Modeling High-Frequency Non-Homogeneous Order Flows by Compound Cox Processes* View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-04

AUTHORS

A. V. Chertok, V. Yu. Korolev, A. Yu. Korchagin

ABSTRACT

A micro-scale model is proposed for the evolution of a limit order book in modern high-frequency trading applications. Within this model, order flows are described by doubly stochastic Poisson processes (also called Cox processes) taking account of the stochastic character of the intensities of order flows. The models for the number of order imbalance (NOI) process and order flow imbalance (OFI) process are introduced as two-sided risk processes that are special compound Cox processes. These processes are sensitive indicators of the current state of the limit order book since time intervals between events in a limit order book are usually so short that price changes are relatively infrequent events. Therefore price changes provide a very coarse and limited description of market dynamics at time micro-scales. NOI and OFI processes track best bid and ask queues and change much faster than prices. They incorporate information about build-ups and depletions of order queues and they can be used to interpolate market dynamics between price changes and to track the toxicity of order flows. The proposed multiplicative model of stochastic intensities makes it possible to analyze the characteristics of the order flows as well as the instantaneous proportion of the forces of buyers and sellers without modeling the external information background. The proposed model gives the opportunity to link the micro-scale high-frequency dynamics of the limit order book with the macroscale models of stock price processes of the form of subordinated Wiener processes by means of limit theorems for special random sums and hence, to give a deeper insight in the nature of popular models of statistical regularities of the evolution of characteristics of financial markets such as generalized hyperbolic distributions and other normal variance-mean mixtures. More... »

PAGES

44-68

Journal

TITLE

Journal of Mathematical Sciences

ISSUE

1

VOLUME

214

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10958-016-2757-6

DOI

http://dx.doi.org/10.1007/s10958-016-2757-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009223472


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Moscow State University", 
          "id": "https://www.grid.ac/institutes/grid.14476.30", 
          "name": [
            "Lomonosov Moscow State University; Euphoria Group LLC, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chertok", 
        "givenName": "A. V.", 
        "id": "sg:person.010123322353.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010123322353.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moscow State University", 
          "id": "https://www.grid.ac/institutes/grid.14476.30", 
          "name": [
            "Lomonosov Moscow State University; Institute of Informatics Problems of RAS, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Korolev", 
        "givenName": "V. Yu.", 
        "id": "sg:person.014166423003.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014166423003.73"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moscow State University", 
          "id": "https://www.grid.ac/institutes/grid.14476.30", 
          "name": [
            "Lomonosov Moscow State University, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Korchagin", 
        "givenName": "A. Yu.", 
        "id": "sg:person.010170417462.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010170417462.26"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1080/14697680701381228", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000748875"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/03461238.2010.485370", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007617982"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1469-7688/2/5/308", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010712276"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1140/epjb/e2004-00328-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013791971", 
          "https://doi.org/10.1140/epjb/e2004-00328-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-88-470-1766-5_7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019040149", 
          "https://doi.org/10.1007/978-88-470-1766-5_7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1469-7688/2/4/301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023486458"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1515/9783110936018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029068134"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/14697688.2012.664926", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031220463"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1386-4181(98)00012-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043604504"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bfb0077758", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048309083", 
          "https://doi.org/10.1007/bfb0077758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bfb0077758", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048309083", 
          "https://doi.org/10.1007/bfb0077758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s007800050032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048954212", 
          "https://doi.org/10.1007/s007800050032"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1540-6261.2005.00795.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049641652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1540-6261.2005.00795.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049641652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1023/a:1009703431535", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056304295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/209749", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058529302"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/296519", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058606345"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/58.1.83", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059418003"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jjfinec/nbt003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059803463"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/rfs/11.4.789", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060005247"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/rfs/hhp011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060006092"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/opre.1090.0780", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064726242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1402598", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069473070"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3318481", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070261642"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4213/tvp2080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072376332"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4213/tvp2609", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072376480"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4213/tvp335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072376699"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/3907", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098866935"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-04", 
    "datePublishedReg": "2016-04-01", 
    "description": "A micro-scale model is proposed for the evolution of a limit order book in modern high-frequency trading applications. Within this model, order flows are described by doubly stochastic Poisson processes (also called Cox processes) taking account of the stochastic character of the intensities of order flows. The models for the number of order imbalance (NOI) process and order flow imbalance (OFI) process are introduced as two-sided risk processes that are special compound Cox processes. These processes are sensitive indicators of the current state of the limit order book since time intervals between events in a limit order book are usually so short that price changes are relatively infrequent events. Therefore price changes provide a very coarse and limited description of market dynamics at time micro-scales. NOI and OFI processes track best bid and ask queues and change much faster than prices. They incorporate information about build-ups and depletions of order queues and they can be used to interpolate market dynamics between price changes and to track the toxicity of order flows. The proposed multiplicative model of stochastic intensities makes it possible to analyze the characteristics of the order flows as well as the instantaneous proportion of the forces of buyers and sellers without modeling the external information background. The proposed model gives the opportunity to link the micro-scale high-frequency dynamics of the limit order book with the macroscale models of stock price processes of the form of subordinated Wiener processes by means of limit theorems for special random sums and hence, to give a deeper insight in the nature of popular models of statistical regularities of the evolution of characteristics of financial markets such as generalized hyperbolic distributions and other normal variance-mean mixtures.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10958-016-2757-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136516", 
        "issn": [
          "1072-3374", 
          "1573-8795"
        ], 
        "name": "Journal of Mathematical Sciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "214"
      }
    ], 
    "name": "Modeling High-Frequency Non-Homogeneous Order Flows by Compound Cox Processes*", 
    "pagination": "44-68", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2bf0306dfa487ff0851c78c0d7304cb843e254c191a3e82d00993a17f881f364"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10958-016-2757-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1009223472"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10958-016-2757-6", 
      "https://app.dimensions.ai/details/publication/pub.1009223472"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:00", 
    "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_8663_00000510.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs10958-016-2757-6"
  }
]
 

