The Characteristics of Cryptocurrency Market Volatility: Empirical Study For Five Cryptocurrency

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dc.contributor.authorFidan, İlayda İsabetli
dc.contributor.authorGüz, Tuğba
dc.date.accessioned2024-05-19T14:23:34Z
dc.date.available2024-05-19T14:23:34Z
dc.date.issued2022
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions. This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 – 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.en_US
dc.identifier.doi10.17093/alphanumeric.941529
dc.identifier.endpage84en_US
dc.identifier.issn2148-2225
dc.identifier.issue2en_US
dc.identifier.startpage69en_US
dc.identifier.trdizinid1186461en_US
dc.identifier.urihttps://doi.org/10.17093/alphanumeric.941529
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1186461
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4031
dc.identifier.volume10en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofAlphanumeric Journalen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.titleThe Characteristics of Cryptocurrency Market Volatility: Empirical Study For Five Cryptocurrencyen_US
dc.typeArticleen_US

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