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

‘J’-shaped returns to timing advantage in access to information – Experimental evidence and a tentative explanation

“…Given the vast literature on heterogeneous agent models (e.g. Brock and Hommes, 1998;Hommes, 2002;Kirman and Teyssie`re, 2002;Lux, 1998;Lux andMarchesi, 1999, 2000) consistently demonstrating that the actions of chartists in markets destabilize prices, we can conclude that providing participants with charts does not make them chartists. Rather, prices became more stable, probably because past prices became more prominent and acted as a 'visual anchor', while most participants probably did not take the time to look at past prices in the list in T1.…”

Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach

“…Similar investigations of the dynamic properties of alternative models revealed that many agent-based approaches share a certain tendency of generating fat tails and volatility clustering although their quantitative manifestations are not always identical to the very robust numbers obtained with empirical data (cf. LeBaron et al, 1999;Chen and Yeh, 2002;Kirman and Teyssie`re, 2002;Lux and Schornstein, 2005).…”

Market clearing by maximum entropy in agent models of stock markets

“…Δt m must occur on a very short scale, otherwise quotation for the DAX every 30 s does not make any sense. A Walrasian market used in Kirman type models (Alfarano et al 2005;Kirman 1993;Kirman and Teyssière 2002) or bounded heterogeneous agent models (Chiarella et al 2009;LeBaron 2006;Hommes 2006) corresponds to Δt m = 0. The second process must ensure that the return at observation time Δt o is given by the product of an iid Gaussian noise (Wagner et al 2010) and a volatility factor changing on a much longer scale Δt v as seen from the autocorrelation.…”