Bitcoin Dynamics: The Inverse Square Law of Price Fluctuations and Other Stylized Facts View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Soumya Easwaran , Manu Dixit , Sitabhra Sinha

ABSTRACT

Analysis of time-series data of different markets have produced evidence for several stylized facts (universal features) including heavy tails characterized by power law exponents, which provide us tantalizing hints of the dynamics underlying such complex systems. It is especially important to see how these features evolve over time after the market is created and gradually develops. The recent advent of the digital currency, Bitcoin, and its growing popularity as an asset traded between agents over the last few years, provides us with an invaluable dataset for such a study. Similar to many financial markets, Bitcoin is de-centralized and its value is not controlled by a single institution, (e.g., a central bank). Here we have analyzed high-frequency Bitcoin trading data (with a resolution of one tick, i.e., a single trading event). We show that the distribution of price fluctuation (measured in terms of logarithmic return) has a heavy tail. The exponent of the tail implies that Bitcoin fluctuations follow an inverse square law, in contrast to the inverse cubic law exhibited by most financial and commodities markets. The distribution of transaction sizes and trading volume are seen to have Levy-stable distribution. Multi-scale analysis show the presence of long term memory effects in market behavior. More... »

PAGES

121-128

References to SciGraph publications

  • 1998-05. Inverse cubic law for the distribution of stock price variations in THE EUROPEAN PHYSICAL JOURNAL B
  • Book

    TITLE

    Econophysics and Data Driven Modelling of Market Dynamics

    ISBN

    978-3-319-08472-5
    978-3-319-08473-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-08473-2_4

    DOI

    http://dx.doi.org/10.1007/978-3-319-08473-2_4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011021834


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1502", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Banking, Finance and Investment", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/15", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Commerce, Management, Tourism and Services", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Institute of Mathematical Sciences", 
              "id": "https://www.grid.ac/institutes/grid.462414.1", 
              "name": [
                "The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, 600113, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Easwaran", 
            "givenName": "Soumya", 
            "id": "sg:person.0673751775.94", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0673751775.94"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Birla Institute of Technology and Science", 
              "id": "https://www.grid.ac/institutes/grid.418391.6", 
              "name": [
                "Birla Institute of Technology and Science, Pilani, 333031, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Dixit", 
            "givenName": "Manu", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute of Mathematical Sciences", 
              "id": "https://www.grid.ac/institutes/grid.462414.1", 
              "name": [
                "The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, 600113, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sinha", 
            "givenName": "Sitabhra", 
            "id": "sg:person.01106420703.25", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106420703.25"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s100510050292", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004743102", 
              "https://doi.org/10.1007/s100510050292"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1209/0295-5075/77/58004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019815785"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.83.016101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040217165"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.83.016101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040217165"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/096031096333917", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050413569"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0378-4371(99)00395-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053710840"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2109682", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069765822"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015", 
        "datePublishedReg": "2015-01-01", 
        "description": "Analysis of time-series data of different markets have produced evidence for several stylized facts (universal features) including heavy tails characterized by power law exponents, which provide us tantalizing hints of the dynamics underlying such complex systems. It is especially important to see how these features evolve over time after the market is created and gradually develops. The recent advent of the digital currency, Bitcoin, and its growing popularity as an asset traded between agents over the last few years, provides us with an invaluable dataset for such a study. Similar to many financial markets, Bitcoin is de-centralized and its value is not controlled by a single institution, (e.g., a central bank). Here we have analyzed high-frequency Bitcoin trading data (with a resolution of one tick, i.e., a single trading event). We show that the distribution of price fluctuation (measured in terms of logarithmic return) has a heavy tail. The exponent of the tail implies that Bitcoin fluctuations follow an inverse square law, in contrast to the inverse cubic law exhibited by most financial and commodities markets. The distribution of transaction sizes and trading volume are seen to have Levy-stable distribution. Multi-scale analysis show the presence of long term memory effects in market behavior.", 
        "editor": [
          {
            "familyName": "Abergel", 
            "givenName": "Fr\u00e9d\u00e9ric", 
            "type": "Person"
          }, 
          {
            "familyName": "Aoyama", 
            "givenName": "Hideaki", 
            "type": "Person"
          }, 
          {
            "familyName": "Chakrabarti", 
            "givenName": "Bikas K.", 
            "type": "Person"
          }, 
          {
            "familyName": "Chakraborti", 
            "givenName": "Anirban", 
            "type": "Person"
          }, 
          {
            "familyName": "Ghosh", 
            "givenName": "Asim", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-08473-2_4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-319-08472-5", 
            "978-3-319-08473-2"
          ], 
          "name": "Econophysics and Data Driven Modelling of Market Dynamics", 
          "type": "Book"
        }, 
        "name": "Bitcoin Dynamics: The Inverse Square Law of Price Fluctuations and Other Stylized Facts", 
        "pagination": "121-128", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-08473-2_4"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "34831af684ae90007a1da714bf222ad028cb9cb3366fdc8ba5d5ec3a44962205"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1011021834"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-08473-2_4", 
          "https://app.dimensions.ai/details/publication/pub.1011021834"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T17:12", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8678_00000249.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-3-319-08473-2_4"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-08473-2_4'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-08473-2_4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-08473-2_4'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-08473-2_4'


     

    This table displays all metadata directly associated to this object as RDF triples.

