This paper applies a fractional integration framework to investigate the behaviour of the stock returns of two sets of representative US companies with different environmental profiles, namely green versus polluting firms, as well as of the widely used CPU (Climate Policy Uncertainty) index over...

This paper applies a fractional integration framework to investigate the behaviour of the stock returns of two sets of representative US companies with different environmental profiles, namely green versus polluting firms, as well as of the widely used CPU (Climate Policy Uncertainty) index over...

This paper examines the long-memory properties of returns of exchange-traded funds (ETFs) specializing in robotics and artificial intelligence (AI) listed on the US market, as well as those of other assets such as the WTI crude oil price (West Texas Intermediate), Bitcoin, the S&P 500 index,...

This paper examines the long-memory properties of returns of exchange-traded funds (ETFs) specializing in robotics and artificial intelligence (AI) listed on the US market, as well as those of other assets such as the WTI crude oil price (West Texas Intermediate), Bitcoin, the S&P 500 index,...

This paper examines tail connectedness between various exchange-traded funds (ETFs) focused on artificial intelligence (AI) and some traditional assets such as bonds, equities, Bitcoin, and oil, as well as the VIX uncertainty index, using US daily data over the period from 1 January 2023 to 23...

This paper applies the R² connectedness method proposed by Balli et al. (2023) to analyse contemporaneous and lagged connectedness between returns on several asset classes (sector ETFs, Bitcoin, stock market indices, Brent crude oil) over the period 1 January 2023 – 22 September 2025, in the...

This paper examines tail connectedness between various exchange-traded funds (ETFs) focused on artificial intelligence (AI) and some traditional assets such as bonds, equities, Bitcoin, and oil, as well as the VIX uncertainty index, using US daily data over the period from 1 January 2023 to 23...