基于深度学习模型的包含日内正负跳变的波动率预测

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-05-15 DOI:10.1002/for.3146
Yilun Zhang, Yuping Song, Ying Peng, Hanchao Wang
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引用次数: 0

摘要

现有的关于波动率预测的研究大多侧重于日间特征,而忽略了高频数据的日内特征,尤其是正负跳变对波动率的非对称影响。本文利用 5 分钟高频数据构建已实现波动率,并将其分解为连续成分和正负方向的跳跃成分。然后,将这些信息与长短期记忆模型相结合,对已实现波动率进行预测。实证分析表明,与正向跳跃相比,负面新闻导致的负向跳跃对市场波动的影响更为显著。此外,在预测样本外波动率方面,包含正负跳跃波动率的长期短期记忆模型优于传统计量经济学和机器学习模型。此外,与广义自回归条件异方差模型相比,将预测结果应用于风险价值会产生更好的测量效果。
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Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model

Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high-frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5-min high-frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short-term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short-term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out-of-sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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