    120 TRIPLES      23 PREDICATES      33 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-08473-2_4 schema:about anzsrc-for:15
    2 anzsrc-for:1502
    3 schema:author Nfc6ad90d4d354d579a04416b113c99bb
    4 schema:citation sg:pub.10.1007/s100510050292
    5 https://doi.org/10.1016/s0378-4371(99)00395-7
    6 https://doi.org/10.1080/096031096333917
    7 https://doi.org/10.1103/physreve.83.016101
    8 https://doi.org/10.1209/0295-5075/77/58004
    9 https://doi.org/10.2307/2109682
    10 schema:datePublished 2015
    11 schema:datePublishedReg 2015-01-01
    12 schema:description Analysis of time-series data of different markets have produced evidence for several stylized facts (universal features) including heavy tails characterized by power law exponents, which provide us tantalizing hints of the dynamics underlying such complex systems. It is especially important to see how these features evolve over time after the market is created and gradually develops. The recent advent of the digital currency, Bitcoin, and its growing popularity as an asset traded between agents over the last few years, provides us with an invaluable dataset for such a study. Similar to many financial markets, Bitcoin is de-centralized and its value is not controlled by a single institution, (e.g., a central bank). Here we have analyzed high-frequency Bitcoin trading data (with a resolution of one tick, i.e., a single trading event). We show that the distribution of price fluctuation (measured in terms of logarithmic return) has a heavy tail. The exponent of the tail implies that Bitcoin fluctuations follow an inverse square law, in contrast to the inverse cubic law exhibited by most financial and commodities markets. The distribution of transaction sizes and trading volume are seen to have Levy-stable distribution. Multi-scale analysis show the presence of long term memory effects in market behavior.
    13 schema:editor N5419d7dc08ce4c1db7d15f3a7f243126
    14 schema:genre chapter
    15 schema:inLanguage en
    16 schema:isAccessibleForFree false
    17 schema:isPartOf Na6917c8094594668b45468fe2846dd05
    18 schema:name Bitcoin Dynamics: The Inverse Square Law of Price Fluctuations and Other Stylized Facts
    19 schema:pagination 121-128
    20 schema:productId N1502c6ac7dbf46d9b8b98adb7cdc6010
    21 N4394f73362a3468cb4b305c8a39ae405
    22 Nc9ef6d9806b94dc4b94a262e6e18b8c9
    23 schema:publisher N2a36600affb04ed6ac0aa073f91d1d45
    24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011021834
    25 https://doi.org/10.1007/978-3-319-08473-2_4
    26 schema:sdDatePublished 2019-04-15T17:12
    27 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    28 schema:sdPublisher N63c4de60cd4f4f07bc9500a91de2e9c6
    29 schema:url http://link.springer.com/10.1007/978-3-319-08473-2_4
    30 sgo:license sg:explorer/license/
    31 sgo:sdDataset chapters
    32 rdf:type schema:Chapter
    33 N106c2174f101422787c2569093f6ef31 rdf:first N9f51d2b29bc6418eb7e36dae251b31d0
    34 rdf:rest rdf:nil
    35 N1502c6ac7dbf46d9b8b98adb7cdc6010 schema:name doi
    36 schema:value 10.1007/978-3-319-08473-2_4
    37 rdf:type schema:PropertyValue
    38 N2a36600affb04ed6ac0aa073f91d1d45 schema:location Cham
    39 schema:name Springer International Publishing
    40 rdf:type schema:Organisation
    41 N4394f73362a3468cb4b305c8a39ae405 schema:name dimensions_id
    42 schema:value pub.1011021834
    43 rdf:type schema:PropertyValue
    44 N44507dc27a80490f9f683cf320dd1ebe schema:familyName Chakrabarti
    45 schema:givenName Bikas K.
    46 rdf:type schema:Person
    47 N5419d7dc08ce4c1db7d15f3a7f243126 rdf:first Nc716f86568d9426eb383b16df54e21b3
    48 rdf:rest N96856f3d61e74bd681ede3ba0417bca0
    49 N62967f4a36c34f71b3381d740db6a600 schema:familyName Aoyama
    50 schema:givenName Hideaki
    51 rdf:type schema:Person
    52 N63c4de60cd4f4f07bc9500a91de2e9c6 schema:name Springer Nature - SN SciGraph project
    53 rdf:type schema:Organization
