192 ACTA WASAENSIA185 fcash = fcash+dnc.*price; %(1*2) aggr. fundament. cash186187188 end %end inner loop over micro time steps189190 % Update history of state variables at integer time steps191192 phist(t,:)=p; %(T*2) history of logarithmic trading prices193 Phist(t,:)=price; %(T*2) history of ordinary trading prices194 nchist(t,:)=nc; %(T*2) history of chartist populations195 nfhist(t,:)=nf; %(T*2) history of fundamentalist populations196197198 % Record history of cash199200 cchist(t,:) = ccash; %(scalar) chartists aggregate cash201 cfhist(t,:) = fcash; %(scalar) fundamentalists aggr. cash202203204 end %end outer loop over integer time steps205206207 % Calculation of Return Series208209 %Individual logreturns210 rhist = phist-[pf;phist(1:T-1,:)]; %(T*2) individual logreturns211 corr = corrcoef(rhist); %(2*2) cross-correlation matrix of returns212213 %Equal weighted index returns214 ret = exp(rhist); %(T*2) individual gross returns215 ewret = mean(ret,2)-1; %(T*1) equally weighted index net-returns216217 %Capitalization weighted index returns218 price = exp([pf; phist(1:T-1,:)]); %(T*2) lagged price history219 weight = price./repmat(sum(price,2),1,2); %(T*2) cap. weigths220 cwret = sum(weight.*ret,2)-1; %(T*1) cap-weighted index net-returns221222 %Index logreturns223 ewlret = log(1+ewret); %(T*1) logreturns of equally weigthed index224 cwlret = log(1+cwret); %(T*1) logreturns of cap-weighted index225 avlret = mean(rhist,2); %(T*1) average logreturns226227228 % Calculate history of traders holdings normalized at tc=1229230 hchist = nchist; %(T*2) chartist aggr. holdings history for tc=1231 hfhist = nfhist.*(repmat(pf,T,1)-phist)/l; %(T*2) fund. holdings
ACTA WASAENSIA 193232233234 % Calculate history of traders wealth235236 cwealth = sum(hchist.*Phist+cchist,2); %(T*1) chartists wealth237 fwealth = sum(hfhist.*Phist+cfhist,2); %(T*1) fundament. wealth238239240 % Create graphical output241242 %Output 1: Trader Populations and <strong>Trading</strong> Prices243 figure;244 subplot(2,1,1),plot([nchist,nfhist]),title(’Population Values’);245 subplot(2,1,2),plot(phist),title(’<strong>Trading</strong> Prices’);246247 %Output 2: Logreturns for asset 1248 figure;249 subplot(2,1,1),plot(rhist(:,1)),title({[’Logreturns <strong>Asset</strong> 1’];...250 [’(Correlation with <strong>Asset</strong> 2 = ’,num2str(corr(1,2)),’)’]});251 subplot(2,1,2),252 plot(100*[hchist(:,1)./sum([hchist(:,1),hfhist(:,1)],2),...253 hfhist(:,1)./sum([hchist(:,1),hfhist(:,1)],2)]),254 title(’<strong>Asset</strong> 1 holdings in % (Chartists blue, Fundamentalists green)’);255256 %Output 3: Logreturns for asset 2257 figure;258 subplot(2,1,1),plot(rhist(:,2)),title({[’Logreturns <strong>Asset</strong> 2’];...259 [’(Correlation with <strong>Asset</strong> 1 = ’,num2str(corr(1,2)),’)’]});260 subplot(2,1,2),261 plot(100*[hchist(:,2)./sum([hchist(:,2),hfhist(:,2)],2),...262 hfhist(:,2)./sum([hchist(:,2),hfhist(:,2)],2)]),263 title(’<strong>Asset</strong> 2 holdings in % (Chartists blue, Fundamentalists green)’);264265 %Output 4: Equal and capitalization weighted index logreturns266 figure;267 subplot(2,1,1),plot(ewlret),title(’Equal Weighted Index Logreturns’);268 subplot(2,1,2),plot(cwlret),269 title(’Capitalization Weighted Index Logreturns’);270271 %Output 5: Average Logreturn and holdings of the two stocks272 figure;273 subplot(2,1,1),274 plot(avlret),title(’Average Logreturn’),275 subplot(2,1,2),276 plot(100*[sum(hchist,2)./sum([hchist,hfhist],2),...277 sum(hfhist,2)./sum([hchist,hfhist],2)]),278 title(’Traders holdings in % (Chartists blue, Fundamentalists green)’);
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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
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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
- Page 184 and 185: 184 ACTA WASAENSIA888990 % create P
- 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 194 and 195: 194 ACTA WASAENSIA279280281 %Output
- Page 196 and 197: 196 ACTA WASAENSIA(4.20), and o(τ
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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