Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1312.4803

Multiscaling edge effects in an agent-based money emergence model

Paweł Oświęcimka·Stanisław Drożdż·Robert Gębarowski·Andrzej Z. Górski·Jarosław Kwapień
View original source ↗Read source document ↗Export metadata ↓

Abstract

An agent-based computational economical toy model for the emergence of money from the initial barter trading, inspired by Menger's postulate that money can spontaneously emerge in a commodity exchange economy, is extensively studied. The model considered, while manageable, is significantly complex, however. It is already able to reveal phenomena that can be interpreted as emergence and collapse of money as well as the related competition effects. In particular, it is shown that - as an extra emerging effect - the money lifetimes near the critical threshold value develop multiscaling, which allow one to set parallels to critical phenomena and, thus, to the real financial markets.

Explore related subjects

q-fin.STphysics.soc-phq-fin.TR

Keep this discovery

BibTeXRIS

Paweł Oświęcimka, Stanisław Drożdż, Robert Gębarowski, Andrzej Z. Górski, Jarosław Kwapień. 2015-12-03. Multiscaling edge effects in an agent-based money emergence model. https://doi.org/10.5506/aphyspolb.46.1579

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Log S-fBM model: Statistical analysis

The Log S-fBM model, introduced by Wu et al., is a stochastic volatility model whose log volatility is a stationary fractional Brownian motion (S-fBM): a stationary Gaussian process with power-decaying autocovariance driven by the Hurst exponent $H$, and variance scaled by an intermittency coefficient. A key property is that it reconciles rough volatility, where $H$ is typically near $0.1$ (see Gatheral et al.), with multifractal volatility, where $H$ is close to $0$ as in Bacry, Muzy et al.: the model's volatility measure converges to a multifractal random measure as $H\to0$. Numerical findings in Wu et al. show intermittency of order $0.02$ across financial assets, motivating a small intermittency approximation of log volatility moments for calibration via the general method of moments (GMM). In this work, we conduct a statistical analysis of the Log S-fBM model. We derive scaling properties of the S-fBM process and the Log S-fBM integrated volatility measure, present deviation inequalities with tail distributions sensitive to $H$ and intermittency, and develop a hypothesis test for the null Hurst exponent, i.e.\ rough versus multifractal dynamics. Finally, we revisit scale invariance of the log volatility increment process via explicit small-intermittency formulas, reproducing analogous properties in both regimes.

q-fin.ST↗
arXiv2026

Asymmetric Long-Memory GARCH: Sign-Dependent Kernel Injection in a Two-Dimensional Markov Chain

We introduce ALM-GARCH, an asymmetric long-memory GARCH model in which positive and negative innovations enter conditional variance with different injection amplitudes and kernel offsets. These departures define testable level and memory channels relative to a nested symmetric benchmark. Positive Harris recurrence holds for interior configurations under a Foster-Lyapunov condition. Across five equity indices and Bitcoin, joint symmetry is rejected throughout, driven primarily by the level channel. The memory channel is supported for the Nikkei 225, KOSPI, and Bitcoin but is weakly identified when the positive branch is nearly inactive. Out-of-sample performance is broadly comparable to standard benchmarks.

q-fin.ST↗
arXiv2026

Modeling Trade Durations under Temporal Granularity Effects in Forex Markets

Trade durations in high-frequency foreign exchange data exhibit increased occurrence near integer values. To address this empirical phenomenon, we propose the granularity-adjusted autoregressive conditional duration (GA-ACD) model. It is based on a novel two-component mixture distribution consisting of a standard generalized gamma component for regular durations and a second component that locally redistributes probability mass around integer values to capture heaping. Conditional dynamics are modeled within a score-driven framework, allowing the scale parameter to vary over time in response to past durations, and enabling maximum likelihood estimation of all model parameters. A simulation study shows that ignoring heaping leads to biased parameter estimates and distorted inference regarding both the distribution and the dynamics of durations. An empirical analysis demonstrates that integer-duration clustering is pervasive across major currency pairs and that the GA-ACD model outperforms the standard generalized gamma ACD model.

q-fin.ST↗