A comparison of value at risk & expected shortfall models in cryptocurrencies

Simple item page
dc.contributor.advisorPerrang, Justinen_ZA
dc.contributor.authorBosman, Lisa-Marieen_ZA
dc.contributor.otherStellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.en_ZA
dc.date.accessioned2023-03-05T11:14:54Z
dc.date.accessioned2023-05-18T07:06:52Z
dc.date.available2023-03-05T11:14:54Z
dc.date.available2023-05-18T07:06:52Z
dc.date.issued2023-03
dc.descriptionThesis (MCom)--Stellenbosch University, 2023.en_ZA
dc.description.abstractENGLISH SUMMARY: The key objective of this study is to examine the application of specific traditional market risk management measures on the cryptocurrency market and investigate the efficiency and accuracy thereof through the application of value at risk (VaR) and expected shortfall (ES) models. The further objective is to provide an extensive literature review of important topics relating to cryptocurrencies and the risk management thereof. Numerous studies and applications related to cryptocurrencies have already been conducted. This study clarifies certain aspects and factors regarding the cryptocurrency market, such as blockchains, cryptocurrency bubbles and the impending regulation of the asset class. As the volatile cryptocurrency market has become more prominent in the financial sector throughout the years, the modelling and management of market risk have become key areas related to the asset class. VaR and ES are well-known measures of market risk. These are implemented in this study on the daily return data for Bitcoin, Ethereum, Ripple and Dogecoin for the period 8 August 2015 — 31 August 2022. The historical simulation model, as well as independent and identically distributed (i.i.d.) models, assuming both normally and Student t distributed returns, are applied. The exponentially weighted moving average (EWMA) model provides an improvement upon the i.i.d. models. Following this, the asymmetric volatility of the data is taken into account with an adjusted EWMA model, the asymmetric exponentially weighted moving average (AEWMA). The final model applied is the filtered historical simulation (FHS), which combines the benefits of the historical simulation and AEWMA models using bootstrapping. The daily VaR and ES forecasts are extensively backtested to enable comparison among the models. The results produced differ among the different coins and for the different significant levels. However, it is clear that the asymmetric volatility of the data significantly impacts the results and must be accounted for in modelling. It can be concluded that traditional market risk management has an important place in the cryptocurrency market.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Die sleuteldoelwit van hierdie studie is om die toepassing van spesifieke tradisionele markrisikobestuursmaatreels op die kripto-geldeenheidmark te ondersoek, en die doeltreffendheid en akkuraatheid daarvan te toets deur die toepassing van waarde-op-risiko (VaR) en verwagte tekort (ES) modelle. Die verdere doelwit is om ’n uitgebreide literatuuroorsig te verskaf van belangrike onderwerpe wat verband hou met kripto-geldeenhede en die risikobestuur daarvan. Talle studies en toepassings wat met kripto-geldeenhede verband hou, is reeds uitgevoer. Hierdie studie verduidelik sekere aspekte en faktore aangaande die kripto-geldeenheidmark, soos blokkettings, kripto-geldeenheidborrels en die naderende regulering van die bateklas. Aangesien die volatiele kripto-geldeenheidmark deur die jare meer prominent in die finansiele sektor geword het, het die modellering en bestuur van markrisiko sleutelareas in die bateklas geword. VaR en ES is bekende maatstawwe van markrisiko. Dit word in hierdie studie geimplementeer op die daaglikse opbrengsdata vir Bitcoin, Ethereum, Ripple en Dogecoin vir die tydperk 8 Augustus 2015 — 31 Augustus 2022. Die historiese simulasiemodel, asook die onafhanklike identies-verdeelde (i.i.d.) modelle, wat beide normaal en Studente t-verdeelde opbrengste aanneem, word toegepas. Die eksponensieel geweegde bewegende gemiddelde (EWMA) model bied ’n verbetering op die i.i.d. modelle. Hierna word die asimmetriese volatiliteit van die data in ag geneem met ’n aangepaste EWMA-model, die asimmetriese eksponensieel geweegde bewegende gemiddelde (AEWMA). Die finale model wat toegepas word, is die gefiltreerde historiese simulasie (FHS), wat die voordele van die historiese simulasie en AEWMA-modelle kombineer deur gebruik te maak van skoenlusberaming. Die daaglikse VaR- en ES-voorspellings word omvattend getoets om vergelyking tussen die modelle moontlik te maak. Die resultate wat geproduseer word verskil tussen die verskillende munte en vir die verskillende betekenispeile. Dit is egter duidelik dat die asimmetriese volatiliteit van die data ’n beduidende impak het op die resultate en in die modellering verreken moet word. Daar word tot die gevolgtrekking gekom dat tradisionele markrisikobestuur ’n belangrike plek in die kripto-geldeenheidmark het.af_ZA
dc.description.versionMasters
dc.format.extentxiii, 98 pages : illustrations, includes annexures
dc.identifier.urihttp://hdl.handle.net/10019.1/127149
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.rights.holderStellenbosch University
dc.subject.lcshSecuritiesen_ZA
dc.subject.lcshFinancial risk managementen_ZA
dc.subject.lcshCryptocurrenciesen_ZA
dc.subject.nameUCTD
dc.titleA comparison of value at risk & expected shortfall models in cryptocurrenciesen_ZA
dc.typeThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
bosman_comparison_2023.pdf
Size:
2.43 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters Degrees (Statistics and Actuarial Science)