Determining which variables affect price realized volatility has always been challenging. This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility. In particular, the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index, euro–US dollar exchange rate, price of gold, and price of Brent crude oil on the realized volatility of natural gas. These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed; the euro–US dollar exchange rate was the primary financial asset and explained 40.1% of the influence. The results of the proposed daily analysis differed from those of the methodology used to study the entire period. The traditional model, which studies the entire period, cannot determine temporal effects, whereas the proposed methodology can. The proposed methodology allows us to distinguish the effects for each day, week, or month rather than averages for entire periods, with the flexibility to analyze different frequencies and periods. This methodological capability is key to analyzing influences and making decisions about realized volatility.

中文翻译：

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基于混合计量经济学和机器学习的天然气实际波动率建模

确定哪些变量影响已实现价格的波动性一直具有挑战性。本文建议通过制定最佳的日常预测来解释金融资产如何影响已实现的波动性。该方法建议基于使用最好的计量经济学和机器学习模型来预测已实现的波动性。特别是，使用异质自回归和长期短期记忆模型的最佳预测来确定标准普尔 500 指数、欧元兑美元汇率、黄金价格和布伦特原油价格对已实现收益的影响。天然气的波动性。这些金融资产在 87.4% 的分析天数中影响了天然气的已实现波动率；欧元兑美元汇率是主要金融资产，解释了40.1%的影响。拟议的每日分析结果与用于研究整个时期的方法的结果不同。研究整个时期的传统模型无法确定时间效应，而所提出的方法可以。所提出的方法使我们能够区分每天、每周或每月的影响，而不是整个时期的平均值，并能够灵活地分析不同的频率和时期。这种方法能力是分析影响并就已实现的波动率做出决策的关键。