Different Approaches to Forecast Interval Time Series: A Comparison in Finance
Different Approaches to Forecast Interval Time Series: A Comparison in Finance
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DOI：
10.1007/s10614-010-9230-2
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发表时间：
2011-02
影响因子：
2
通讯作者：
J. Arroyo;R. Espínola;C. Maté
中科院分区：
文献类型：
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作者：
J. Arroyo;R. Espínola;C. Maté
间隔时间序列（ITS）是一个时间序列，其中每个周期都由一个间隔来描述。在金融中，ITS可以描述资产价格在一段时间内的高和低的时间演变。这些价格区间与波动性的概念有关，值得考虑以放置买入或卖出订单。本文回顾了两种预测ITS的方法。一方面，第一种方法包括使用单变量或多变量预测方法。高和低的值之间的可能的协整关系进行了分析，多变量模型和VAR模型的等价性示出的最小和最大的时间序列，以及为中心和半径的时间序列。另一方面，第二种方法采用经典的预测方法来处理ITS使用区间算法。这些方法包括指数平滑、k-NN算法和多层感知器。这些方法的性能进行了研究，在两个金融ITS。结果表明，ITS具有可预报性，尤其是在区间范围内。这一事实为波动性预测开辟了一条新的道路。
An interval time series (ITS) is a time series where each period is described by an interval. In finance, ITS can describe the temporal evolution of the high and low prices of an asset throughout time. These price intervals are related to the concept of volatility and are worth considering in order to place buy or sell orders. This article reviews two approaches to forecast ITS. On the one hand, the first approach consists of using univariate or multivariate forecasting methods. The possible cointegrating relation between the high and low values is analyzed for multivariate models and the equivalence of the VAR models is shown for the minimum and the maximum time series, as well as for the center and radius time series. On the other hand, the second approach adapts classic forecasting methods to deal with ITS using interval arithmetic. These methods include exponential smoothing, thek-NN algorithm and the multilayer perceptron. The performance of these approaches is studied in two financial ITS. As a result, evidences of the predictability of the ITS are found, especially in the interval range. This fact opens a new path in volatility forecasting.