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This paper examines the relationship between oil movements and systemic risk of financial institution in major petroleum-based economies. We estimate ΔCoVaR for those institutions and observe the presence of elevated increases in its levels corresponding to the subprime and global financial crises. The results provide evidence in favor of risk measurement improvements by accounting for oil returns in the risk functions. The spread between the standard CoVaR and the CoVaR that includes oil absorbs in a time range longer than the duration of the oil shock. This indicates that the drop in the oil price has a longer effect on risk and requires more time to be discounted by the financial institutions. To support the analysis, we consider also the other major market-based systemic risk measures.
The impact of network connectivity on factor exposures, asset pricing and portfolio diversification
(2017)
This paper extends the classic factor-based asset pricing model by including network linkages in linear factor models. We assume that the network linkages are exogenously provided. This extension of the model allows a better understanding of the causes of systematic risk and shows that (i) network exposures act as an inflating factor for systematic exposure to common factors and (ii) the power of diversification is reduced by the presence of network connections. Moreover, we show that in the presence of network links a misspecified traditional linear factor model presents residuals that are correlated and heteroskedastic. We support our claims with an extensive simulation experiment.
Causality is a widely-used concept in theoretical and empirical economics. The recent financial economics literature has used Granger causality to detect the presence of contemporaneous links between financial institutions and, in turn, to obtain a network structure. Subsequent studies combined the estimated networks with traditional pricing or risk measurement models to improve their fit to empirical data. In this paper, we provide two contributions: we show how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints; and we demonstrate that Granger causality should be combined with quantile-based causality when the focus is on risk propagation. The empirical evidence supports the latter claim.