Endogenous switch and a memory in cross-market bitcoin prices: A practitioner’s dilemma
Endogenous switch and a memory in cross-market bitcoin prices: A practitioner’s dilemma
Literature shows that Efficient Market Hypothesis (EMH) or Stock-to-Flow (SF) models have poor predictive power for virtual currencies, particularly Bitcoin, because prices move too much compared with what one would expect from the predictions of these models. The magnitude of `memory' (or slow-mean convergence) and inherent endogenous switches in the growth trajectory of Bitcoin prices can explain such large price fluctuations. This paper argues and empirically shows that Bitcoin investors are driven, although not beaten by `memory' as well as a number of endogenous switches in the price level. Both memory and endogenous switches are compatible with what we call an endogenous growth mechanism in Bitcoin market. Finding of persistence has also relevance to the theory of learning: an agent that learns synchronously is an agent that will depict less persistence behaviour. We employ a variant of Auto Regressive Fractionally Integrated Moving Average (ARFIMA) Markov model with endogenous switches to characterise the profile of price fluctuations in cross-market Bitcoin prices. We show that Bitcoin markets depict true long memory and that price dynamics are mostly driven by endogenous feedback mechanism or market reflexivity.
Bitcoin, Cross-market, volatility, Endogenous switch, structural breaks, Long memory, Fractional integration, Persistence mechanism
Maaitah, Ahmad
7057414d-762b-49d8-9724-f6d9e1fffd09
Mishra, Tapas
218ef618-6b3e-471b-a686-15460da145e0
Uddin, Gazi Salah
1fb91726-92eb-4441-8a40-b5dee36201f9
9 September 2021
Maaitah, Ahmad
7057414d-762b-49d8-9724-f6d9e1fffd09
Mishra, Tapas
218ef618-6b3e-471b-a686-15460da145e0
Uddin, Gazi Salah
1fb91726-92eb-4441-8a40-b5dee36201f9
Maaitah, Ahmad, Mishra, Tapas and Uddin, Gazi Salah
(2021)
Endogenous switch and a memory in cross-market bitcoin prices: A practitioner’s dilemma.
In Emerging Topics in Financial Economics Workshop 2021, Department of Management and Engineering, Linköping University.
46 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Literature shows that Efficient Market Hypothesis (EMH) or Stock-to-Flow (SF) models have poor predictive power for virtual currencies, particularly Bitcoin, because prices move too much compared with what one would expect from the predictions of these models. The magnitude of `memory' (or slow-mean convergence) and inherent endogenous switches in the growth trajectory of Bitcoin prices can explain such large price fluctuations. This paper argues and empirically shows that Bitcoin investors are driven, although not beaten by `memory' as well as a number of endogenous switches in the price level. Both memory and endogenous switches are compatible with what we call an endogenous growth mechanism in Bitcoin market. Finding of persistence has also relevance to the theory of learning: an agent that learns synchronously is an agent that will depict less persistence behaviour. We employ a variant of Auto Regressive Fractionally Integrated Moving Average (ARFIMA) Markov model with endogenous switches to characterise the profile of price fluctuations in cross-market Bitcoin prices. We show that Bitcoin markets depict true long memory and that price dynamics are mostly driven by endogenous feedback mechanism or market reflexivity.
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Published date: 9 September 2021
Venue - Dates:
Emerging Topics in Financial Economics: Third Symposium at the Division of Economics<br/>, Linköping University, Sweden, Linköping, Sweden, 2020-03-12 - 2020-03-13
Keywords:
Bitcoin, Cross-market, volatility, Endogenous switch, structural breaks, Long memory, Fractional integration, Persistence mechanism
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Local EPrints ID: 451868
URI: http://eprints.soton.ac.uk/id/eprint/451868
PURE UUID: 998b90b4-6cb6-4b2c-aa6c-7203fee4339e
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Date deposited: 02 Nov 2021 17:41
Last modified: 17 Mar 2024 04:04
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Author:
Gazi Salah Uddin
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