"Modeling, forecasting and diagnostic testing for financial and econometric time series"

“金融和计量经济时间序列的建模、预测和诊断测试”

• 批准号: 418500-2012
• 负责人: Chen, Bei
• 金额: $ 1.09万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=508625
• 关键词: Modeling; forecasting; diagnostic; testing; financial

The proposed research is in the area of Time Series Analysis and Statistics in Business, Economics and Finance. Its main objectives are (i) developing new models for the volatility of high frequency data; (ii) designing effective methods for measuring and forecasting volatility; (iii) constructing diagnostic tests for the detection of changes in the market dynamics. The context of this research is today's financial markets where transactions and price changes are recorded on the millisecond time scale. The classical quantitative models developed by statisticians and econometricians in the 1980s fail to explain characteristic features of such high frequency data. More suitable models have been introduced since the late 1990s, however, there is still a lack of comprehensive models explaining market behaviour both on the intra- and inter-daily level. The proposed research aims to close this gap and lead to a better understanding of how market dynamics manifests itself on different time scales. Its main focus is on the study of volatility which measures the variation of commodity prices over time and has important application in risk management. A special focus will be on efficient methods for forecasting volatility. Another main line of research is the development of models for the durations between transaction events. Empirical studies have shown strong correlations among the durations, so there is a need for models which can capture this long-range dependence in the data. Finally, the proposed research aims to look at models in which the market dynamics is governed by a set of latent variables. Highly relevant questions in this context are how regime switches in the latent structure can be detected, and to what extent it is possible to forecast market dynamics. The proposed research will yield methods which help researchers and practitioners in the Canadian financial industry to better understand the dynamics of financial markets, obtain more accurate predictions and better assess risk-return tradeoffs. It involves the training of five undergraduate, eight Master's and one PhD students who will acquire solid knowledge in financial time series analysis, strong programming and problem solving skills, and gain experience in frontier interdisciplinary research.

拟议的研究是在商业，经济和金融领域的时间序列分析和统计。其主要目标是：（一）开发高频数据波动性的新模型;（二）设计衡量和预测波动性的有效方法;（三）构建检测市场动态变化的诊断测试。这项研究的背景是今天的金融市场，交易和价格变化记录在毫秒的时间尺度上。统计学家和计量经济学家在20世纪80年代开发的经典定量模型无法解释这种高频数据的特征。自20世纪90年代末以来，已经引入了更合适的模型，但是，仍然缺乏全面的模型来解释每日内和每日之间的市场行为。拟议的研究旨在缩小这一差距，并更好地了解市场动态如何在不同的时间尺度上表现出来。它的主要重点是研究波动性，衡量商品价格随时间的变化，并在风险管理中有重要的应用。一个特别的重点将是预测波动性的有效方法。研究的另一条主线是开发交易事件之间的持续时间模型。实证研究表明，持续时间之间存在很强的相关性，因此需要能够在数据中捕捉这种长期依赖性的模型。最后，拟议的研究旨在研究模型，其中市场动态是由一组潜变量。在这种情况下，高度相关的问题是如何在潜在结构的政权转换可以检测到，以及在何种程度上是可能的预测市场动态。拟议的研究将产生方法，帮助加拿大金融业的研究人员和从业人员更好地了解金融市场的动态，获得更准确的预测和更好地评估风险回报权衡。它涉及五名本科生，八名硕士和一名博士生的培训，他们将获得金融时间序列分析的扎实知识，强大的编程和解决问题的能力，并获得前沿跨学科研究的经验。