Microeconomic models for long memory in the volatility of financial time series

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 the individuals in the markets 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 models is able to replicate the common long-memory properties in the volatility and co-volatility of financial time series, uncovered by Teyssière (1997, 1998). These properties are investigated by using various model independent tests and estimators, i.e., semiparametric and nonparametric, introduced by Lo (1991), Kwiatkowski, Phillips, Schmidt and Shin (1992), Robinson (1995), Lobato adn Robinson (1998), Giraitis, Kokoszka and Leipus (1999), Giraitis, Kokoszka, Leipus and Teyssière (1999). The relative performance of these tests and estimators for long-memory in an non-standard data generating process is then assessed.(This abstract was borrowed from another version of this item.)