This study aims to examine the static and dynamic return and volatility spillovers between four main segments of nonfungible tokens (NFTs) (XTZ-Tezos, ENJ-EnjinCoin, MANA-Decentraland and THETA-Theta) and other assets [S&P Green Bond Index (GB), S&P Global Clean Energy Index (CEI), WTI-West Texas Intermediate-crude oil and Krane Shares Global Carbon Strategy ETF price (Krbn)].
The time-varying parameter vector autoregression model is used to capture the time-varying relationships within these markets; the BEKK-GARCH model is used to calculate the hedge ratios, optimal weights and hedging effectiveness for portfolios pairing segments CEI/NFTs, Krbn/NFTs, GB/NFTs and WTI/NFTs. For robustness checks, the authors use Long Short-Term Memory (LSTM) networks to forecast the performance of both hedged and unhedged portfolios.
This static study reveals weak total connectedness among the markets. CEI is identified as a primary shock transmitter of return in the system, whereas ENJ, XTZ and THETA are receivers of volatility spillovers. According to the results of optimal weights, hedge ratios and hedging effectiveness, portfolio managers should add NFTs to portfolios to reap the diversification benefits; LSTM confirms the results.
This study is innovative in that it combines Krbn, GB, CEI, WTI and NFTs into a single, thorough analysis. However, the effects of these assets individually with NFTs have been examined in earlier studies. This study offers fresh perspectives on how NFTs might improve the financial portfolios’ effectiveness. It offers useful implications for investors and policymakers looking to advance sustainable financial practices.
