Modelling the Volatility of NFTs and Traditional Financial Assets using MGARCH Family Models

This paper examines the efficiency and asymmetric multiracial features of NFTs (Mana, Tezos), and traditional assets (EGX30, Oil index) using Asymmetric Multiracial Cross-Correlations Analysis covering the period from January 2020 to May 2021. Considering the full sample with a significant variation among asset classes. (Oil-Tezos)and (Mana-Tezos) is the most efficient.

Since their inception, the blockchain-based digital asset classes have received immense interest from investors and portfolio managers as an alternative investment platform. Along with other established traditional cryptocurrencies such as Bitcoin, Litecoin, Ripple, and Ethereum, new blockchain asset classes such as Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) have made a considerable contribution tothe asset market’s recent expansion (Aharon & Demir, 2021; Alam, Chowdhury, Abdullah, & Masih, 2023; Maouchi, Charfeddine, & el Montasser, 2021; Yousaf & Yarovaya, 2022).Fundamentally, NFTs and DeFi differ from traditional cryptocurrencies as they are not virtual currency. Where NFTs are non-transferable cryptographic digital assetscreated by Ethereum smart contracts and can be sold and traded, the interchangeability of NFTs when comparing the other cryptocurrencies is very low (Karim, Lucey, Naeem, & Uddin, 2022; Q. Wang, Li, Wang, & Chen, 2021; Y. Wang, 2022).The NFTs and DeFi are relatively contemporary and unexplored asset classes, but their market capitalization has grown substantially as risk minimizing assets, particularly during the COVID-19 period. In the NFT space, the

Keywords

Volatility, NFTs, Traditional Financial Assets, and MGARCH