BERND PAPE Asset Allocation, Multivariate Position Based Trading ...

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94 ACTA WASAENSIA0.2Logreturns Parameter Set I0.10−0.1−0.20 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Logreturns Parameter Set II0.150.10.050−0.05−0.10 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Logreturns Parameter Set III0.060.040.020−0.02−0.04−0.060 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Logreturns Parameter Set IV0.20.10−0.1−0.20 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Figure 2. Logreturns over 20,000 integer time steps (Simulation II).
ACTA WASAENSIA 950.8Chartist Index Parameter Set I0.60.40.200 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Chartist Index Parameter Set II0.80.60.40.200 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Chartist Index Parameter Set III0.80.60.40.200 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Chartist Index Parameter Set IV0.80.60.40.200 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Figure 3. Chartist index z over 20,000 integer time steps (Simulation I). The horizontallines indicate the threshold z max ,beyondwhichthefirst type equilibriumof (4.51) is expected to loose its stability.
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ACTA WASAENSIA 52.10.1 Cross-Sectio
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ACTA WASAENSIA 7A5 Matlabcodeforerr
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10 ACTA WASAENSIAIn chapter 5 I sha
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12 ACTA WASAENSIA2 Statistical Prop
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14 ACTA WASAENSIA2.2 Absence of Ser
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ACTA WASAENSIA 17The survival or ta
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ACTA WASAENSIA 19& Scheinkman (1987
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ACTA WASAENSIA 212.6 Long Range Dep
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ACTA WASAENSIA 23A particular class
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26 ACTA WASAENSIAMultiscaling may t
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28 ACTA WASAENSIAHansen (1982) to c
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30 ACTA WASAENSIAThe leverage hypot
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32 ACTA WASAENSIAIn order to aviod
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34 ACTA WASAENSIAon a risk-adjusted
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36 ACTA WASAENSIAplanations for the
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38 ACTA WASAENSIAspeculative prices
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40 ACTA WASAENSIAindex α is howeve
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42 ACTA WASAENSIAThis approach has
Page 44 and 45: 44 ACTA WASAENSIAimplying exponenti
Page 46 and 47: 46 ACTA WASAENSIA3.2.4 Descriptive
Page 48 and 49: 48 ACTA WASAENSIAdivisible version
Page 50 and 51: 50 ACTA WASAENSIAto check their ade
Page 52 and 53: 52 ACTA WASAENSIAvolatility into th
Page 54 and 55: 54 ACTA WASAENSIAfeedback of the co
Page 56 and 57: 56 ACTA WASAENSIAarriving at the fo
Page 58 and 59: 58 ACTA WASAENSIAat iteration k com
Page 60 and 61: 60 ACTA WASAENSIAmultipliers in the
Page 62 and 63: 62 ACTA WASAENSIALux & Ausloos (200
Page 64 and 65: 64 ACTA WASAENSIAmarkets dominated
Page 66 and 67: 66 ACTA WASAENSIAThe “representat
Page 68 and 69: 68 ACTA WASAENSIA(1983) 114 motivat
Page 70 and 71: 70 ACTA WASAENSIAIn the following w
Page 72 and 73: 72 ACTA WASAENSIASethi (1996) exten
Page 74 and 75: 74 ACTA WASAENSIASummation over all
Page 76 and 77: 76 ACTA WASAENSIAthe microscopic un
Page 78 and 79: 78 ACTA WASAENSIA& Winker (2003) an
Page 80 and 81: 80 ACTA WASAENSIAWe wish to obtain
Page 82 and 83: 82 ACTA WASAENSIAindividual markets
Page 84 and 85: 84 ACTA WASAENSIAreturn series. I l
Page 86 and 87: 86 ACTA WASAENSIASwitches between c
Page 88 and 89: 88 ACTA WASAENSIAfollowing necessar
Page 90 and 91: 90 ACTA WASAENSIATable 1. Parameter
Page 92 and 93: 92 ACTA WASAENSIAin stepwise search