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/s10958-016-2757-6'

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/s10958-016-2757-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10958-016-2757-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10958-016-2757-6'


 

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

159 TRIPLES      21 PREDICATES      53 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10958-016-2757-6 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N66d45e2dd56d4142a5a6cd84074b6c07
4 schema:citation sg:pub.10.1007/978-88-470-1766-5_7
5 sg:pub.10.1007/bfb0077758
6 sg:pub.10.1007/s007800050032
7 sg:pub.10.1140/epjb/e2004-00328-1
8 https://doi.org/10.1016/s1386-4181(98)00012-3
9 https://doi.org/10.1023/a:1009703431535
10 https://doi.org/10.1080/03461238.2010.485370
11 https://doi.org/10.1080/14697680701381228
12 https://doi.org/10.1080/14697688.2012.664926
13 https://doi.org/10.1086/209749
14 https://doi.org/10.1086/296519
15 https://doi.org/10.1088/1469-7688/2/4/301
16 https://doi.org/10.1088/1469-7688/2/5/308
17 https://doi.org/10.1093/biomet/58.1.83
18 https://doi.org/10.1093/jjfinec/nbt003
19 https://doi.org/10.1093/rfs/11.4.789
20 https://doi.org/10.1093/rfs/hhp011
21 https://doi.org/10.1111/j.1540-6261.2005.00795.x
22 https://doi.org/10.1142/3907
23 https://doi.org/10.1287/opre.1090.0780
24 https://doi.org/10.1515/9783110936018
25 https://doi.org/10.2307/1402598
26 https://doi.org/10.2307/3318481
27 https://doi.org/10.4213/tvp2080
28 https://doi.org/10.4213/tvp2609
29 https://doi.org/10.4213/tvp335
30 schema:datePublished 2016-04
31 schema:datePublishedReg 2016-04-01
32 schema:description A micro-scale model is proposed for the evolution of a limit order book in modern high-frequency trading applications. Within this model, order flows are described by doubly stochastic Poisson processes (also called Cox processes) taking account of the stochastic character of the intensities of order flows. The models for the number of order imbalance (NOI) process and order flow imbalance (OFI) process are introduced as two-sided risk processes that are special compound Cox processes. These processes are sensitive indicators of the current state of the limit order book since time intervals between events in a limit order book are usually so short that price changes are relatively infrequent events. Therefore price changes provide a very coarse and limited description of market dynamics at time micro-scales. NOI and OFI processes track best bid and ask queues and change much faster than prices. They incorporate information about build-ups and depletions of order queues and they can be used to interpolate market dynamics between price changes and to track the toxicity of order flows. The proposed multiplicative model of stochastic intensities makes it possible to analyze the characteristics of the order flows as well as the instantaneous proportion of the forces of buyers and sellers without modeling the external information background. The proposed model gives the opportunity to link the micro-scale high-frequency dynamics of the limit order book with the macroscale models of stock price processes of the form of subordinated Wiener processes by means of limit theorems for special random sums and hence, to give a deeper insight in the nature of popular models of statistical regularities of the evolution of characteristics of financial markets such as generalized hyperbolic distributions and other normal variance-mean mixtures.
33 schema:genre research_article
34 schema:inLanguage en
35 schema:isAccessibleForFree false
36 schema:isPartOf N1c42006cfae9443eb919d67e3f8b8774
37 Nced38a3606e640b488e2c36a7d678b8f
38 sg:journal.1136516
39 schema:name Modeling High-Frequency Non-Homogeneous Order Flows by Compound Cox Processes*
40 schema:pagination 44-68
41 schema:productId N0b927edf193a4704b369f4521531a25b
42 N6a0506f07b1844ccb300a51d685f5eb1
43 Nb7c8069ae91a46b2b359aa770f37dd3f
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009223472
45 https://doi.org/10.1007/s10958-016-2757-6
46 schema:sdDatePublished 2019-04-10T15:00
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher N5cde40a851af4f83923eeb724ab445a2
49 schema:url http://link.springer.com/10.1007%2Fs10958-016-2757-6
50 sgo:license sg:explorer/license/
51 sgo:sdDataset articles
52 rdf:type schema:ScholarlyArticle
53 N0b927edf193a4704b369f4521531a25b schema:name readcube_id
54 schema:value 2bf0306dfa487ff0851c78c0d7304cb843e254c191a3e82d00993a17f881f364
55 rdf:type schema:PropertyValue
56 N1bf9f88a4a76475d8f468e17fb3a4692 rdf:first sg:person.014166423003.73
57 rdf:rest Nce87ceae7a314b04b328741372ce5bd1
58 N1c42006cfae9443eb919d67e3f8b8774 schema:issueNumber 1
59 rdf:type schema:PublicationIssue
60 N5cde40a851af4f83923eeb724ab445a2 schema:name Springer Nature - SN SciGraph project
61 rdf:type schema:Organization
62 N66d45e2dd56d4142a5a6cd84074b6c07 rdf:first sg:person.010123322353.25
63 rdf:rest N1bf9f88a4a76475d8f468e17fb3a4692
64 N6a0506f07b1844ccb300a51d685f5eb1 schema:name doi
65 schema:value 10.1007/s10958-016-2757-6
66 rdf:type schema:PropertyValue
67 Nb7c8069ae91a46b2b359aa770f37dd3f schema:name dimensions_id
68 schema:value pub.1009223472
69 rdf:type schema:PropertyValue
70 Nce87ceae7a314b04b328741372ce5bd1 rdf:first sg:person.010170417462.26
