# Exploring Financial Networks Using Quantile Regression and Granger Causality

@inproceedings{Karpman2022ExploringFN, title={Exploring Financial Networks Using Quantile Regression and Granger Causality}, author={Kara Karpman and Samriddha Lahiry and Diganta Mukherjee and Suman Basu}, year={2022} }

In the post-crisis era, ﬁnancial regulators and policymakers are increasingly interested in data-driven tools to measure systemic risk and to identify systemically important ﬁrms. Granger Causality (GC) based techniques to build networks among ﬁnancial ﬁrms using time series of their stock returns have received signiﬁcant attention in recent years. Existing GC network methods model conditional means, and do not distinguish between connectivity in lower and upper tails of the return distribution…

## One Citation

### Learning Financial Networks with High-frequency Trade Data

- Economics
- 2022

Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized…

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