Common Drifting Volatility in Large Bayesian VARs
Common Drifting Volatility in Large Bayesian VARs
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DOI：
10.1080/07350015.2015.1040116
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发表时间：
2016-07-01
影响因子：
3
通讯作者：
Marcellino, Massimiliano
中科院分区：
文献类型：
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作者：
Carriero, Andrea;Clark, Todd E.;Marcellino, Massimiliano
宏观经济和金融变量估计波动率的一般模式往往大致相似。我们提出了两个模型，其中条件波动特征共动，并研究他们使用美国的宏观经济数据。第一个模型指定由一个共同的未观察到的因素，加上一个特殊的组件驱动的条件波动。我们标记这个模型的BVAR一般因素随机波动率（BVAR-GFSV），我们表明，从假设一个共同的因素波动率的边际似然损失是温和的。第二个模型，我们标记为BVAR与共同随机波动率（BVAR-CSV），是一个特殊的情况下的BVAR-GFSV，其中的特质成分被消除，并加载到因子设置为1的所有条件波动率。此类限制允许VAR系数的后验方差采用方便的克罗内克结构，这反过来又允许即使使用大型数据集也能估计模型。虽然可能被错误指定，但与标准的同方差BVAR相比，BVAR-CSV模型得到了数据的强烈支持，并且通过利用大型数据集中包含的信息，它可以产生相对较好的点和密度预测。
The general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar. We propose two models in which conditional volatilities feature comovement and study them using U.S. macroeconomic data. The first model specifies the conditional volatilities as driven by a single common unobserved factor, plus an idiosyncratic component. We label this model BVAR with general factor stochastic volatility (BVAR-GFSV) and we show that the loss in terms of marginal likelihood from assuming a common factor for volatility is moderate. The second model, which we label BVAR with common stochastic volatility (BVAR-CSV), is a special case of the BVAR-GFSV in which the idiosyncratic component is eliminated and the loadings to the factor are set to 1 for all the conditional volatilities. Such restrictions permit a convenient Kronecker structure for the posterior variance of the VAR coefficients, which in turn permits estimating the model even with large datasets. While perhaps misspecified, the BVAR-CSV model is strongly supported by the data when compared against standard homoscedastic BVARs, and it can produce relatively good point and density forecasts by taking advantage of the information contained in large datasets.