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ACTA WASAENSIA 159Loretan, M. & W. B. English (2000). Evaluating “correlation breakdowns” duringperiods of market volatility. International Finance Discussion Paper 658, Board ofGovernors of the Federal Reserve System, Washington DC.Loretan, M. & P. C. Phillips (1994). Testing the covariance stationarity of heavy-tailedtime series. Journal of Empirical Finance 1, 211—248.Lucas, R. E. (1986). Adaptive behavior and economic theory. Journal of Business59:4, 401—426.Lui, Y.-H. & D. Mole (1998). The use of fundamental and technical analysis by foreignexchange dealers: Hong Kong evidence. Journal of International Money and Finance17:3, 535—545.Lux, T. (1995). Herd behaviour, bubbles and crashes. Economic Journal 105:431,881—896.Lux, T. (1996a). Long-term stochastic dependence in financial prices: Evidence fromthe German stock market. Applied Economics Letters 3, 701—706.Lux, T. (1996b). The stable Paretian hypothesis and the frequency of large returns:an examination of major German stocks. Applied Financial Economics 6, 463—475.Lux, T. (1998). The socio-economic dynamics of speculative markets: interactingagents, chaos, and the fat tails of return distributions. Journal of Economic Behaviorand Organization 33, 143—165.Lux, T. (2001). Power laws and long memory. Quantitative Finance 1:6, 560—562.Lux, T. & M. Ausloos (2002). Market fluctuations I: Scaling, multiscaling, and theirpossible origins. In Bunde, A., J. Kropp & H. J. Schellnhuber, editors, The Scienceof Disasters, pages 373—409, Berlin. Springer.Lux, T. & M. Marchesi (1999). Scaling and criticality in a stochastic multi-agent modelof a financial market. Nature 397, 498—500.Lux, T. & M. Marchesi (2000). Volatility clustering in financial markets: A microsimulationof interacting agents. International Journal of Theoretical and Applied Finance3:4, 675—702.
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10 ACTA WASAENSIAIn chapter 5 I sha
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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 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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- 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 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 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 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