Purpose: This study investigates volatility and returns spillovers among US technology stocks, decentralized finance (DeFi) tokens and conventional cryptocurrencies, while also examining strategies for optimal portfolio allocation. Design/methodology/approach: The study analyses daily financial market data from October 05, 2020 to February 09, 2024 by employing the Diebold and Yilmaz (2012) and dynamic conditional correlations generalized auto-regressive conditional heteroscedasticity (DCC-GARCH) models. Findings: Empirical findings showed that the US technology stocks were highly interconnected both in returns and volatilities (same as the crypto assets), while technology stock-crypto asset market connections were quite low. Moreover, the technology stocks (crypto assets) were generally net volatility and return receivers (transmitters). Overall, market connectedness was high (65.6% for volatility and 77.2% for return). Portfolio optimization results showed that technology stock-crypto asset (all-DeFi, all-cryptocurrency, all-technology stock and DeFi-cryptocurrency) portfolios were attractive to risk-averse (risk-neutral and risk-seeking) investors. Originality/value: This is the first study to comprehensively analyze volatility and return connectedness and provide insights into portfolio optimization across traditional technology, DeFi and cryptocurrency markets. The insights from this study will aid in risk management, optimal portfolio diversification and formulation of regulations and policies to promote market stability.

Using a time series approach, this study investigates how natural resources impact financial development from a global perspective over the 1980-2019 period. Some important determinants of financial development (economic growth, trade openness, population growth, and investment) have been added to the model as control variables. Unit root tests have revealed that all the variables are integrated of order one. Johansen cointegration test has shown that the variables are in a long-run equilibrium relationship. The vector error correction model (VECM) has estimated the coefficient of the error correction term (ECT) which suggests that the short-run values of natural resources, economic growth, trade openness, population growth, and investment contribute to financial development converging to its long-run equilibrium level by a 23.63% annual speed of adjustment. The estimated coefficients suggest that global natural resource rent has a statistically-significant negative impact on global financial development in the long-run (thereby validating the financial resource curse), but not in the short-run. Causality test results imply that neither global natural resource rent nor global financial development Granger-causes each other.

Purpose: The evolution of financial technology has been rapid, culminating in the mainstream acceptance and adoption of blockchain technology over the past decade. By providing the foundational infrastructure on which smart contracts and decentralized applications can be built and operated, the Ethereum blockchain facilitated the emergence of decentralized finance (DeFi). Not only have DeFi instruments increased portfolio options for investors, but they also have the potential to influence volatility transmissions both in traditional financial markets and within the digital space. To better inform policymaking, risk management and portfolio construction, this study aims to investigate both the time-based and frequency-based volatility connectedness among four leading DeFi instruments and 12 traditional financial markets. Design/methodology/approach: This study analyzes weekly price data ranging from October 05, 2020, to March 04, 2024. The study employs advanced econometric frameworks (Diebold–Yilmaz and Baruník–Krehlík models) to estimate both the time-based and frequency-domain volatility connectedness among the studied financial instruments. Findings: Empirical results show that the DeFi instruments are highly interconnected, the very-large financial markets are highly interconnected and there are low connections between DeFi instruments and traditional financial markets. Moreover, the larger (smaller) stock markets are net volatility transmitters (receivers). Overall, the volatility connectedness among all the studied instruments is moderate (48.4% on average), with the instruments being most (least) connected in the long (short) term. Originality/value: This study expands the literature by including major DeFi assets that have been largely overlooked. Also, the study introduces novelty by incorporating global markets. In fact, to the best of the authors’ knowledge, it is the first study to analyze both time- and frequency-based volatility connectedness among DeFi assets and global financial markets.