Realized Volatility, Jumps and the Interface between Financial Markets and the Real Economy

最近，大量不同金融市场和金融工具的高频日内资产价格和实时经济公告数据的出现，促使大量且快速增长的文献关注这一新的丰富数据来源的统计和实证分析。该项目旨在进一步扩展我们的能力，通过开发新的和通用的计量经济学程序和建模范式，结合具体的实证应用，从这些数据中提取有关重要经济现象的有用信息。特别是，在研究人员早期工作的基础上，他们寻求获得：(i)新的鲁棒非参数程序，用于将每天的价格变化分解为连续和不连续的组成部分，以及相应的程序，用于建模，预测和定价连续和跳跃风险；（ii）更好地理解导致金融资产价格大幅波动或跃升的事件或新闻类型及其与宏观经济的关系；（iii）新的和改进的已实现变异措施，以更好地适应市场微观结构的复杂性和高频数据中的其他摩擦；（iv）更好地理解经济基本面与资产市场之间的经验联系，正如多个跨国市场对特定宏观经济新闻公告的同时高频反应所阐明的那样；(v)新的和改进的测量和模拟时变相关性和β因子负荷的程序，以及对明显的时间依赖性背后的宏观经济决定因素的更好理解。更广泛的影响：目前，人们普遍认为金融市场波动是可预测的，这种可预测性对资产定价、风险管理、监测和监督具有深远的实际意义。从理论上讲，使用更频繁的采样数据应该会导致更好的波动性测量和更准确的预测。然而，与现有的(G)ARCH和随机波动率文献中采用的风格化参数模型相比，实际的高频金融数据受到许多复杂性的困扰，并且直到最近才有一些收益，预计将通过使用更精细的采样日内数据来利用，开始实现经验。研究人员在之前nsf赞助的研究中开发的已实现的波动率测量和预测模型一直处于这些发展的前沿。本提案试图在几个重要的方向上扩展这些想法，包括用于测量和预测已实现的相关性和因素负荷的新的强大的多变量程序，以及用于评估可归因于跳跃或不连续的总体价格变化的贡献的非参数度量的开发和使用，从而允许对价格过程的连续和不连续部分进行单独建模、定价和对冲。重要的是，调查人员还试图更好地了解在不同市场和国际上引发金融市场大幅价格波动的经济新闻类型，并希望借此揭示跨商业周期资产市场与实体经济之间的基本联系。因此，拟议活动的一般结果应与应用宏观经济学家、时间序列计量经济学家、应用统计学家、金融研究人员、监管机构和从业人员等相关。

The recent availability of high-frequency intraday asset prices and real-time economic announcement data for a host of different financial markets and instruments has spurred a large and rapidly growing literature concerned with the statistical and empirical analysis of this new rich source of data. This project aims to further expand on our ability to extract useful information about important economic phenomena from such data through the development of new and general econometric procedures and modeling paradigms, coupled with specific empirical applications. In particular, building on the investigators' earlier work, they seek to obtain: (i) new robust non-parametric procedures for disentangling the day-today price variation into continuous and discontinuous components, and corresponding procedures for modeling, forecasting and pricing continuous and jump risks; (ii) a better understanding of the type of events, or news, that induces large price movements, or jumps, in financial asset prices and their relation to the macro economy; (iii) new and improved realized variation measures for better accommodating market microstructure complications and other frictions in the high-frequency data; (iv) a better understanding of the empirical linkages between economic fundamentals and asset markets, as illuminated by the simultaneous high-frequency response of multiple cross-country markets to specific macroeconomic news announcements; (v) new and improved procedures for measuring and modeling time-varying correlations and beta factor loadings and a better understanding of the macroeconomic determinants behind the apparent temporal dependencies.Broader Impacts: It is by now widely accepted that financial market volatility is predictable, and that this predictability has profound practical implications for asset pricing, risk management, monitoring, and oversight. In theory, the use of more frequently sampled data should result in better volatility measurements and more accurate forecasts. However, actual high-frequency financial data are beset by a host of complications relative to the stylized parametric models employed in the existing (G)ARCH and stochastic volatility literature and only very recently have some of the gains, expected to be harnessed from the use of finer sampled intraday data, started to materialize empirically. The realized volatility measures and forecasting models developed in the invesetigators' prior NSF-sponsored research have been at the forefront of these developments. The present proposal seeks to expand on these ideas in several important directions, including new robust multivariate procedures for measuring and forecasting realized correlations and factor loadings, along with the development and use of non-parametric measures for assessing the contribution to the overall price variation attributable to jumps, or discontinuities, in turn allowing for separate modeling, pricing and hedging of the continuous and discontinuous part of the price process. Importantly, the investigators also seek to obtain a better understanding of the type of economic news that induces large price movements in financial markets, both across different markets and internationally, and as such hope to shed new light on the fundamental linkages between asset markets and the real economy across business cycles. The general results of the proposed activities should therefore be of relevance to applied macroeconomists, time series econometricians, applied statisticians, financial researchers, regulators, and practitioners alike.