GEOVOL: A NEW STATISTICAL MODEL FOR GEOPOLITICAL RISK

GEOVOL：地缘政治风险的新统计模型

• 批准号: 2018923
• 负责人: Robert Engle
• 金额: $ 24.47万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018923&HistoricalAwards=false
• 关键词: GEOVOL; NEW; STATISTICAL; MODEL; GEOPOLITICAL

This proposal develops a new statistical measure of geopolitical risk called GEOVOL, which will have broad implications for understanding, measuring, and hedging geopolitical risk. GEOVOL will involve multiple factors and data sets, a deeper set of measures of geopolitical risks can be presented. By examining shocks that move all markets, this measure focuses on material news. Society has become used to news stories that appear to have massive implications, but do not move the markets. The spread of the coronavirus provided an extraordinary shock to virtually all financial markets in 2020. Similar to Brexit election and 9-11 Bombings, these can be interpreted as truly geopolitical events which were essentially unpredictable. This research will help to understand the distribution and impact of such events. In addition to developing the statistical model, this research will also collect and store data on geopolitical risk. By posting daily estimates of GEOVOL on the V-LAB web site, investors world-wide will be able to see the extent of this geopolitical risk. The web site will also incorporate several popular measures that have been developed by other research teams. This presentation will help firms and individuals everywhere to make better decisions. The GEOVOL measure is based on the insight that geopolitical risk is an innovation to the idiosyncratic volatility of financial assets in all countries, sectors, factors and asset classes. The proposal utilizes a statistical model, developed by Susana Martins and Robert Engle, extending a conventional framework: a set of risk factors generate contemporaneous correlation among financial assets. Volatilities of these assets net of factors are predictable and the innovation to these volatilities are the squared standardized residuals. These standardized residuals will be cross-sectionally uncorrelated but their squares can be and turn out to be correlated. A day with a geopolitical shock will be a day when most of the squared standardized residuals are above average. The common factor GEOVOL and factor loading can be estimated consistently as the number of assets and time series become large. The proposal requests support to 1) complete a paper documenting the analysis of the global equity market data, 2) take this model and put it into V-LAB where estimates are updated daily and published on the internet, 3) extend the model to adjust for different time zones, 4) apply the model to commodities, currencies and cross asset class data, 5) examine the portfolio implications by back-testing the risk reductions that come from the theory and 6) extend the theory to include multi- factor models. This model is a major advance in understanding multivariate volatility models and how idiosyncratic volatilities of different assets and countries become correlated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该提案开发了一种新的地缘政治风险统计指标，称为GEOVOL，这将对理解，衡量和对冲地缘政治风险产生广泛的影响。 GEOVOL将涉及多个因素和数据集，可以提出一套更深层次的地缘政治风险衡量标准。通过研究影响所有市场的冲击，这一指标侧重于重大新闻。社会已经习惯了那些看起来有巨大影响，但并不影响市场的新闻故事。2020年，新型冠状病毒的传播对几乎所有金融市场都造成了巨大冲击。 与英国脱欧选举和9-11爆炸事件类似，这些事件可以被解释为真正的地缘政治事件，本质上是不可预测的。 这项研究将有助于了解这些事件的分布和影响。除了开发统计模型外，这项研究还将收集和存储地缘政治风险数据。通过在V-LAB网站上发布GEOVOL的每日估计，全球投资者将能够看到这种地缘政治风险的程度。 该网站还将纳入其他研究小组制定的几项流行措施。 本演示文稿将帮助各地的公司和个人做出更好的决策。 GEOVOL指标基于这样一种见解，即地缘政治风险是所有国家、行业、因素和资产类别金融资产特殊波动性的一种创新。该提案利用了苏珊娜·马丁斯（Susana Martins）和罗伯特·恩格尔（Robert Engle）开发的统计模型，扩展了传统的框架：一组风险因素在金融资产之间产生同期相关性。 这些资产的波动率是可预测的，这些波动率的新息是标准化残差的平方。 这些标准化残差在横截面上是不相关的，但它们的平方可以是相关的，并且最终是相关的。 地缘政治冲击的一天将是大多数标准化残差平方都高于平均值的一天。 随着资产和时间序列数量的增加，可以一致地估计公共因子GEOVOL和因子载荷。该提案要求支持1）完成一份文件，记录全球股票市场数据的分析，2）采用该模型并将其放入V-LAB，每天更新估计并在互联网上发布，3）扩展模型以调整不同的时区，4）将模型应用于商品，货币和跨资产类别数据，5）通过回溯测试来自该理论的风险降低来检查投资组合的含义，以及6）将该理论扩展到包括多因素模型。 该模型是理解多元波动模型以及不同资产和国家的特质波动如何相互关联的重大进步。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。