Cryptocurrency has become an important topic in the financial industry, which seeks to determine its impact in current transaction spaces. This study is focused on the analysis of the cryptocurrency market taking into consideration cryptocurrencies of the Bitcoin family (BTC, BSV), Ethereum family (ETC, ETH), Dogecoin, and Litecoin. The objective of this study is to better understand the distributed ledger and how it works in real or simulated environments. In particular, this study targets a natural question that regards whether cryptocurrency time series exhibit similar behavior to other assets with an emphasis on technical and fundamental analysis applied to cryptocurrencies. Cryptocurrency prices are purely driven by the demand-supply model and are characterized by very high volatility and the absence of any regulatory authority. Therefore, forecasting cryptocurrency prices represents a challenge. Though, this study aims to forecast future returns using time series models such as ARIMA and GARCH. The data considered are historical as well as real-time prices, volumes and flows, retrieved from public domain platforms and analyzed through time series models along with other fundamental and technical approaches and indicators using RStudio and Python.

Cryptocurrency

Busato, Beatrice
2022/2023

Abstract

Cryptocurrency has become an important topic in the financial industry, which seeks to determine its impact in current transaction spaces. This study is focused on the analysis of the cryptocurrency market taking into consideration cryptocurrencies of the Bitcoin family (BTC, BSV), Ethereum family (ETC, ETH), Dogecoin, and Litecoin. The objective of this study is to better understand the distributed ledger and how it works in real or simulated environments. In particular, this study targets a natural question that regards whether cryptocurrency time series exhibit similar behavior to other assets with an emphasis on technical and fundamental analysis applied to cryptocurrencies. Cryptocurrency prices are purely driven by the demand-supply model and are characterized by very high volatility and the absence of any regulatory authority. Therefore, forecasting cryptocurrency prices represents a challenge. Though, this study aims to forecast future returns using time series models such as ARIMA and GARCH. The data considered are historical as well as real-time prices, volumes and flows, retrieved from public domain platforms and analyzed through time series models along with other fundamental and technical approaches and indicators using RStudio and Python.
2022-07-19
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14247/6013