GEOVOL: A NEW STATISTICAL MODEL FOR GEOPOLITICAL RISK

这一建议开发了一种名为GEOVOL的新的地缘政治风险统计指标，它将对理解、衡量和对冲地缘政治风险产生广泛影响。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。通过考察影响所有市场的冲击，这一指标将重点放在重大新闻上。社会已经习惯了那些看似具有重大影响，但并不能打动市场的新闻报道。冠状病毒的传播在2020年给几乎所有金融市场带来了非同寻常的冲击。与英国退欧选举和9·11爆炸事件类似，这些事件可以被解读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此类事件的分布和影响。除了开发统计模型外，这项研究还将收集和存储有关地缘政治风险的数据。通过在V-LAB网站上发布对GEOVOL的每日估计，世界各地的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开发的几个流行的衡量标准。这次演讲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，即地缘政治风险是对所有国家、部门、因素和资产类别中金融资产特殊波动性的创新。该建议利用了苏珊娜·马丁斯和罗伯特·恩格尔开发的统计模型，扩展了一个传统框架：一系列风险因素产生金融资产之间的同期相关性。这些资产扣除因素后的波动性是可预测的，而对这些波动性的创新是标准化残差的平方。这些标准化残差将是横截面不相关的，但它们的平方可以是相关的，并最终被证明是相关的。有地缘政治冲击的一天，将是大多数平方标准化残差高于平均水平的一天。随着资产和时间序列的数量变大，可以一致地估计共同因素GEOVOL和要素负载量。该提议要求得到支持：1)完成一篇记录全球股市数据分析的论文；2)将这一模型放入V-Lab，估计值每天更新并在互联网上发布；3)扩展该模型以适应不同时区的调整；4)将该模型应用于大宗商品、货币和跨资产类别数据；5)通过反向测试来自该理论的风险降低来检验投资组合的影响；6)将该理论扩展到包括多因素模型。该模型在理解多变量波动率模型以及不同资产和国家/地区的特殊波动性如何变得相关方面是一个重大进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.