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Volatility spillovers and heavy tails: a large t-Vector AutoRegressive approach

机译:波动性溢出和沉重的尾巴:大型t矢量自回归方法

摘要

Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the possible fat-tailed distribution of the VAR model errors using a VAR model with errors following a multivariate Student t-distribution with unknown degrees of freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of the VAR model with t-distributed errors. We study volatility spillovers among energy, biofuel and agricultural commodities and reveal bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.
机译:波动性是财务分析中衡量风险的关键指标。今天一种金融资产的高波动性可能会影响明天另一种资产的波动性。使用向量自回归(VAR)模型研究了波动率之间的这些滞后效应(我们称为波动率溢出)。我们使用具有不确定自由度的多元Student t分布后的误差的VAR模型,说明VAR模型误差的可能的胖尾分布。此外,我们研究了大量资产之间的波动溢出效应。为此,我们使用带有t分布误差的VAR模型的惩罚估计。我们研究了能源,生物燃料和农产品之间的波动性溢出,并揭示了能源和生物燃料之间以及能源和农产品之间的双向波动性溢出。

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