论文标题
多元分布预测的静态和动态模型：适当的评分规则测试与多元GARCH模型
Static and Dynamic Models for Multivariate Distribution Forecasts: Proper Scoring Rule Tests of Factor-Quantile vs. Multivariate GARCH Models
论文作者
论文摘要
存在多种静态和动态模型，以预测风险和其他用于金融风险管理中的与分数相关的指标。行业实践倾向于偏爱更简单的静态模型，例如历史模拟或其变体，而大多数学术研究中的Garch家族动态模型中心。尽管许多研究研究了多元模型对预测风险指标的准确性，但对于准确预测整个多元分布的研究很少。然而，这是具有非分析解决方案的资产定价或投资组合优化问题的基本要素。我们使用各种适当的多元评分规则来解决这个高度复杂的问题，以评估超过100,000个八维多元分布的预测：汇率，利率和商品期货。这样，我们测试静态模型的性能，即。经验分布函数和新的因子量词模型，在不对称的多元GARCH类中具有常用的动态模型。
A plethora of static and dynamic models exist to forecast Value-at-Risk and other quantile-related metrics used in financial risk management. Industry practice tends to favour simpler, static models such as historical simulation or its variants whereas most academic research centres on dynamic models in the GARCH family. While numerous studies examine the accuracy of multivariate models for forecasting risk metrics, there is little research on accurately predicting the entire multivariate distribution. Yet this is an essential element of asset pricing or portfolio optimization problems having non-analytic solutions. We approach this highly complex problem using a variety of proper multivariate scoring rules to evaluate over 100,000 forecasts of eight-dimensional multivariate distributions: of exchange rates, interest rates and commodity futures. This way we test the performance of static models, viz. empirical distribution functions and a new factor-quantile model, with commonly used dynamic models in the asymmetric multivariate GARCH class.
