摘要翻译：
我们探索了一个简单的格场模型来描述高频金融市场的统计特性。该模型适用于经济物理学的交叉领域。它的标志性特征是自组织临界状态的出现。这意味着模型的尺度不变性，而不需要调整参数。我们模拟的显著结果是收益、价格、波动率和收益频率分布的时间序列，这些都与历史市场数据的特征相比较。将标准GARCH(1,1）拟合应用于格型模型，给出的结果与纳斯达克历史数据几乎无法区分。
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英文标题：
《Replicating financial market dynamics with a simple self-organized
  critical lattice model》
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作者：
B. Dupoyet and H.R. Fiebig and D.P. Musgrove
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最新提交年份：
2010
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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英文摘要：
  We explore a simple lattice field model intended to describe statistical properties of high frequency financial markets. The model is relevant in the cross-disciplinary area of econophysics. Its signature feature is the emergence of a self-organized critical state. This implies scale invariance of the model, without tuning parameters. Prominent results of our simulation are time series of gains, prices, volatility, and gains frequency distributions, which all compare favorably to features of historical market data. Applying a standard GARCH(1,1) fit to the lattice model gives results that are almost indistinguishable from historical NASDAQ data.
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PDF链接：
https://arxiv.org/pdf/1010.4831