190 ACTA WASAENSIA9192 for t = 1:T %Start outer loop over integer time steps9394 for st = 1:steps %start inner loop over micro time steps9596 % Calculate traders utilities9798 c(1) = (nc(1)+nf(1)-nc(2)-nf(2))/N;99 c(2) = -c(1); %(1*2) chartist utilities100 f = s*abs(pf-p); %(1*2) fundamentalist utilities101102103 % Fill probability matrix for transitions within stocks104105 pmat(1,1) = c(2) - c(1); % c1 -> c2106 pmat(2,1) = f(2) - f(1); % f1 -> f2107 pmat(3,1) = f(1) - c(1); % c1 -> f1108 pmat(4,1) = f(2) - c(2); % c2 -> f2109 pmat(5,1) = f(2) - c(1); % c1 -> f2110 pmat(6,1) = f(1) - c(2); % c2 -> f1111 pmat(:,2) = -pmat(:,1); % reverse direction of col. 1112113 pmat = (v/steps)*exp(a*pmat); %(6*2) transition prob’s114115116 % Fill prob-matrix for transitions between stocks / bonds117118 ne = N - nb; %# equity investors119 pbe = exp(ab*(ne-nb)/N); %p_BE: from bond to equity120 pbmat(1:2,1) = nc’*(pbe/ne); % b -> c1; b -> c2121 pbmat(3:4,1) = nf’*(pbe/ne); % b -> f1; b -> f2122 pbmat(:,2) = repmat(1/pbe,4,1); % c1, c2, f1, f2 -> b123124 pbmat = (vb/steps)*pbmat; %(4*2) transition prob’s125126127 % (6*2) draws of traders leaving their strategy w’in stocks128129 dnmat(1,1) = fastbin(nc(1), pmat(1,1)); % c1 -> c2130 dnmat(1,2) = fastbin(nc(2), pmat(1,2)); % c2 -> c1131 dnmat(2,1) = fastbin(nf(1), pmat(2,1)); % f1 -> f2132 dnmat(2,2) = fastbin(nf(2), pmat(2,2)); % f2 -> f1133 dnmat(3,1) = fastbin(nc(1), pmat(3,1)); % c1 -> f1134 dnmat(3,2) = fastbin(nf(1), pmat(3,2)); % f1 -> c1135 dnmat(4,1) = fastbin(nc(2), pmat(4,1)); % c2 -> f2136 dnmat(4,2) = fastbin(nf(2), pmat(4,2)); % f2 -> c2137 dnmat(5,1) = fastbin(nc(1), pmat(5,1)); % c1 -> f2
ACTA WASAENSIA 191138 dnmat(5,2) = fastbin(nf(2), pmat(5,2)); % f2 -> c1139 dnmat(6,1) = fastbin(nc(2), pmat(6,1)); % c2 -> f1140 dnmat(6,2) = fastbin(nf(1), pmat(6,2)); % f1 -> c2141142143 % (4*2) draws of traders switching between stocks / bonds144145 dnbmat(1,1) = fastbin(nb, pbmat(1,1)); % b -> c1146 dnbmat(2,1) = fastbin(nb, pbmat(2,1)); % b -> c2147 dnbmat(3,1) = fastbin(nb, pbmat(3,1)); % b -> f1148 dnbmat(4,1) = fastbin(nb, pbmat(4,1)); % b -> f2149150 dnbmat(1,2) = fastbin(nc(1), pbmat(1,2)); %c1 -> b151 dnbmat(2,2) = fastbin(nc(2), pbmat(2,2)); %c2 -> b152 dnbmat(3,2) = fastbin(nf(1), pbmat(3,2)); %f1 -> b153 dnbmat(4,2) = fastbin(nf(2), pbmat(4,2)); %f2 -> b154155156 % Calculation of stock population increments157158 dnc(1)= dnmat(1,2) + dnmat(3,2) + dnmat(5,2) + dnbmat(1,1) - ...159 dnmat(1,1) - dnmat(3,1) - dnmat(5,1) - dnbmat(1,2);160 dnc(2)= dnmat(1,1) + dnmat(4,2) + dnmat(6,2) + dnbmat(2,1) - ...161 dnmat(1,2) - dnmat(4,1) - dnmat(6,1) - dnbmat(2,2);162 dnf(1)= dnmat(2,2) + dnmat(3,1) + dnmat(6,1) + dnbmat(3,1) - ...163 dnmat(2,1) - dnmat(3,2) - dnmat(6,2) - dnbmat(3,2);164 dnf(2)= dnmat(2,1) + dnmat(4,1) + dnmat(5,1) + dnbmat(4,1) - ...165 dnmat(2,2) - dnmat(4,2) - dnmat(5,2) - dnbmat(4,2);166167168 % Update of trader populations169170 nc = nc + dnc; %(1*2) updated chartist populations171 nf = nf + dnf; %(1*2) updated fundamentalist populations172 nb = N - sum([nc nf]); %(scalar) upd. bond population173174175 % Update of (1*2) trading prices176177 dp = (l*dnc + (pf-p).*dnf)./nf; %(1*2) price increments178 p = p + dp; %(1*2) updated trading prices179180181 % Update of traders cash182183 price = exp(p); %(1*2) ordinary trading price184 ccash = ccash-dnc.*price; %(1*2) aggr. chartists cash
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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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134 ACTA WASAENSIATable 18. Median
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136 ACTA WASAENSIAIn order to test
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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
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- Page 176 and 177: 176 ACTA WASAENSIA275276 figure;277
- Page 178 and 179: 178 ACTA WASAENSIA45 end464748 % In
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- Page 182 and 183: 182 ACTA WASAENSIAA4Matlab code for
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- Page 188 and 189: 188 ACTA WASAENSIAA6Matlab code for
- 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 ˙
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- Page 202 and 203: 202 ACTA WASAENSIA f˙1−c1 = v B
- Page 204 and 205: 204 ACTA WASAENSIAlocal stability o