Cameron Cornell, Lewis Mitchell, Matthew Roughan
arXiv 22 Aug 2024 · Finance — Statistical Finance · publishedPhysica A Statistical Mechanics and its Applications (2025) · 1 citations (OpenAlex)
arXiv:2408.12210 · PDF · DOI · OpenAlex · Extracted main text
Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, don't fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme - allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer - a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cornell, C., Mitchell, L., Roughan, M.: Vector autoregression in cry… (2023) ArXiv preprint arXiv:2308.15769 self | 0.843 | 3 | 3 | 100% |
| 2 | Ahelegbey, D.F., Cerchiello, P., Scaramozzino, R.: Network based evi… (2021) International Review of Financial Analysis 81, 102,101 – 102,101 (2021) | 0.511 | 2 | 1 | 100% |
| 3 | Cornell, C., Mitchell, L., Roughan, M.: Rank is all you need: Robust… (2024) In: Complex Networks & Their Applications XII, pp. 468–482. Springer Nature Switzerland, Cham (2024) self | 0.511 | 2 | 1 | 100% |
| 4 | Aste, T.: Cryptocurrency market structure: connecting emotions and e… (2019) Digital Finance 1 (2019) | 0.511 | 2 | 1 | 100% |
| 5 | Greene, W.H.: Econometric Analysis, 7th, international edition edn (2012) Pearson, Boston (2012) | 0.511 | 2 | 1 | 100% |
| 6 | Cornell, C., Mitchell, L., Roughan, M.: Rank is all you need: develo… (2024) Applied Network Science 9(1), 39 (2024) self | 0.511 | 2 | 1 | 100% |
| 7 | Al-Yahyaee, K.H., Mensi, W., Yoon, S.M.: Efficiency, multifractality… (2018) Finance Research Letters 27, 228–234 (2018) | 0.405 | 1 | 1 | 100% |
| 8 | Azqueta-Gavaldón, A.: Causal inference between cryptocurrency narrat… (2020) Physica A: Statistical Mechanics and its Applications 537, 122,574 (2020) | 0.405 | 1 | 1 | 100% |
| 9 | Balcilar, M., Gupta, R., Pierdzioch, C.: Does uncertainty move the g… (2016) Resources Policy 49, 74–80 (2016) | 0.405 | 1 | 1 | 100% |
| 10 | Baur, D.G.: The structure and degree of dependence: A quantile regre… (2013) Journal of Banking & Finance 37(3), 786–798 (2013) | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 41 scored citations.