英文标题：
《Modeling stock markets through the reconstruction of market processes》
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作者：
Jo\\~ao Pedro Rodrigues do Carmo
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最新提交年份：
2018
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英文摘要：
  There are two possible ways of interpreting the seemingly stochastic nature of financial markets: the Efficient Market Hypothesis (EMH) and a set of stylized facts that drive the behavior of the markets. We show evidence for some of the stylized facts such as memory-like phenomena in price volatility in the short term, a power-law behavior and non-linear dependencies on the returns.   Given this, we construct a model of the market using Markov chains. Then, we develop an algorithm that can be generalized for any N-symbol alphabet and K-length Markov chain. Using this tool, we are able to show that it\'s, at least, always better than a completely random model such as a Random Walk. The code is written in MATLAB and maintained in GitHub.
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中文摘要：
有两种可能的方法可以解释金融市场看似随机的性质：有效市场假说（EMH）和一组驱动市场行为的程式化事实。我们展示了一些程式化事实的证据，例如短期内价格波动中的记忆现象、幂律行为和对回报的非线性依赖。有鉴于此，我们利用马尔可夫链构建了一个市场模型。然后，我们开发了一个可以推广到任何N符号字母表和K长度马尔可夫链的算法。使用这个工具，我们能够证明它至少总是优于完全随机的模型，比如随机行走。代码是用MATLAB编写的，并在GitHub中维护。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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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