Abstract
This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.
Highlights
In recent decades, cryptocurrencies have witnessed spectacular development
In early 2017, the following group set out to objectively measure the overall growth and movement in the blockchain sector: Igor Rivin and Carlo Scevola; CS&P presidents and economists; as well as engineer Robert Davis. They designed the Crypto Currency Index 30 (CCI30) to track the top 30 cryptocurrencies by market capitalization, excluding stablecoins, and suggested that the CCI30 could serve as an investment tool for passive investors and investment managers
Let Rit be the returns in cryptocurrency i (i = 1, · · ·, 10), Rmt be the returns in the CCI30 index, and Rft be the risk-free rate at time t (t = 1, · · ·, T )
Summary
Cryptocurrencies have witnessed spectacular development. They are growing rapidly and are used for many different applications in the economy due to their ability to facilitate electronic payments between individuals without the involvement of a (trusted) third party. In early 2017, the following group set out to objectively measure the overall growth and movement in the blockchain sector: Igor Rivin and Carlo Scevola (the team leaders of a group of mathematicians, fund managers, and quants); CS&P presidents and economists; as well as engineer Robert Davis. They designed the Crypto Currency Index 30 (CCI30) to track the top 30 cryptocurrencies by market capitalization, excluding stablecoins (www.cci30.com), and suggested that the CCI30 could serve as an investment tool for passive investors and investment managers. This research explores the stochastic behavior of the CCI30 index, especially for the purpose of helping crypto market policymakers and investors interested in portfolio diversification
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