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Hierarchical causality in financial economics

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  • Diane Wilcox
  • Tim Gebbie

Abstract

Hierarchical analysis is considered and a multilevel model is presented in order to explore causality, chance and complexity in financial economics. A coupled system of models is used to describe multilevel interactions, consistent with market data: the lowest level is occupied by agents generating the prices of individual traded assets; the next level entails aggregation of stocks into markets; the third level combines shared risk factors with information variables and bottom-up, agent-generated structure, consistent with conditions for no-arbitrage pricing theory; the fourth level describes market factors which originate in the greater economy and the highest levels are described by regulated market structure and the customs and ethics which define the nature of acceptable transactions. A mechanism for emergence or innovation is considered and causal sources are discussed in terms of five causation classes.

Suggested Citation

  • Diane Wilcox & Tim Gebbie, 2014. "Hierarchical causality in financial economics," Papers 1408.5585, arXiv.org, revised Sep 2014.
    • Handle: RePEc:arx:papers:1408.5585
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    Cited by:

    1. Ivan Jericevich & Dharmesh Sing & Tim Gebbie, 2021. "CoinTossX: An open-source low-latency high-throughput matching engine," Papers 2102.10925, arXiv.org.
    2. Dieter Hendricks & Tim Gebbie & Diane Wilcox, 2015. "Detecting intraday financial market states using temporal clustering," Papers 1508.04900, arXiv.org, revised Feb 2017.
    3. Joel da Costa & Tim Gebbie, 2020. "Learning low-frequency temporal patterns for quantitative trading," Papers 2008.09481, arXiv.org.
    4. Donovan Platt & Tim Gebbie, 2016. "Can Agent-Based Models Probe Market Microstructure?," Papers 1611.08510, arXiv.org, revised Aug 2017.
    5. Dicks, Matthew & Paskaramoorthy, Andrew & Gebbie, Tim, 2024. "A simple learning agent interacting with an agent-based market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    6. Patrick Chang & Roger Bukuru & Tim Gebbie, 2019. "Revisiting the Epps effect using volume time averaging: An exercise in R," Papers 1912.02416, arXiv.org, revised Feb 2020.
    7. Dieter Hendricks & Adam Cobb & Richard Everett & Jonathan Downing & Stephen J. Roberts, 2017. "Inferring agent objectives at different scales of a complex adaptive system," Papers 1712.01137, arXiv.org.
    8. Tim Gebbie & Fayyaaz Loonat, 2016. "Learning zero-cost portfolio selection with pattern matching," Papers 1605.04600, arXiv.org.
    9. Andrew Paskaramoorthy & Terence van Zyl & Tim Gebbie, 2020. "A Framework for Online Investment Algorithms," Papers 2003.13360, arXiv.org.
    10. Yelibi, Lionel & Gebbie, Tim, 2020. "Fast Super-Paramagnetic Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).

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