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

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184 ACTA WASAENSIA888990 % create PCTILE_C output structure (correct table: includes constant)9192 pctile_c.lt025 = sum(ltc025); %P(RHO) < 0.02593 pctile_c.lt05 = sum(ltc05-ltc025); %0.025
ACTA WASAENSIA 185A5Matlab code for error detection in unit root tests1 function [rho,taustat,pctile_nc,pctile_c,tests] = df1976(y)2 % DF1976 Dickey Fuller test without constant or trend3 %---------------------------------------------------------------4 % USAGE: [RHO,TAUSTAT,PCTILE_NC,PCTILE_C,TESTS] = DF1976(Y)5 % performs TESTS=LENGTH(Y)/500 Dickey Fuller tests without6 % a constant upon 500 observations of the time series Y7 % using the critical values from Fuller (1976).8 % The length of Y must be an integer multiple of 500.9 %---------------------------------------------------------------10 % OUTPUT:11 % RHO is a ((LENGTH(Y)/500)*1) vector of estimated values12 % for rho in the regression y_t = rho*y_{t-1} + e_t.13 % TAUSTAT is a ((LENGTH(Y)/500)*1) vector of DF-statistics.14 % PCTILE_NC is a structure containing the number of tests15 % resulting in the following percentile of the asymptotic16 % distribution of RHO under the null hypothesis rho = 117 % using the correct table without constant in Fuller (1976):18 % PCTILE_NC.LT025: P(RHO) < 0.02519 % PCTILE_NC.LT05: 0.025
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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
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44 ACTA WASAENSIAimplying exponenti
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46 ACTA WASAENSIA3.2.4 Descriptive
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48 ACTA WASAENSIAdivisible version
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50 ACTA WASAENSIAto check their ade
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52 ACTA WASAENSIAvolatility into th
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54 ACTA WASAENSIAfeedback of the co
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56 ACTA WASAENSIAarriving at the fo
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58 ACTA WASAENSIAat iteration k com
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60 ACTA WASAENSIAmultipliers in the
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62 ACTA WASAENSIALux & Ausloos (200
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64 ACTA WASAENSIAmarkets dominated
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66 ACTA WASAENSIAThe “representat
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68 ACTA WASAENSIA(1983) 114 motivat
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70 ACTA WASAENSIAIn the following w
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72 ACTA WASAENSIASethi (1996) exten
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74 ACTA WASAENSIASummation over all
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76 ACTA WASAENSIAthe microscopic un
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78 ACTA WASAENSIA& Winker (2003) an
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80 ACTA WASAENSIAWe wish to obtain
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82 ACTA WASAENSIAindividual markets
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84 ACTA WASAENSIAreturn series. I l
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86 ACTA WASAENSIASwitches between c
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88 ACTA WASAENSIAfollowing necessar
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90 ACTA WASAENSIATable 1. Parameter
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92 ACTA WASAENSIAin stepwise search
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94 ACTA WASAENSIA0.2Logreturns Para
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96 ACTA WASAENSIA0.8Chartist Index
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98 ACTA WASAENSIATable 3. Kurtosis
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100 ACTA WASAENSIAthe most extreme
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102 ACTA WASAENSIATable 4. Estimate
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104 ACTA WASAENSIATable 8. Results
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106 ACTA WASAENSIATable 9. Results
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108 ACTA WASAENSIATable 11. Paramet
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110 ACTA WASAENSIA1 x 105 Aggr. Inv
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112 ACTA WASAENSIA200Aggr. Inventor
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114 ACTA WASAENSIA5 x 105 Aggr. Wea
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116 ACTA WASAENSIAOverall, we can a
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118 ACTA WASAENSIAby Lux (1998). Th
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120 ACTA WASAENSIAwhere s is a disc
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122 ACTA WASAENSIAprocessing valuat
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124 ACTA WASAENSIAProof. See append
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126 ACTA WASAENSIA2.5Logarithmice T
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128 ACTA WASAENSIA0.4Logreturns Ass
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130 ACTA WASAENSIA0.4Logreturns Ass
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132 ACTA WASAENSIATable 17. Probabi
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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
Page 144 and 145: 144 ACTA WASAENSIAButler, R. J., J.
Page 146 and 147: 146 ACTA WASAENSIACont, R. (2001).
Page 148 and 149: 148 ACTA WASAENSIAEmbrechts, P., C.
Page 150 and 151: 150 ACTA WASAENSIAFielitz, B. D. (1
Page 152 and 153: 152 ACTA WASAENSIAGhysels, E., A. C
Page 154 and 155: 154 ACTA WASAENSIAHeston, S. L. (19
Page 156 and 157: 156 ACTA WASAENSIAKaldor, N. (1939)
Page 158 and 159: 158 ACTA WASAENSIALiesenfeld, R. (1
Page 160 and 161: 160 ACTA WASAENSIALye, J. N. & V. L
Page 162 and 163: 162 ACTA WASAENSIAMikosch, T. (2003
Page 164 and 165: 164 ACTA WASAENSIAPindyck, R. S. (1
Page 166 and 167: 166 ACTA WASAENSIASethi, R. (1996).
Page 168 and 169: 168 ACTA WASAENSIATesfatsion, L. &
Page 170 and 171: 170 ACTA WASAENSIAAAppendixA1Matlab
Page 172 and 173: 172 ACTA WASAENSIA87 % a3 = 1; %imp
Page 174 and 175: 174 ACTA WASAENSIA181 nmout = max([
Page 176 and 177: 176 ACTA WASAENSIA275276 figure;277
Page 178 and 179: 178 ACTA WASAENSIA45 end464748 % In
Page 180 and 181: 180 ACTA WASAENSIA454647 % Check wh
Page 182 and 183: 182 ACTA WASAENSIAA4Matlab code for
Page 186 and 187: 186 ACTA WASAENSIA4344 nobs = 500;
Page 188 and 189: 188 ACTA WASAENSIAA6Matlab code for
Page 190 and 191: 190 ACTA WASAENSIA9192 for t = 1:T
Page 192 and 193: 192 ACTA WASAENSIA185 fcash = fcash
Page 194 and 195: 194 ACTA WASAENSIA279280281 %Output
Page 196 and 197: 196 ACTA WASAENSIA(4.20), and o(τ
Page 198 and 199: 198 ACTA WASAENSIAas t = n k P ˙
Page 200 and 201: 200 ACTA WASAENSIA nBn˙f2 = v B n
Page 202 and 203: 202 ACTA WASAENSIA f˙1−c1 = v B
Page 204 and 205: 204 ACTA WASAENSIAlocal stability o
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