Search Results - subject:"Litecoin."

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Sebastião, Helder Miguel Correia Virtuoso; Godinho, Pedro - In: Blockchain, crypto assets, and financial innovation : a …, (pp. 252-294). 2025

Persistent link: https://www.econbiz.de/10015433280

Unsupervised machine learning based anomaly detection in high frequency data: Evidence from Cryptocurrency Market

Latif, Muhammad Nouman; Kaplan, Muhittin; Khan, Asad ul … - In: Pakistan Journal of Commerce and Social Sciences (PJCSS) 19 (2025) 3, pp. 407-440

The rapid integration of cryptocurrencies into the global financial ecosystem has introduced unprecedented challenges in market surveillance, risk management, and anomaly detection. While conventional statistical models such as ARIMA (Autoregressive Integrated Moving Average) and GARCH...

Persistent link: https://www.econbiz.de/10015482432

Unsupervised machine learning based anomaly detection in high frequency data : Evidence from Cryptocurrency Market

Latif, Muhammad Nouman; Kaplan, Muhittin; Khan, Asad ul … - In: Pakistan journal of commerce and social sciences 19 (2025) 3, pp. 407-440

Persistent link: https://www.econbiz.de/10015475249

Unveiling interconnectedness and volatility transmission : a novel GARCH analysis of leading global cryptocurrencies

Vigg Kushwah, Silky; Hundal, Shab; Goel, Payal - In: International journal of economics and financial issues … 14 (2024) 3, pp. 132-139

Persistent link: https://www.econbiz.de/10014631784

Modelling the dynamics of cryptocurrency prices for risk hedging: The case of Bitcoin, Ethereum, and Litecoin

Madichie, Chekwube V.; Ngwu, Franklin N.; Eze, Eze A.; … - In: Cogent Economics & Finance 11 (2023) 1, pp. 1-23

Bitcoin, Ethereum, and Litecoin along the time and frequency dimensions of monthly data from 1 March 2016 to 05/31/2022. Based … on the ARDL model, results show that the volume of transactions of Bitcoin, Ethereum, and Litecoin, oil prices, and gold …, results from the Wavelet Granger causality tests show no causality between the raw series of Bitcoin, Ethereum, and Litecoin …

Persistent link: https://www.econbiz.de/10015074718

Literacy, profile, and determinants of Bitcoin, Ethereum, and Litecoin : survey results

Khan, Mohammad Tariqul Islam - In: Journal of education for business 98 (2023) 7, pp. 367-377

Persistent link: https://www.econbiz.de/10014390590

Modelling the dynamics of cryptocurrency prices for risk hedging : the case of Bitcoin, Ethereum, and Litecoin

Madichie, Chekwube V.; Ngwu, Franklin N.; Eze, Eze A.; … - In: Cogent economics & finance 11 (2023) 1, pp. 1-23

Persistent link: https://www.econbiz.de/10014500765

Exploring the dependencies among main cryptocurrency log-returns : a hidden Markov model

Pennoni, Fulvia; Bartolucci, Francesco; Forte, Gianfranco; … - In: Economic notes 51 (2022) 1, pp. 1-16

Persistent link: https://www.econbiz.de/10012795398

Cryptocurrencies intraday high-frequency volatility spillover effects using univariate and multivariate GARCH models

Ampountolas, Apostolos - In: International Journal of Financial Studies : open … 10 (2022) 3, pp. 1-22

volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling volatility to …

Persistent link: https://www.econbiz.de/10013368338

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Sebastião, Helder; Godinho, Pedro - In: Financial Innovation 7 (2021) 1, pp. 1-30

This study examines the predictability of three major cryptocurrencies-bitcoin, ethereum, and litecoin-and the … litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading …

Persistent link: https://www.econbiz.de/10012602886