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Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

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  • Serdar Neslihanoglu

    (University of Minho
    Eskisehir Osmangazi University)

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.

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  • Serdar Neslihanoglu, 2021. "Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
  • Handle: RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00247-z
    DOI: 10.1186/s40854-021-00247-z
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    Cited by:

    1. Foroutan, Parisa & Lahmiri, Salim, 2022. "The effect of COVID-19 pandemic on return-volume and return-volatility relationships in cryptocurrency markets," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    2. Bartłomiej Lisicki, 2023. "Sektorowe zróżnicowanie efektu interwału akcji spółek z GPW w dobie pandemii COVID-19," Ekonomista, Polskie Towarzystwo Ekonomiczne, issue 2, pages 174-194.
    3. Yu Song & Bo Chen & Xin-Yi Wang, 2023. "Cryptocurrency technology revolution: are Bitcoin prices and terrorist attacks related?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-20, December.
    4. Francisco Javier García-Corral & José Antonio Cordero-García & Jaime de Pablo-Valenciano & Juan Uribe-Toril, 2022. "A bibliometric review of cryptocurrencies: how have they grown?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    5. Ngo Thai Hung, 2022. "The COVID-19 effects on cryptocurrency markets: robust evidence from time-frequency analysis," Economics Bulletin, AccessEcon, vol. 42(1), pages 109-123.

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    More about this item

    Keywords

    CAPM; COVID-19; Crypto Currency Index 30; Generalized additive model; Kalman filter;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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