A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data
A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data
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DOI：
10.1115/1.4029751
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Wei Yang;Ai Han
中科院分区：
文献类型：
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作者：
Wei Yang;Ai Han
本文提出了一种基于区间的方法来建模和预测金融资产价格的价格区间或基于区间的波动过程。与现有的波动率模型相比，该模型利用了区间时间序列中包含的更多信息，而不是只利用区间信息或分别对高、低价格过程进行建模。对美国股市日数据的实证研究表明，所提出的区间模型在样本内和样本外的极差过程预测中均比经典的点线性模型更准确.统计检验表明，在大多数情况下，基于区间的模型的预测优势在统计上是显著的。此外，还通过不同的样本窗口和预测期进行了一些稳定性测试，以确定基于区间的模型的优势，结果显示了相似的结果。该研究为基于区间的认知语言学研究提供了一个新的视角。
This paper proposes an interval-based methodology to model and forecast the price range or range-based volatility process of financial asset prices. Comparing with the existing volatility models, the proposed model utilizes more information contained in the interval time series than using the range information only or modeling the high and low price processes separately. An empirical study of the U.S. stock market daily data shows that the proposed interval-based model produces more accurate range forecasts than the classic point-based linear models for range process, in terms of both in-sample and out-of-sample forecasts. The statistical tests show that the forecasting advantages of the interval-based model are statistically significant in most cases. In addition, some stability tests have been conducted for ascertaining the advantages of the interval-based model through different sample windows and forecasting periods, which reveals similar results. This study provides a new interval-based perspective f...