Can a zero-intelligence plus model explain the stylized facts of financial time series data?
Can a zero-intelligence plus model explain the stylized facts of financial time series data?
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零智能加模型能否解释金融时间序列数据的程式化事实？
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
W. Ng
中科院分区：
文献类型：
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作者：
Imon Palit;S. Phelps;W. Ng
许多基于代理人的金融市场模型都能够通过使用行为在很大程度上是随机的“零智力”代理人，重现在实际经验时间序列数据中观察到的某些程式化事实，以确定某些现象是否源于市场微观结构而不是战略行为。虽然这些模型非常成功，但它们无法解释每一个典型事实，这并不奇怪，而且确实似乎有道理的是，虽然有些现象纯粹来自市场微观结构，但其他现象来自参与代理人的行为，正如更复杂的基于代理人的模型所表明的那样，这些模型使用被赋予各种形式的战略行为的代理人。鉴于零智力模型和战略模型都能够解释各种现象，一个有趣的问题是，是否有混合的，“零智力加”模型包含最少量的战略行为，同时能够解释所有的程式化事实。我们推测，随着我们逐渐提高金融市场的零智能模型中的战略行为水平，我们将获得与经验金融时间序列数据的程式化事实越来越好的拟合。我们通过系统地评估几种不同的实验处理来测试这一假设，在这些实验处理中，我们逐步将最低限度的战略行为水平添加到我们的模型中，并测试由此产生的价格回报的时间序列的以下统计特征：厚尾，波动性聚类，持久性和非高斯性。令人惊讶的是，由此产生的“零智力加”模型并没有引入更多的现实主义的时间序列，从而支持其他研究，其中断言，金融市场中的一些现象确实是更复杂的学习，互动和适应的结果。
Many agent-based models of financial markets have been able to reproduce certain stylized facts that are observed in actual empirical time series data by using "zero-intelligence" agents whose behaviour is largely random in order to ascertain whether certain phenomena arise from market micro-structure as opposed to strategic behaviour. Although these models have been highly successful, it is not surprising that they are unable to explain every stylized fact, and indeed it seems plausible that although some phenomena arise purely from market micro-structure, other phenomena arise from the behaviour of the participating agents, as suggested by more complex agent-based models which use agents endowed with various forms of strategic behaviour. Given that both zero-intelligence and strategic models are each able to explain various phenomena, an interesting question is whether there are hybrid, "zero-intelligence plus" models containing a minimal amount of strategic behaviour that are simultaneously able to explain all of the stylized facts. We conjecture that as we gradually increase the level of strategic behaviour in a zero-intelligence model of a financial market we will obtain an increasingly good fit with the stylized facts of empirical financial time-series data. We test this hypothesis by systematically evaluating several different experimental treatments in which we incrementally add minimalist levels of strategic behaviour to our model, and test the resulting time series of price returns for the following statistical features: fat tails, volatility clustering, persistence and non-Gaussianity. Surprisingly, the resulting "zero-intelligence plus" models do not introduce more realism to the time series, thus supporting other research which conjectures that some phenomena in the financial markets are indeed the result of more sophisticated learning, interaction and adaptation.