通过将损失纳入波动性预测来提高预测
Renaldas Urniezius1, Rytis Petrauskas1, Vygandas Vaitkus1
1Department of Automation, Kaunas University of Technology, Studentu St. 48, 51367 Kaunas, Lithuania.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究评估了异质自回归 (HAR) 模型的预测准确性. 输入损失函数和强线性模型在各种地平线上显示出卓越的性能,特别是添加了实现的四度性和VIX指数数据.
科学领域:
- 数量金融
- 经济计量学
- 金融建模
背景情况:
- 准确的金融市场预测对于风险管理和投资策略至关重要.
- 异质自回归模型 (HAR) 广泛用于波动性预测.
- 评估不同的估计技术和视野对于优化HAR模型性能至关重要.
研究的目的:
- 通过使用各种估计技术和视野,比较异质自回归模型的预测准确度.
- 确定HAR模型的最佳估计方法和预测范围.
- 评估外源变量和增强模型规范对预测准确性的影响.
主要方法:
- 研究了五种估计技术和四种预测范围的三种HAR型模型.
- 使用标准普尔500指数和VIX指数的5分钟内数据作为外部变量.
- 使用近似概率 (QLIKE),平均绝对误差 (MAE) 和平均平方误差 (MSE) 进行绩效评估.
主要成果:
- 透损失函数在所有时间段,特别是每周时间段中始终表现出最佳的近似概率 (QLIKE) 结果.
- 强大的线性模型被证明是一个具有竞争力的替代方案,在平均绝对误差 (MAE) 和平均平方误差 (MSE) 中表现出强的表现.
- 纳入实现的四度性 (HARQ模型) 和VIX指数显著改善了模型的整体预测准确性.
结论:
- 输入损失函数和强线性模型都显示了HAR模型的显著预测准确性.
- 估计技术和预测时间的选择对HAR模型的性能产生了重大影响.
- 用信息滞后和外源变量 (如VIX) 增强HAR模型,从而提高预测能力.
相关概念视频
01:10Entropy Change in Reversible Processes
2.7K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.7K
03:00Standard Entropy Change for a Reaction
21.2K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
21.2K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
00:59Propagation of Uncertainty from Random Error
1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
01:10Propagation of Uncertainty from Systematic Error
882
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
882
01:20Entropy and the Second Law of Thermodynamics
3.1K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
3.1K