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Microeconomic Models for Long-Memory in the Volatility of Financial Time Series

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Author Info
Alan P. Kirman, Gilles Teyssiere

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Abstract

We show that a class of microeconomic behavioral models with interacting agents, introduced by Kirman (1991,1993), can replicate the empirical long-memory properties of the two first conditional moments of financial time series. The essence of these models is that the forecasts and thus the desired trades of individuals are influenced, directly or indirectly by those of the other participants. These "field effects" generate herding behaviour which affects the structure of the asset price dynamics. The series of squared returns and absolute returns generated by these models display long-memory, while the returns are uncorrelated. Furthermore, this class of modesl is also able to replicate the common long-memory properties in the volatility and co-volatility of financial time-series uncovered by Teyssiere (1997,1998). These properties are investigated by using various semiparametric and non-parametric tests and estimators.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 221.

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Date of creation: 01 Apr 2001
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Handle: RePEc:sce:scecf1:221

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Keywords: long-memory microeconomic models field effects

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Jonathan Dark, 2004. "Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures," Monash Econometrics and Business Statistics Working Papers 4/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Jonathan Dark, 2004. "Long memory in the volatility of the Australian All Ordinaries Index and the Share Price Index futures," Monash Econometrics and Business Statistics Working Papers 5/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  3. G. Teyssiere, . "Long-Memory Analysis," Sonderforschungsbereich 373 2000-57, Humboldt Universitaet Berlin.
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  5. Paul De Grauwe & Pablo Rovira Kaltwasser, 2006. "A Behavioral Finance Model of the Exchange Rate with Many Forecasting Rules," CESifo Working Paper Series CESifo Working Paper No. , CESifo GmbH. [Downloadable!]
  6. Paul De Grauwe & Pablo Rovira Kaltwasser, 2007. "Modeling Optimism and Pessimism in the Foreign Exchange Market," CESifo Working Paper Series CESifo Working Paper No. , CESifo GmbH. [Downloadable!]
  7. Lux, Thomas, 2008. "Stochastic Behavioral Asset Pricing Models and the Stylized Facts," Economics working papers 2008,08, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
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  9. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2005. "Time-Variation of Higher Moments in a Financial Market with Heterogeneous Agents: An Analytical Approach," Economics working papers 2005,14, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
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  10. Alfarano, Simone & Lux, Thomas, 2003. "A Minimal Noise Trader Model with Realistic Time Series Properties," Economics working papers 2003,15, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
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  11. Gaunersdorfer, A. & Hommes, C.H.,, 2005. "A nonlinear structural model for volatility clustering," CeNDEF Working Papers 05-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. [Downloadable!]
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