2015
DOI: 10.1142/s2382626615500082
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A Million Metaorder Analysis of Market Impact on the Bitcoin

Abstract: We present a thorough empirical analysis of market impact on the Bitcoin/USD exchange market using a complete dataset that allows us to reconstruct more than one million metaorders. We empirically confirm the "square-root law" for market impact, which holds on four decades in spite of the quasi-absence of statistical arbitrage and market marking strategies. We show that the square-root impact holds during the whole trajectory of a metaorder and not only for the final execution price. We also attempt to decompo… Show more

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Cited by 52 publications
(44 citation statements)
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“…More precisely, we consider the limit ϕ, λ → 0, while keeping L constant. 6 Note that in the limit T tc, one recorvers the normal diffusion results of Donier et al [11] In this limit -and starting from the equilibrium book ψ 0 (x) = ψ eq (x) -Eq. (16) becomes:…”
Section: Market Impactmentioning
confidence: 98%
“…More precisely, we consider the limit ϕ, λ → 0, while keeping L constant. 6 Note that in the limit T tc, one recorvers the normal diffusion results of Donier et al [11] In this limit -and starting from the equilibrium book ψ 0 (x) = ψ eq (x) -Eq. (16) becomes:…”
Section: Market Impactmentioning
confidence: 98%
“…A metaorder of total size Q impacts the price as ∼ ffiffiffiffi Q p and not proportionally to Q as naively expected and actually predicted by classical economics arguments [2]. The square-root law is surprisingly universal: it is found to be to a large degree independent of details such as the asset class, time period, execution style, and market venues [3][4][5][6][7][8][9][10][11][12][13][14]. In particular, the advent of electronic markets and high frequency trading has not altered the square-root behavior, in spite of radical changes in the microstructure of markets.…”
mentioning
confidence: 88%
“…Such data-driven approaches were especially made attractive with development of (a) fast and efficient computation, (b) vast amounts of data and (c) modern Machine Learning algorithms that include classical statistical learning techniques (Hastie et al 2001) and more recently, Deep Learning (Goodfellow et al 2016). Goals of such studies vary from studying market impact and order book modelling (Donier and Bonart 2015;Cont et al 2014), to extracting predictive capability from various market microstructure features (Dixon 2018;Sirignano and Cont 2018). Criticism around data-driven approach is centred around the fact that studies tackle the data head-on, often studying the statistical mechanics of the after-facts, rather than asking fundamental questions about possible origins of the underlying processes.…”
Section: Approaches To Lob Analysismentioning
confidence: 99%
“…A small fraction of research focuses on macroscopic price dynamics of cryptocurrencies ). To our current knowledge, there are only two academic studies that concern themselves with market microstructure of digital assets (Donier and Bonart 2015;Guo and Antulov-Fantulin 2018) and some blog posts that shine light on the subject (Heusser 2013). Technology research is concerned with various improvements of blockchain, such as scalability, throughput, applications to new industries and disruption of existing services by means of decentralisation.…”
Section: Current Researchmentioning
confidence: 99%
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