Volatility spillover between economic sectors in financial crisis prediction: Evidence spanning the great financial crisis and Covid-19 pandemic

被引:74
作者
Laborda, Ricardo [1 ]
Olmo, Jose [2 ,3 ]
机构
[1] Acad Gen Mil, Ctr Univ Def, Ctra Huesca S-N, Zaragoza 50090, Spain
[2] Univ Zaragoza, Dept Anal Econ, Gran Via 2, Zaragoza 50005, Spain
[3] Univ Southampton, Dept Econ, Highfield Campus, Southampton SO17 1BJ, Hants, England
关键词
Covid-19; Financial crises; Sectoral connectedness; Volatility spillovers; S&P 500 volatility; Random forest; RISK; OIL;
D O I
10.1016/j.ribaf.2021.101402
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper measures volatility spillovers between sectors of economic activity using network connectivity measures. Volatility spillovers are an accurate proxy for the transmission of risk across sectors and are particularly informative during crisis periods. To do this, we apply the novel methodology proposed in Diebold and Yilmaz (2012) to seven economic sectors of U.S. economic activity and find that Banking&Insurance, Energy, Technology and Biotechnology are the main channels through which shocks propagate to the rest of the economy. Banking&Insurance is especially relevant during the 2007-2009 global financial crisis while the Energy sector and Technology are especially relevant during the COVID-19 crisis. We also show that volatility spillovers exhibit ability to predict high episodes of volatility for the S&P 500 index being useful as early financial crisis indicators.
引用
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页数:13
相关论文
共 33 条
[1]   Systemic Risk and Stability in Financial Networks [J].
Acemoglu, Daron ;
Ozdaglar, Asuman ;
Tahbaz-Salehi, Alireza .
AMERICAN ECONOMIC REVIEW, 2015, 105 (02) :564-608
[2]   The Network Origins of Aggregate Fluctuations [J].
Acemoglu, Daron ;
Carvalho, Vasco M. ;
Ozdaglar, Asuman ;
Tahbaz-Salehi, Alireza .
ECONOMETRICA, 2012, 80 (05) :1977-2016
[3]  
Aloui D., 2020, COVID 19's Impact on Crude Oil and Natural Gas S&P GS Indexes
[4]  
[Anonymous], 2021, IEEE Trans. Broadcast.
[5]  
[Anonymous], 2015, FINANCIAL MACROECONO, DOI DOI 10.1093/ACPROF:OSO/9780198747123.003.0008
[6]  
Breiman L, 1996, ANN STAT, V24, P2350
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]  
Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1007/BF00058655
[9]  
Breiman L., 1996, Out of Bag Estimation
[10]  
Breiman L., 1984, CLASSIFICATION REGRE, V1st ed., DOI [10.1201/9781315139470, DOI 10.1201/9781315139470]