Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Volatility Evaluation Using Conditional Heteroscedasticity Models on Bitcoin, Ethereum and Ripple
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utvärdering av volatilitet via betingade heteroskedastiska modeller på Bitcoin, Ethereum och Ripple (Swedish)
Abstract [en]

This study examines and compares the volatility in sample fit and out of sample forecast of four different heteroscedasticity models, namely ARCH, GARCH, EGARCH and GJR-GARCH applied to Bitcoin, Ethereum and Ripple. The models are fitted over the period from 2016-01-01 to 2019-01-01 and then used to obtain one day rolling forecasts during the period from 2018-01-01 to 2019-01-01. The study investigates three different themes consisting of the modelling framework structure, complexity of models and the relation between a good in sample fit and good out of sample forecast. AIC and BIC are used to evaluate the in sample fit while MSE, MAE and R2LOG are used as loss functions when evaluating the out of sample forecast against the chosen Parkinson volatility proxy. The results show that a heavier tailed reference distribution than the normal distribution generally improves the in sample fit, while this generality is not found for the out of sample forecast. Furthermore, it is shown that GARCH type models clearly outperform ARCH models in both in sample fit and out of sample forecast. For Ethereum, it is shown that the best fitted models also result in the best out of sample forecast for all loss functions, while for Bitcoin non of the best fitted models result in the best out of sample forecast. Finally, for Ripple, no generality between in sample fit and out of sample forecast is found.

Abstract [sv]

Den här rapporten undersöker om bättre anpassade volatilitetsmodeller leder till bättre prognoser av volatiliteten för olika heteroskedastiska modeller, i detta fall ARCH, GARCH, EGARCH och GJR-GARCH, med olika innovationsdistributioner. Modellerna anpassas för Bitcoin, Ethereum och Ripple under 2016-01-01 till 2017-01-01 och därefter görs endagsprognoser under perioden 2018-01-01 till 2018-12-31. Studien undersöker tre olika teman bestående av modellstruktur, komplexitet av modeller och relationen mellan en god passning och god prognos. För att evaluera passningen för modellerna används AIC och BIC och för prognoserna används förlustfunktionerna MSE, MAE och R2log som evaluering av prognosen mot den valda volatilitetsproxyn Parkinson. Resultaten visar på att innovationsdistributioner med tyngre svansar än normalfördelningen generellt leder till bättre passning, medan man för prognoserna inte kan dra en sådan slutsats. Vidare visas det att GARCH-modellerna påvisade bättre resultat både för passning och prognoser än dem mer simpla ARCH-modellerna. För Ethereum var samma modell bäst för samtliga förlustfunktioner medan Bitcoin visar olika modeller för respektive förlustfunktion. För Ripple kan inte heller någon generalitet påvisas mellan passning och prognoser.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:099
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252570OAI: oai:DiVA.org:kth-252570DiVA, id: diva2:1319675
Subject / course
Financial Mathematics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-03 Last updated: 2019-06-04Bibliographically approved

Open Access in DiVA

fulltext(1310 kB)43 downloads
File information
File name FULLTEXT02.pdfFile size 1310 kBChecksum SHA-512
d42eaba3bceaec8d2ed72722127aa796ca20480b473e31d5f2d1f2480a9f1d92922c3567f6ebcca46fa4f33a060bf497a43790801c14f4d4165cc6a060f19aea
Type fulltextMimetype application/pdf

By organisation
Mathematical Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 43 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 95 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf