Interconnectedness and Volatility Dynamics in Major Cryptocurrency Markets: A Study of LTC-USD, BTC-USD, BNB-USD, and ETH-USD

Authors

DOI:

https://doi.org/10.48001/veethika.1004001

Keywords:

Interconnectedness, volatility, cryptocurrency, spillover, multivariate GARCH

Abstract

This study employs an asymmetric VAR(1)-multivariate GARCH(1,1)-BEKK approach to evaluate the returns, shock, and volatility spillovers for BTC-USD, LTC-USD, BNB-USD, and ETH-USD cryptocurrencies. The findings of the current study reveal significant interconnectedness in between currencies, and notable complementarity observation between BTC and ETH. Additionally, LTC and BNB exhibit an inverse impact on BTC and ETH returns, indicating their increasing popularity among investors. The variance equation analysis demonstrates that past shocks/news significantly affect all cryptocurrencies, with lower cross-news effects compared to internal news impacts. In addition to that,  a significant short-term and long-term volatility spillovers are found among the four major cryptocurrencies market. Further, we find a substantial increase in the conditional volatility of the selected cryptocurrencies from April 2022 to November 2022. This research work emphasizes the significance of interconnectedness, and volatility dynamics of cryptocurrencies in portfolio management. Additionally, it provides suggestions for  policymaking for effective risk management strategies and regulatory  measures in market.

Downloads

Download data is not yet available.

References

Ankenbrand, T., & Bieri, D. (2018). Assessment of cryptocurrencies as an asset class by their characteristics. Investment Management and Financial Innovations, 15(3), 169–181. https://doi.org/10.21511/imfi.15(3).2018.14

Becker, J., Breuker, D., Heide, T., Holler, J., Rauer, H. P., & Böhme, R. (2013). Can we afford integrity by proof-of-work? scenarios inspired by the bitcoin currency. The Economics of Information Security and Privacy, 135–156. https://doi.org/10.1007/978-3-642-39498-0_7

Bezerra, P. C. S., & Albuquerque, P. H. M. (2017). Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels. Computational Management Science, 14(2), 179–196. https://doi.org/10.1007/s10287-016-0267-0

Boubaker, S., Karim, S., Naeem, M. A., & Rahman, M. R. (2024). On the prediction of systemic risk tolerance of cryptocurrencies. Technological Forecasting and Social Change, 198. https://doi.org/10.1016/j.techfore.2023.122963

Bouri, E., Gupta, R., & Vo, X. V. (2022). Jumps in Geopolitical Risk and the Cryptocurrency Market: The Singularity of Bitcoin. Defence and Peace Economics, 33(2), 150–161. https://doi.org/10.1080/10242694.2020.1848285

Catania, L., Grassi, S., & Ravazzolo, F. (2019). Forecasting cryptocurrencies under model and parameter instability. International Journal of Forecasting, 35(2), 485–501. https://doi.org/10.1016/jijforecast.2018.09.005

Charfeddine, L., Benlagha, N., & Maouchi, Y. (2020). Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling, 85, 198–217. https://doi.org/10.1016/j.econmod.2019.05.016

Chen, Y., Yu, L., & Gang, J. (2021). Half-day trading and spillovers. Frontiers of Business Research in China, 15(1). https://doi.org/10.1186/s11782-021-00097-7

Cheraghali, H., Molnár, P., Storsveen, M., & Veliqi, F. (2024). The impact of cryptocurrency-related cyberattacks on return, volatility, and trading volume of cryptocurrencies and traditional financial assets. International Review of Financial Analysis, 95. https://doi.org/10.1016/j.irfa.2024.

103439

Corbet, S., Eraslan, V., Lucey, B. M., & Sensoy, A. (2019). The Effectiveness of Technical Trading Rules in Cryptocurrency Markets. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3454216

Dua, P., & Suri, R. (2019). Interlinkages Between USD–INR, EUR–INR, GBP–INR and JPY–INR Exchange Rate Markets and the Impact of RBI Intervention. Journal of Emerging Market Finance, 18(1suppl), S102–S136. https://doi.org/10.1177/0972652719831562

Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis, 63, 431–437. https://doi.org/10.1016/j.irfa.2018.03.004

Lahmiri, S., & Bekiros, S. (2020). The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets. Chaos, Solitons and Fractals, 138. https://doi.org/10.1016/j.chaos.2020.109936

Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020). A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions. Journal of Information Security and Applications,55. https://doi.org/10.1016/j.jisa.2020.102583

Xiao, H., & Sun, Y. (2020). Forecasting the Returns of Cryptocurrency: A Model Averaging Approach.Journal of Risk and Financial Management, 13(11). https://doi.org/10.3390/jrfm13110278

Published

2024-11-29

How to Cite

Suri, R., & Singh, P. K. (2024). Interconnectedness and Volatility Dynamics in Major Cryptocurrency Markets: A Study of LTC-USD, BTC-USD, BNB-USD, and ETH-USD. VEETHIKA-An International Interdisciplinary Research Journal, 10(4), 1–15. https://doi.org/10.48001/veethika.1004001

Issue

Section

Articles