ABSTRACT
Since the emergence of blockchain technology, several digital assets such as cryptocurrencies, DeFi, and NFTs have gained considerable attention from investors and policymakers. However, the blockchain market has significant negative ramifications for the environment that may transmit shocks towards eco-friendly financial assets. We use the rolling window wavelet correlation (RWWC) model and the quantile-based time-varying (QVAR) connectedness framework to analyze the dynamic price correlation and connectedness between the blockchain market and green (eco-friendly) financial assets. As a representative of the blockchain market, we use the price returns of four cryptocurrencies, DeFi, and NFTs. For green equities, we use the MSCI Global Environment Price Index and the S&P Green Bond Price Index. We find a low correlation between the blockchain market and green financial assets before the outbreak of COVID-19 and a strong correlation during the COVID-19 and the Russia-Ukraine war. The quantile VAR results show symmetric connectedness of the examined and identical spillovers between extremely positive and strongly negative returns. Green bonds and stocks are the system's major shock receivers. The transmission network results imply major shock transmissions are driven by short-term frequency, whereas there is a lower transmission in the long-term. © 2023 Elsevier Inc.
ABSTRACT
This paper uses fractional integration to assess the impact of US policy responses to the COVID-19 pandemic on 10 US sectoral stock indices from 1 January 2020 to 11 June 2021. The results provide evidence of mean reversion in most cases and suggest that the Effective Federal Funds Rate and monetary and fiscal announcements are the most effective policy tools. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
This paper assesses the impact of US policy responses to the Covid-19 pandemic on various technology-related assets such as cryptocurrencies, financial technology, and artificial intelligence stocks using fractional integration techniques. More precisely, it analyzes the behavior of the percentage returns in the case of nine major coins (Bitcoin-BITC, Stella-STEL, Litecoin-LITE, Ethereum-ETHE, XRP (Ripple), Dash, Monero-MONE, NEM, Tether-TETH) and two technology-related stock market indices (the KBW NASDAQ Technology Index-KFTX, and the NASDAQ Artificial Intelligence index-AI) over the period 1 January 2020-5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down;by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all.
ABSTRACT
This paper provides a comparative analysis of how the energy-sector stocks of 20 regional blocs (Americas, Australasia, BRIC, Southeast Asia, Scandinavia, Southern Europe, Far East, Europe, European Union, Emerging Europe, Asia, G7, G12, Economic and Monetary Union (EMU), CCARBNS, Latin America, North America, PIIGS, Asia-Pacific and NORCS) are connected from 5 July 1994 to 21 April 2020. It uses various techniques: Diebold and Yilmaz (2014)(DY 2014, hereafter) spillover indices and TVP-VAR, LASSO-VAR. Our main results are as follows: First, the DY approach results show that the biggest net contributor of volatility is the CCARBNS region, followed by the G12 and G7 regions, while the biggest receiver of volatility is the Southeast Asia region. Second, the TVP-VAR and LASSO-VAR results reveal that Scandinavia, Far East, and America's regions are net receivers of energy shocks, with net transmitters being CCARBNS, G7, G12 and Emerging European regions. Third, during the 2007-2008 financial crisis and recent COVID-19 outbreak, energy stock market spillovers have reached unprecedented high levels. Fourth, the world policy uncertainty greatly influenced the magnitude of volatility spillovers across regional energy stock markets.
ABSTRACT
This paper investigates the dependence structure and dynamics between artificial intelligence (AI) and carbon prices in the era of the 4th industrial revolution. Using the NASDAQ AI price index as a measure of AI and the European Energy Exchange EU emissions trading system (i.e. certificate prices for CO2 emissions) as a measure of carbon prices, we employ time-varying Markov switching copula models from December 2017 to July 2020 that provide evidence of a time-varying Markov tail dependence structure and dynamics between AI and carbon prices. The result shows a negative dependence structure for the return series between AI and carbon prices. However, the relationship is asymmetric, indicating that there is a stronger tail dependence in the lower tails instead of the upper tails. The finding implies that AI is a favourable hedge against carbon prices, therefore indicating the diversification benefits of AI. To understand the issue in detail, we examine the effect of economic policy uncertainty, equity market volatility, and the recent COVID-19 pandemic;we find their negative effect on the dynamic dependence structure between AI and carbon prices at lower and higher quantiles. This evidence offers additional support for the safe-haven ability of AI for carbon prices.