Econometric Volatility Measurement, Modeling, and Forecasting

作者：Diebold，弗朗西斯X.组织机构：宾夕法尼亚大学标题：计量经济学波动性测量、建模和预测本研究深化和拓宽了经济学波动性测量、建模和预测的科学工具。 在理论和实证方面，它扩展并显著完成了由研究者及其合著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，在近二十年的波动性文献中一直没有得到解决，被广泛认为是同时具有高度挑战性和至关重要的文献的全面发展。 这项工作的更广泛影响也同样巨大，因为它始终侧重于消除现有工具与政府、政策组织和行业中广大从业者社区所需工具之间的差距。知识价值：无论如何，测量，建模，波动率的预测是近二十年来时间序列计量经济学研究中最活跃、最成功的领域之一。 然而，一些最具挑战性和最重要的问题仍然没有解决，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高维多变量数据，以及（3）理解条件方差动态与一般条件均值动态的关系（或缺乏），特别是市场时机能力。 Diebold的工作通过构建和评估（1）用于衰减市场微观结构噪声对波动率估计的有害影响的过滤方法，（2）用于高维情况下波动率测量，建模和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，直接有助于他们的解决方案。 这项工作扩展了理论和实证计量经济学的前沿，推动了特殊半鞅的经验二次变分新理论，高频建模的新方法，以及至关重要的是，它们的交叉点。 首先，它将通过研究人员的指导和与研究生的合作直接促进教学和学习。 第二，它将通过在网上广泛传播所有研究成果，接触代表性不足的群体。 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：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界面的理解），研究人员和国家之间（通过利用国家和国际合作作者和联合项目），以及学术界和包括政府在内的其他社区之间，政策组织和产业（通过促进和加速学术界的知识转让）。 这项研究还将大大推动波动性测量，建模和预测的日常应用，通过改善风险管理，资产定价和资产配置造福社会，从而改善金融市场和宏观经济的一般功能。

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.