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基于数据驱动的波动率模型研究进展

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波动率是度量标的资产投资收益不确定性的重要指标,在金融、能源和环境等领域有广泛的应用.由于真实的波动率无法直接观测,因此构建合理的波动率模型来估计真实波动率显得尤为重要.本文试图从数据驱动的角度入手,基于低频、高频和混频数据三个方面对国内外波动率模型的研究成果进行综述,以期为该主题的后续研究提供借鉴.
The Advancement in Data-driven Volatility Models
Volatility,as an important index for evaluation of uncertainty of the underlying asset,has been widely used in the field of finance,economy,energy,environment and so on.With the rapid development of financial markets,volatility risk has become a major systemic risk,and thus has been identified as a re-search priority by financial market regulators,financial institutions and investors.Modeling and forecas-ting the volatility of financial assets are necessities in the financial market risk management,but the vola-tility itself is not directly observable,so it is imperative to construct a suitable volatility model which can offer a good forecasting of real volatility.Driven by the evaluations of usable data sources,the research a-chievements of volatility models were summarized from low frequency data,high frequency data and mixed frequency data in order to provide references for the following research.

volatilityfinancial marketlow frequency datahigh frequency datamixed frequency data

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上海兴伟学院,上海 201300

波动率 金融市场 低频数据 高频数据 混频数据

2024

上海管理科学
上海市管理科学协会

上海管理科学

CHSSCD
影响因子:0.466
ISSN:1005-9679
年,卷(期):2024.46(6)