英文文献：The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options-多元复杂模型的价值:对道琼斯工业平均指数期权定价的应用
英文文献作者：Jeroen V.K. Rombouts,Lars Stentoft,Francesco Violante
英文文献摘要：
We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differ in their specification of the conditional variance, conditional correlation, and innovation distribution. All models belong to the dynamic conditional correlation class which is particularly suited because it allows to consistently estimate the risk neutral dynamics with a manageable computational effort in relatively large scale problems. It turns out that the most important gain in pricing accuracy comes from increasing the sophistication in the marginal variance processes (i.e. nonlinearity, asymmetry and component structure). Enriching the model with more complex correlation models, and relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a smaller way. Apart from investigating directly the value of model sophistication in terms of dollar losses, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performance.

我们评估了在道琼斯工业平均指数的定价期权方面的大量多元波动模型的预测准确性。我们通过考虑一组248个在条件方差、条件相关性和创新分布规范上不同的多元模型，以美元损失来衡量模型复杂性的价值。所有的模型都属于动态条件相关类，这是特别适合的，因为它允许一致地估计风险中性动态与一个可管理的计算努力，在相对大的规模问题。结果表明，在定价准确性方面最重要的收益来自于边际方差过程(即非线性、非对称性和成分结构)的复杂性的增加。用更复杂的相关模型来丰富模型，在拉普拉斯创新假设下放宽高斯创新，对定价的改善作用较小。除了根据美元损失直接调查模型复杂性的价值之外，我们还使用模型置信集方法从统计学上推断出提供最佳定价表现的一组模型。