Granularity and (Downside) Risk in Equity Markets

Ghysels, Éric; Liu, Hanwei; Raymond, Steve

doi:10.2139/ssrn.3156654

Cited by 2 publications

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“…1 Our work is complementary to the large macroeconomic literature that uses input-output tables, or other criteria such as firm size, to determine whether a certain series is granular, see among others Gabaix (2011), Acemoglu et al (2012), Di Giovanni and Levchenko (2012), Carvalho and Gabaix (2013), Di Giovanni, Levchenko and Mejean (2014), Bernard, Jensen, Redding and Schott (2017), Pesaran and Yang (2016), Gaubert and Itskhoki (2016) and Ghysels, Liuy and Raymondz (2017). Instead of relying on potentially arbitrary criteria for granular selection we detect granular series based on the covariance properties of the output data directly.…”

Section: Introductionmentioning

confidence: 99%

Detecting granular time series in large panels

Brownlees

Mesters

2021

Journal of Econometrics

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Large economic and financial panels often contain time series that influence the entire cross-section. We name such series granular. In this paper we introduce a panel data model that allows to formalize the notion of granular time series. We then propose a methodology, which is inspired by the network literature in statistics and econometrics, to detect the set of granulars when such set is unknown. The influence of the i-th series in the panel is measured by the norm of the i-th column of the inverse covariance matrix. We show that a detection procedure based on the column norms allows to consistently select granular series when the cross-section and time series dimensions are large. Importantly, the methodology allows to consistently detect granulars also when the series in the panel are influenced by common factors. A simulation study shows that the proposed procedures perform satisfactorily in finite samples. Our empirical studies demonstrate, among other findings, the granular influence of the automobile sector in US industrial production.

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