Page 96 and 97: 96 ACTA WASAENSIA0.8Chartist Index
Page 98 and 99: 98 ACTA WASAENSIATable 3. Kurtosis
Page 100 and 101: 100 ACTA WASAENSIAthe most extreme
Page 102 and 103: 102 ACTA WASAENSIATable 4. Estimate
Page 104 and 105: 104 ACTA WASAENSIATable 8. Results
Page 106 and 107: 106 ACTA WASAENSIATable 9. Results
Page 108 and 109: 108 ACTA WASAENSIATable 11. Paramet
Page 110 and 111: 110 ACTA WASAENSIA1 x 105 Aggr. Inv
Page 112 and 113: 112 ACTA WASAENSIA200Aggr. Inventor
Page 114 and 115: 114 ACTA WASAENSIA5 x 105 Aggr. Wea
Page 116 and 117: 116 ACTA WASAENSIAOverall, we can a
Page 118 and 119: 118 ACTA WASAENSIAby Lux (1998). Th
Page 120 and 121: 120 ACTA WASAENSIAwhere s is a disc
Page 122 and 123: 122 ACTA WASAENSIAprocessing valuat
Page 124 and 125: 124 ACTA WASAENSIAProof. See append
Page 126 and 127: 126 ACTA WASAENSIA2.5Logarithmice T
Page 128 and 129: 128 ACTA WASAENSIA0.4Logreturns Ass
Page 130 and 131: 130 ACTA WASAENSIA0.4Logreturns Ass
Page 132 and 133: 132 ACTA WASAENSIATable 17. Probabi
Page 134 and 135: 134 ACTA WASAENSIATable 18. Median
Page 136 and 137: 136 ACTA WASAENSIAIn order to test
Page 138 and 139: 138 ACTA WASAENSIAReferencesAbhyank
Page 140 and 141: 140 ACTA WASAENSIABaillie, R. T. (1
Page 142 and 143: 142 ACTA WASAENSIABlack, F. & M. Sc
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144 ACTA WASAENSIAButler, R. J., J.
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146 ACTA WASAENSIACont, R. (2001).
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148 ACTA WASAENSIAEmbrechts, P., C.
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150 ACTA WASAENSIAFielitz, B. D. (1
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152 ACTA WASAENSIAGhysels, E., A. C
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154 ACTA WASAENSIAHeston, S. L. (19
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156 ACTA WASAENSIAKaldor, N. (1939)
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158 ACTA WASAENSIALiesenfeld, R. (1
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160 ACTA WASAENSIALye, J. N. & V. L
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162 ACTA WASAENSIAMikosch, T. (2003
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164 ACTA WASAENSIAPindyck, R. S. (1
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166 ACTA WASAENSIASethi, R. (1996).
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168 ACTA WASAENSIATesfatsion, L. &
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170 ACTA WASAENSIAAAppendixA1Matlab
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172 ACTA WASAENSIA87 % a3 = 1; %imp
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174 ACTA WASAENSIA181 nmout = max([
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176 ACTA WASAENSIA275276 figure;277
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178 ACTA WASAENSIA45 end464748 % In
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180 ACTA WASAENSIA454647 % Check wh
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182 ACTA WASAENSIAA4Matlab code for
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184 ACTA WASAENSIA888990 % create P
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186 ACTA WASAENSIA4344 nobs = 500;
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188 ACTA WASAENSIAA6Matlab code for
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190 ACTA WASAENSIA9192 for t = 1:T
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192 ACTA WASAENSIA185 fcash = fcash
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194 ACTA WASAENSIA279280281 %Output
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196 ACTA WASAENSIA(4.20), and o(τ
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198 ACTA WASAENSIAas t = n k P ˙
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200 ACTA WASAENSIA nBn˙f2 = v B n
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202 ACTA WASAENSIA f˙1−c1 = v B
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204 ACTA WASAENSIAlocal stability o
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