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

已实现的波动、跳跃以及金融市场与实体经济之间的衔接

• 批准号: 0550929
• 负责人: Tim Bollerslev
• 金额: --
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0550929&HistoricalAwards=false
• 关键词: Realized; Volatility; Jumps; Interface; between

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.

许多不同的金融市场和工具的高频盘中资产价格和实时经济公告数据的最新可用性刺激了与这一新丰富数据来源的统计和经验分析有关的大量，快速增长的文献。该项目旨在进一步扩展我们通过开发新的和一般的计量经济学程序和建模范式以及特定的经验应用来从此类数据中提取有关重要经济现象的有用信息的能力。特别是，在调查人员较早的工作的基础上，他们寻求获得：（i）新的可靠的非参数程序，以将日期价格变化分解为连续和不连续的组件，以及用于建模，预测和定价连续和定价的相应程序跳跃风险； （ii）更好地理解事件类型或新闻的类型，这些事件类型或新闻的价格转移或跳高金融资产价格及其与宏观经济的关系； （iii）新的和改进的已实现的变化措施，以更好地适应高频数据中的市场微观结构并发症和其他摩擦； （iv）更好地理解经济基本面和资产市场之间的经验联系，这是由多个越野市场对特定宏观经济新闻公告的高频响应的同时启示的； （v）测量和建模时间变化的相关性和β因子负载以及对明显时间依赖性背后的宏观经济决定因素的新的和改进的程序。BROADER的影响：现在正是广泛地接受金融市场的波动性是可预测的，并且是可预测的，并且这种可预测性对资产定价，风险管理，监控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导致更好的波动率测量和更准确的预测。但是，相对于现有（G）Arch和随机波动性文献中采用的风格化参数模型的许多并发症，实际上的高频财务数据受到困扰较精细的采样盘中数据，开始从经验上实现。在InveseTigators先前的NSF赞助的研究中开发的实现的波动率指标和预测模型一直处于这些发展的最前沿。本提案旨在在多个重要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载的新的强大多元程序，以及开发和使用非参数措施，以评估可归因于整体价格变化的贡献跳高或不连续性，允许对价格过程的连续和不连续部分进行单独的建模，定价和对冲。重要的是，调查人员还寻求更好地了解在不同市场和国际上引起金融市场的大型价格变动的经济新闻类型，因此希望对资产市场与资产市场之间的基本联系有了新的启示。跨业务周期的真实经济。因此，拟议活动的总体结果应与应用宏观经济人，时间序列计算师，应用统计学家，财务研究人员，监管机构和从业人员相关。