This paper investigates the impact that the ‛green character’ of stocks generates on their market price of risk. The study covers a spectrum of international large-cap stocks in influential sectors since 1 January 2013 up to 5 July 2022. Focusing on the asymmetric and shape features of price risk, econometric outcomes reveal that wellestablished (Industrials) or highly profitable (Technology) sectors are less affected by the skewness/kurtosis price of risk or the “environmental-friendly” features. On the contrary, sectors vulnerable to alterations in income or wealth, such as Financials and Real Estate are more sizably influenced by negative asymmetry, kurtosis, and the higher volatility in bear markets. The ‛green character’ lowers the market price of stocks, increases risk premia due to higher probability for downwards movements and renders investors more risk-averse and nevertheless, this impact only reflects the initial period of green innovation with high anti-polluting costs as the green label could prove to be greatly beneficial in the future.

This study investigates the dynamic nexus that major international currencies (US dollar, Euro, Japanese yen) exhibit with cryptocurrencies and highly innovative digital money (DeFi and NFT assets) during inflationary periods such as the Russia-Ukraine conflict (from 14 December 2021 until 1 March 2024). The Quantile Vector Autoregressive methodology as in Cunado et al. (2023) and daily data are adopted to investigate the net joint extended dynamic connectedness and network connectedness at lower and upper quantiles. Conventional international currencies act as hedgers against shocks while major cryptocurrencies are only modest generators with Ripple being an influential absorber of effects. DeFi mainly serve for counteracting losses from conventional investments in bear or bull markets and Maker is the most prominent generator of spillovers while NFTs mostly rely on a few very strong leaders –Gala being by far the strongest- to have an impact, imitating Bitcoin in the early cryptocurrency era.

This study investigates whether representative sectoral stock indices, gold, oil, Bitcoin, and wheat can mitigate risk and improve portfolio performance during normal times versus crises. The cutting-edge Quantile Vector Auto regressive model and the Generalized Dynamic Conditional Correlations (Generalized-DCC) framework are adopted covering from 9 January 2017 until 30 August 2022.Econometric findings by the Q-VAR reveal that oil presents the strongest connection with commodities and stock indices and that Bitcoin and wheat despite their significant linkages with financial markets fail to act as safe havens. Moreover, GDCC-GARCH indicates that the returns of sector all indices are weakly related but display powerful volatility co-movements. Gold serves efficiently as a hedger and oil follows and both act as better shelters during crises. Nevertheless, Bitcoin partly abides by conventional markets in stressed periods. Notably, wheat reliably works as a hedger overall but does not become a safe haven during crises.