71 rdf:rest rdf:nil
72 Nced38a3606e640b488e2c36a7d678b8f schema:volumeNumber 214
73 rdf:type schema:PublicationVolume
74 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
75 schema:name Mathematical Sciences
76 rdf:type schema:DefinedTerm
77 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
78 schema:name Statistics
79 rdf:type schema:DefinedTerm
80 sg:journal.1136516 schema:issn 1072-3374
81 1573-8795
82 schema:name Journal of Mathematical Sciences
83 rdf:type schema:Periodical
84 sg:person.010123322353.25 schema:affiliation https://www.grid.ac/institutes/grid.14476.30
85 schema:familyName Chertok
86 schema:givenName A. V.
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010123322353.25
88 rdf:type schema:Person
89 sg:person.010170417462.26 schema:affiliation https://www.grid.ac/institutes/grid.14476.30
90 schema:familyName Korchagin
91 schema:givenName A. Yu.
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010170417462.26
93 rdf:type schema:Person
94 sg:person.014166423003.73 schema:affiliation https://www.grid.ac/institutes/grid.14476.30
95 schema:familyName Korolev
96 schema:givenName V. Yu.
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014166423003.73
98 rdf:type schema:Person
99 sg:pub.10.1007/978-88-470-1766-5_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019040149
100 https://doi.org/10.1007/978-88-470-1766-5_7
101 rdf:type schema:CreativeWork
102 sg:pub.10.1007/bfb0077758 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048309083
103 https://doi.org/10.1007/bfb0077758
104 rdf:type schema:CreativeWork
105 sg:pub.10.1007/s007800050032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048954212
106 https://doi.org/10.1007/s007800050032
107 rdf:type schema:CreativeWork
108 sg:pub.10.1140/epjb/e2004-00328-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013791971
109 https://doi.org/10.1140/epjb/e2004-00328-1
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/s1386-4181(98)00012-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043604504
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1023/a:1009703431535 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056304295
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1080/03461238.2010.485370 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007617982
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1080/14697680701381228 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000748875
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1080/14697688.2012.664926 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031220463
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1086/209749 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058529302
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1086/296519 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058606345
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1088/1469-7688/2/4/301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023486458
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1088/1469-7688/2/5/308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010712276
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1093/biomet/58.1.83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059418003
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1093/jjfinec/nbt003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059803463
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1093/rfs/11.4.789 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060005247
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1093/rfs/hhp011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060006092
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1111/j.1540-6261.2005.00795.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1049641652
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1142/3907 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098866935
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1287/opre.1090.0780 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064726242
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1515/9783110936018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029068134
144 rdf:type schema:CreativeWork
145 https://doi.org/10.2307/1402598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069473070
146 rdf:type schema:CreativeWork
147 https://doi.org/10.2307/3318481 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070261642
148 rdf:type schema:CreativeWork
149 https://doi.org/10.4213/tvp2080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072376332
150 rdf:type schema:CreativeWork
151 https://doi.org/10.4213/tvp2609 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072376480
152 rdf:type schema:CreativeWork
153 https://doi.org/10.4213/tvp335 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072376699
154 rdf:type schema:CreativeWork
155 https://www.grid.ac/institutes/grid.14476.30 schema:alternateName Moscow State University
156 schema:name Lomonosov Moscow State University, Moscow, Russia
157 Lomonosov Moscow State University; Euphoria Group LLC, Moscow, Russia
158 Lomonosov Moscow State University; Institute of Informatics Problems of RAS, Moscow, Russia
159 rdf:type schema:Organization
 




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


...