Econometric Volatility Measurement, Modeling, and Forecasting

项目编号：0317720 P I: Diebold， Francis X.组织机构：宾夕法尼亚大学标题：计量波动率测量、建模和预测本研究深化和拓宽了经济学中波动率测量、建模和预测的科学工具。在理论和经验上，它扩展并显著地完成了研究者及其合作者开发和推广的第二代波动率模型的研究计划。这项工作的智力价值很高，因为所解决的问题，在波动性文献中已经逃避了近二十年的解决方案，被广泛认为同时具有高度挑战性，对文献的全面发展至关重要。这项工作的广泛影响同样高，因为它始终关注于消除可用工具与政府、政策组织和行业中的大型从业者社区所需的工具之间的差距。知识价值：在过去的二十年里，波动性的测量、建模和预测一直是时间序列计量经济学研究领域中最活跃和最成功的领域之一。然而，几个最具挑战性和最重要的问题仍未解决，包括如何(1)处理市场微观结构噪声对波动率估计的污染，(2)处理在实践中经常相关的非常高维的多元数据，以及(3)理解条件方差动态与条件均值动态的关系（或缺乏关系），特别是市场时机选择能力。Diebold的工作通过构建和评估(1)用于降低市场微观结构噪声对波动性估计的有害影响的过滤方法，(2)用于波动性测量、建模和高维情况预测的潜在因素框架，以及(3)用于理解条件均值动态、条件方差动态和市场运动之间联系的框架，直接为他们的解决方案做出了贡献。这项工作扩展了理论和经验计量经济学的前沿，推动了特殊半鞅的经验二次变分的新理论，高频建模的新经验，以及至关重要的，它们的交叉。更广泛的影响：项目的更广泛的影响是实质性的和多重的。首先，它将通过研究者的指导和与研究生的合作直接促进教学和学习。其次，它将通过广泛的网络传播所有研究成果，接触到代表性不足的群体。第三，它将通过建立各种合作来加强研究和教育的基础设施：在学科之间（通过加深我们对宏观经济学/金融经济学与波动性相关的接口的理解），在研究人员与国家之间（通过利用国家和国际合作和联合项目），以及在学术界与包括政府，政策组织和行业在内的其他社区之间（通过促进和加速学术界的知识转移）。本研究还将显著推动波动性测量、建模和预测走向常规应用，通过改善风险管理、资产定价和资产配置，从而改善金融市场和宏观经济的总体功能，从而造福社会。

Prop ID: 0317720 P I: Diebold, Francis X. Organization: University of Pennsylvania Title: Econometric Volatility Measurement, Modeling, and ForecastingThis research both deepens and broadens the scientific tools available for volatility measurement, modeling, and forecasting in economics. Both theoretically and empirically, it extends and significantly completes the research program on second generation volatility models developed and popularized by the investigator and his coauthors. The intellectual merit of the work is high, as the problems addressed, which have eluded solution in the volatility literature for nearly two decades, are widely acknowledged to be simultaneously highly challenging and crucially important to the full development of the literature. The broader impacts of the work are equally high, as it focuses throughout on eliminating the gaps between the available tools and those needed by the large communities of practitioners in government, policy organizations, and industry.Intellectual Merit: By any measure, the measurement, modeling, and forecasting of volatility has been one of the most active and successful areas of time-series econometric research areas in the past twenty years. However, several of the most challenging and important problems remain unresolved, including how to (1) deal with pollution of volatility estimates by market microstructure noise, (2) deal with the very high-dimensional multivariate data often relevant in practice, and (3) understand conditional variance dynamics in their relation (or lack thereof) to conditional mean dynamics in general, and market timing ability in particular. Diebold's work contributes directly to their solution by constructing and evaluating (1) filtering methods for attenuating the deleterious effects of market microstructure noise on volatility estimates, (2) a latent-factor framework for volatility measurement, modeling, and forecasting in high-dimensional situations, and (3) a framework for understanding the links among conditional mean dynamics, conditional variance dynamics, and market movements. The work extends both theoretical and empirical econometrics frontiers, pushing forward the new theory of empirical quadratic variation for special semi-martingales, the new empirics of high-frequency modeling, and crucially, their intersection.Broader Impacts: The broader impacts of the project are substantial and several-fold. First, it will contribute directly to teaching and learning via the investigator's mentoring and collaborating with graduate students. Second, it will reach out to underrepresented groups via broad web-based dissemination of all research results. Third, it will enhance infrastructure for research and education by establishing a variety of collaborations: between disciplines (by deepening our understanding of the macroeconomics / financial economics interface as related to volatility), between researchers and nations (by utilizing national and international coauthorships and joint projects), and between academia and other communities including government, policy organizations and industry (by facilitating and accelerating knowledge transfer from academia). This research will also significantly push volatility measurement, modeling, and forecasting toward routine application, benefiting society via improved risk management, asset pricing, and asset allocation, which in turn improve the general functioning of financial markets and the macroeconomy.