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首页> 外文期刊>The European journal of finance >Affine and quadratic models with many factors and few parameters
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Affine and quadratic models with many factors and few parameters

机译:带有许多因素和少数参数的仿射和二次模型

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'Classic' affine and quadratic term structure models in the literature usually have three or four factors and tens of parameters. However affine and quadratic term structure models with many factors and few parameters (MFFP), i.e. with up to twenty factors and with six to seven parameters, fit and predict U.S. and Euro sovereign yields better than 'classic' affine and quadratic models. MFFP models also fit the volatility of and the correlations between changes in yields of different maturities better than 'classic' models. MFFP models outperform because fewer parameters reduce in sample over-fitting and because more factors give models more flexibility to match yields of different maturities. Among MFFP models, a type of affine model with stochastic volatility is usually preferable to the homoschedastic affine model, but for U.S. yields the quadratic model seems preferable among five factor MFFP models.
机译:文献中的“经典”仿射和二次术语结构模型通常具有三个或四个因素和数十个参数。然而,具有许多因素和少数参数(MFFP)的仿射和二次术语结构模型,即最多20个因素,并且具有六到七个参数,适合和预测美国和欧元的主权比“经典”仿射和二次模型更好。 MFFP模型也符合不同成熟的产量之间的波动性和相关性比“经典”模型的关系。 MFFP型号优于胜过,因为更少的参数减少样品过度拟合,因为更多的因素使模型更具灵活性,以匹配不同的日期产量。在MFFP模型中,具有随机挥发性的一种仿射模型通常优于Homoschedastic仿射模型,但对于U.S.产生二次模型在五因素MFFP模型中似乎是优选的。

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