英文标题：
《Predictability of Volatility Homogenised Financial Time Series》
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作者：
Pawe{\\l} Fiedor and Odd Magnus Trondrud
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最新提交年份：
2014
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英文摘要：
Modelling financial time series as a time change of a simpler process has been proposed in various forms over the years. One of such recent approaches is called volatility homogenisation decomposition, and has been designed specifically to aid the forecasting of price changes on financial markets. The authors of this method have attempted to prove the its usefulness by applying a specific forecasting procedure and determining the effectiveness of this procedure on the decomposed time series, as compared with the original time series. This is problematic in at least two ways. First, the choice of the forecasting procedure obviously has an effect on the results, rendering them non-exhaustive. Second, the results obtained were not completely convincing, with some values falling under 50% guessing rate. Additionally, only nine Australian stocks were being investigated, which further limits the scope of this proof. In this study we propose to find the usefulness of volatility homogenisation by calculating the predictability of the decomposed time series and comparing it to the predictability of the original time series. We are applying information-theoretic notion of entropy rate to quantify predictability, which guarantees the result is not tied to a specific method of prediction, and additionally we base our calculations on a large number of stocks from the Warsaw Stock Exchange.
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中文摘要：
多年来，人们以各种形式提出将金融时间序列建模为更简单过程的时间变化。最近的一种方法被称为波动率均一化分解，专门用于帮助预测金融市场上的价格变化。该方法的作者试图通过应用特定的预测程序，并与原始时间序列相比，确定该程序对分解时间序列的有效性，来证明其有效性。这至少在两个方面存在问题。首先，预测程序的选择显然会对结果产生影响，这使得预测结果并非详尽无遗。其次，获得的结果并不完全令人信服，一些值的猜测率低于50%。此外，只有九只澳大利亚股票正在接受调查，这进一步限制了这一证据的范围。在本研究中，我们建议通过计算分解时间序列的可预测性，并将其与原始时间序列的可预测性进行比较，来发现波动率均匀化的有用性。我们运用熵率的信息论概念来量化可预测性，这保证了结果与特定的预测方法无关，此外，我们的计算基于华沙证券交易所的大量股票。
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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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