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.

最近获得了许多不同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，这促使大量快速增长的文献关注对这一新的丰富数据来源的统计和实证分析。该项目旨在通过发展新的和一般的计量经济学程序和模型范例，再加上具体的实证应用，进一步扩大我们从这些数据中提取有关重要经济现象的有用信息的能力。特别是，在调查人员早期工作的基础上，他们寻求获得：(1)新的稳健的非参数程序，用于将当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，用于对连续和跳跃风险进行建模、预测和定价；(2)更好地理解导致金融资产价格大幅波动或跳跃的事件或新闻的类型及其与宏观经济的关系；(3)新的和改进的已实现的变化措施，以更好地适应市场微观结构复杂情况和高频数据中的其他摩擦；(4)更好地理解经济基本面和资产市场之间的经验联系，这从多个跨国市场对特定宏观经济新闻公告的同时高频反应中得到说明；(5)衡量和模拟时变相关性和贝塔因素负荷的新的和改进的程序，以及更好地理解明显的时间相关性背后的宏观经济决定因素。广泛影响：现在人们普遍接受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影响。从理论上讲，使用更频繁的抽样数据应该会带来更好的波动性衡量和更准确的预测。然而，与现有的(G)ARCH和随机波动率文献中采用的程式化参数模型相比，实际的高频金融数据受到一系列复杂因素的困扰，直到最近，一些预期通过使用更精细的采样日内数据而获得的收益才开始经验地实现。投资人之前在NSF赞助的研究中开发的已实现波动率衡量标准和预测模型一直处于这些发展的前沿。本提案力求在几个重要方向扩展这些想法，包括用于衡量和预测已实现的相关性和要素负荷的新的强有力的多变量程序，以及开发和使用非参数措施来评估可归因于跳跃或不连续的总体价格变化的贡献，从而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，并因此希望对跨商业周期的资产市场和实体经济之间的根本联系有新的了解。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、监管者和从业人员相关。