Data-driven risk forecasting and algorithmic trading models for cryptocurrencies

Simple item page
dc.contributor.authorSingh, Japjeet
dc.contributor.examiningcommitteeLivi, Lorenzo (Computer Science)en_US
dc.contributor.examiningcommitteeAkcora, Cuneyt (Computer Science and Statistics)en_US
dc.contributor.supervisorThulasiram, Ruppa
dc.contributor.supervisorThavaneswaran, Aerambamoorthy
dc.date.accessioned2022-12-01T16:01:37Z
dc.date.available2022-12-01T16:01:37Z
dc.date.copyright2022-11-30
dc.date.issued2022-11-23
dc.date.submitted2022-12-01T02:03:05Zen_US
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractRisk modelling is an active area of research in computational finance, which involves design of models for quantifying and forecasting the uncertainty associated with investments. Unlike traditional financial assets, the risk modelling for blockchain powered digital assets is rarely studied. The main contributions of this thesis lie in design and application of the novel data-driven models to risk forecasting of the price and algorithmic returns of cryptocurrency assets. In our first study, we proposed data-driven and neuro-volatility risk forecasting models for cryptocurrencies. Our results are quantified with two widely used risk metrics - Value at Risk (VaR) and Expected Shortfall (ES). Both the data-driven and neuro-volatility estimates demonstrated a significantly higher risk for cryptocurrencies as compared to traditional technology stocks. We observed that the data-driven models produced better forecasts for cryptocurrencies, while better forecasts resulted for the regular stocks and indexes with the neuro-volatility model. Also, the data-driven models are more efficient in terms of computation time. We did another study with the algorithmic returns for simple moving average crossover strategy using Sharpe ratio as risk quantifier, which we extended to develop a novel algorithmic trading strategy for cryptocurrencies. As a case study, we applied this knowledge to decentralized finance (DeFi) ecosystem. The main findings from the studies indicate superiority of the data-driven approach proposed in this research for the risk forecasting problems. The data-driven approach performed equivalently well or better compared to neuro volatility models in different studies reported in this thesis and also that the data-driven approach was significantly efficient in terms of computation time in each of these studies.en_US
dc.description.noteFebruary 2023en_US
dc.description.sponsorshipDr. Ruppa Thulasiram (My supervisor) MITACS (Through Accelerate research internship program)en_US
dc.identifier.urihttp://hdl.handle.net/1993/36988
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectCryptocurrencyen_US
dc.subjectRisk forecastingen_US
dc.subjectVolatility forecastingen_US
dc.subjectData-driven modelsen_US
dc.subjectNeuro volatility modelsen_US
dc.subjectFuzzy intervalsen_US
dc.subjectFuzzy alpha cutsen_US
dc.subjectValue at risk (VaR)en_US
dc.subjectExpected shortfall (ES)en_US
dc.subjectAlgorithmic tradingen_US
dc.subjectSharpe ratioen_US
dc.subjectLong short-term memory (LSTM)en_US
dc.subjectAlgo returnsen_US
dc.subjectDecentralized Finance (DeFi)en_US
dc.subjectDecentralized Exchange (DeX)en_US
dc.subjectLiquidity poolsen_US
dc.subjectUniswap protocolen_US
dc.titleData-driven risk forecasting and algorithmic trading models for cryptocurrenciesen_US
dc.typemaster thesisen_US
local.subject.manitobanoen_US
oaire.awardTitleGETS Awarden_US
project.funder.identifierhttps://doi.org/10.13039/100010318en_US
project.funder.nameUniversity of Manitobaen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Singh_Japjeet.pdf
Size:
13.5 MB
Format:
Adobe Portable Document Format
Description:
Thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.2 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
FGS - Electronic Theses and Practica