    54 N737658552fbb4eafb50f82354cc75522 rdf:first N921fd58f8c934658b33ba60c7ea4fa94
    55 rdf:rest Nce1da32d3cda45d1b8c6aaabdba403ae
    56 N823a301c5dda4fad90abbdb2101baa22 schema:familyName Chakraborti
    57 schema:givenName Anirban
    58 rdf:type schema:Person
    59 N921fd58f8c934658b33ba60c7ea4fa94 schema:affiliation https://www.grid.ac/institutes/grid.418391.6
    60 schema:familyName Dixit
    61 schema:givenName Manu
    62 rdf:type schema:Person
    63 N96856f3d61e74bd681ede3ba0417bca0 rdf:first N62967f4a36c34f71b3381d740db6a600
    64 rdf:rest Nd0133c648334492fb2c7ff9c9efb62c8
    65 N9f51d2b29bc6418eb7e36dae251b31d0 schema:familyName Ghosh
    66 schema:givenName Asim
    67 rdf:type schema:Person
    68 Na6917c8094594668b45468fe2846dd05 schema:isbn 978-3-319-08472-5
    69 978-3-319-08473-2
    70 schema:name Econophysics and Data Driven Modelling of Market Dynamics
    71 rdf:type schema:Book
    72 Nc716f86568d9426eb383b16df54e21b3 schema:familyName Abergel
    73 schema:givenName Frédéric
    74 rdf:type schema:Person
    75 Nc9ef6d9806b94dc4b94a262e6e18b8c9 schema:name readcube_id
    76 schema:value 34831af684ae90007a1da714bf222ad028cb9cb3366fdc8ba5d5ec3a44962205
    77 rdf:type schema:PropertyValue
    78 Nce1da32d3cda45d1b8c6aaabdba403ae rdf:first sg:person.01106420703.25
    79 rdf:rest rdf:nil
    80 Nd0133c648334492fb2c7ff9c9efb62c8 rdf:first N44507dc27a80490f9f683cf320dd1ebe
    81 rdf:rest Nd52762bc843145059c2b3936c4eeb47f
    82 Nd52762bc843145059c2b3936c4eeb47f rdf:first N823a301c5dda4fad90abbdb2101baa22
    83 rdf:rest N106c2174f101422787c2569093f6ef31
    84 Nfc6ad90d4d354d579a04416b113c99bb rdf:first sg:person.0673751775.94
    85 rdf:rest N737658552fbb4eafb50f82354cc75522
    86 anzsrc-for:15 schema:inDefinedTermSet anzsrc-for:
    87 schema:name Commerce, Management, Tourism and Services
    88 rdf:type schema:DefinedTerm
    89 anzsrc-for:1502 schema:inDefinedTermSet anzsrc-for:
    90 schema:name Banking, Finance and Investment
    91 rdf:type schema:DefinedTerm
    92 sg:person.01106420703.25 schema:affiliation https://www.grid.ac/institutes/grid.462414.1
    93 schema:familyName Sinha
    94 schema:givenName Sitabhra
    95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106420703.25
    96 rdf:type schema:Person
    97 sg:person.0673751775.94 schema:affiliation https://www.grid.ac/institutes/grid.462414.1
    98 schema:familyName Easwaran
    99 schema:givenName Soumya
    100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0673751775.94
    101 rdf:type schema:Person
    102 sg:pub.10.1007/s100510050292 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004743102
    103 https://doi.org/10.1007/s100510050292
    104 rdf:type schema:CreativeWork
    105 https://doi.org/10.1016/s0378-4371(99)00395-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053710840
    106 rdf:type schema:CreativeWork
    107 https://doi.org/10.1080/096031096333917 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050413569
    108 rdf:type schema:CreativeWork
    109 https://doi.org/10.1103/physreve.83.016101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040217165
    110 rdf:type schema:CreativeWork
    111 https://doi.org/10.1209/0295-5075/77/58004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019815785
    112 rdf:type schema:CreativeWork
    113 https://doi.org/10.2307/2109682 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069765822
    114 rdf:type schema:CreativeWork
    115 https://www.grid.ac/institutes/grid.418391.6 schema:alternateName Birla Institute of Technology and Science
    116 schema:name Birla Institute of Technology and Science, Pilani, 333031, India
    117 rdf:type schema:Organization
    118 https://www.grid.ac/institutes/grid.462414.1 schema:alternateName Institute of Mathematical Sciences
    119 schema:name The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, 600113, India
    120 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...