arXiv subjects

Giacomo Raffaelli

Publications and source records attributed to Giacomo Raffaelli.

4 recordsLinked to original sources

arXiv2006

Dynamic instability in a phenomenological model of correlated assets

We show that financial correlations exhibit a non-trivial dynamic behavior. We introduce a simple phenomenological model of a multi-asset financial market, which takes into account the impact of portfolio investment on price dynamics. This captures the fact that correlations determine the optimal portfolio but are affected by investment based on it. We show that such a feedback on correlations gives rise to an instability when the volume of investment exceeds a critical value. Close to the critical point the model exhibits dynamical correlations very similar to those observed in real markets. Maximum likelihood estimates of the model's parameter for empirical data indeed confirm this conclusion, thus suggesting that real markets operate close to a dynamically unstable point.

physics.soc-ph↗

arXiv2005

A statistical mechanics model for the emergence of consensus

The statistical properties of pairwise majority voting over S alternatives is analyzed in an infinite random population. We first compute the probability that the majority is transitive (i.e. that if it prefers A to B to C, then it prefers A to C) and then study the case of an interacting population. This is described by a constrained multi-component random field Ising model whose ferromagnetic phase describes the emergence of a strong transitive majority. We derive the phase diagram, which is characterized by a tri-critical point and show that, contrary to intuition, it may be more likely for an interacting population to reach consensus on a number S of alternatives when S increases. This effect is due to the constraint imposed by transitivity on voting behavior. Indeed if agents are allowed to express non transitive votes, the agents' interaction may decrease considerably the probability of a transitive majority.

cond-mat.stat-mech↗

arXiv2002

Short- and Long-Term Statistical Properties of Heartbeat Time-Series in Healthy and Pathological Subjects

We analize heartbeat time-series corresponding to several groups of individuals (healthy, heart transplanted, with congestive heart failure (CHF), after myocardial infarction (MI), hypertensive), looking for short- and long-time statistical behaviors. In particular we study the persistency patterns of interbeat times and interbeat-time variations. Long-range correlations are revealed using an information-based technique which makes a wise use of the available statistics. The presence of strong long-range time correlations seems to be a general feature for all subjects, with the exception of some CHF individuals. We also show that short time-properties detected in healthy subjects, and seen also in hypertensive and MI patients, and completely absent in the trasplanted, are characterized by a general behavior when we apply a proper coarse-graining procedure for time series analysis.

cond-mat.soft↗

arXiv2001

Asymmetric Anomalous Diffusion: an Efficient Way to Detect Memory in Time Series

We study time series concerning rare events. The occurrence of a rare event is depicted as a jump of constant intensity always occurring in the same direction, thereby generating an asymmetric diffusion process. We consider the case where the waiting time distribution is an inverse power law with index $μ$. We focus our attention on $μ<3$, and we evaluate the scaling $δ$ of the resulting diffusion process. We prove that $δ$ gets its maximum value, $δ=1$, corresponding to the ballistic motion, at $μ=2$. We study the resulting diffusion process by means of joint use of the continuous time random walk and of the generalized central limit theorem, as well as adopting numerical treatment. We show that rendering asymmetric the diffusion process yelds the significant benefit of enhancing the value of the scaling parameter $δ$. Furthermore, this scaling parameter becomes sensitive to the power index $μ$ in the whole region $1<μ<3$. Finally, we show our method in action on real data concerning human heartbeat sequences.

cond-mat.stat-mech↗