英文标题：
《Complexity, Chaos, and the Duffing-Oscillator Model: An Analysis of
  Inventory Fluctuations in Markets》
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作者：
Varsha S. Kulkarni
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最新提交年份：
2013
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英文摘要：
  Apparently random financial fluctuations often exhibit varying levels of complexity, chaos. Given limited data, predictability of such time series becomes hard to infer. While efficient methods of Lyapunov exponent computation are devised, knowledge about the process driving the dynamics greatly facilitates the complexity analysis. This paper shows that quarterly inventory changes of wheat in the global market, during 1974-2012, follow a nonlinear deterministic process. Lyapunov exponents of these fluctuations are computed using sliding time windows each of length 131 quarters. Weakly chaotic behavior alternates with non-chaotic behavior over the entire period of analysis. More importantly, in this paper, a cubic dependence of price changes on inventory changes leads to establishment of deterministic Duffing-Oscillator-Model(DOM) as a suitable candidate for examining inventory fluctuations of wheat. DOM represents the interaction of commodity production cycle with an external intervention in the market. Parameters obtained for shifting time zones by fitting the Fourier estimated time signals to DOM are able to generate responses that reproduce the true chaotic nature exhibited by the empirical signal at that time. Endowing the parameters with suitable meanings, one may infer that temporary changes in speculation reflect the pattern of inventory volatility that drives the transitions between chaotic and non-chaotic behavior.
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中文摘要：
显然，随机的金融波动往往表现出不同程度的复杂性和混乱。由于数据有限，很难推断此类时间序列的可预测性。虽然设计了有效的Lyapunov指数计算方法，但有关驱动动力学过程的知识极大地促进了复杂性分析。本文表明，1974-2012年全球市场上小麦的季度库存变化遵循非线性确定性过程。这些波动的李雅普诺夫指数是使用长度为131个四分之一的滑动时间窗计算的。在整个分析过程中，弱混沌行为与非混沌行为交替发生。更重要的是，在本文中，价格变化对库存变化的立方依赖性导致建立确定性杜芬振子模型（DOM），作为检验小麦库存波动的合适候选者。DOM代表商品生产周期与外部市场干预的相互作用。通过将傅里叶估计的时间信号拟合到DOM来获得用于移动时区的参数，能够生成再现当时经验信号所显示的真实混沌性质的响应。赋予这些参数适当的含义，可以推断投机行为的暂时变化反映了推动混沌行为和非混沌行为之间转换的库存波动模式。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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