutes.The correlation between the DAX and S&P e-mini futures (ES), on the other hand,made a short-term peak at the 75-minute (11/4 hour) increment but continued risingat a more moderate rate.
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Intermarket Trading Strategies
Figure 6.2 Graph of correlation between percent changes of DAX, Euro Stoxx (solid line)and e-mini S&P 500 futures (dashed line) for intraday time segments ranging from 5 to 1500minutes (25 trading hours) for the 3-month period from 1 January 2007 to 31 March 2007. Thecorrelation with Euro Stoxx futures flattens out after the first 75 minutes.
It is worthwhile noting that for longer than 10-minute percentage differences,the correlation coefficient between the DAX and Euro Stoxx futures improved byonly 5 % but its correlation with the S&P e-mini improved substantially (23 %). Itis therefore better to use longer time spans when designing short- to medium-termintermarket trading systems to trade the DAX based on its correlation with the S&Pe-mini.
7Gold
Good judgment is usually the result of experience and experience frequently isthe result of bad judgment.–Robert Lovell
In 1925, in a speech at the University of Cambridge, the English economist JohnMaynard Keynes called the Gold Standard “a barbaric relic” but even though theGold Standard has since been abolished the “barbaric relic” has not lost its value.Federal Reserve Board chairman Alan Greenspan once wrote about gold: “This isthe shabby secret of the welfare statistics’ tirades against gold. Deficit spendingis simply a scheme for the confiscation of