Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies

Toan Huynh (), Erik Hille and Muhammad Ali Nasir

Technological Forecasting and Social Change, 2020, vol. 159, issue C

Abstract: In the context of the 4th industrial revolution, artificial intelligence (AI) and environmental challenges, this study investigates the role of AI, robotics stocks and green bonds in portfolio diversification. Using daily data from 2017 to 2020, we employ tail dependence as copulas and the Generalized Forecast Error Variance Decomposition to examine the volatility connectedness. Our results suggest that, first, portfolios consisting of these assets exhibit heavy-tail dependence which implies that in the times of economic turbulence, there will be a high probability of large joint losses. Second, volatility transmission is higher in the short term, implying that short-term shocks can cause higher volatility in the assets, but in the long run, volatility transmission decreases. Third, Bitcoin and gold are vital assets for hedging, though the Bitcoin is also affected by its past volatility, a feature it shares with green bonds and NASDAQ AI. During economic downturns, gold may act as a safe haven, as its shock transmission to NASDAQ AI is just around 1.41%. Lastly, the total volatility transmission of all financial assets is considerably high, suggesting that the portfolio has an inherent self-transmitting risk which requires careful diversification. The NASDAQ AI and general equity indexes are not good hedging instruments for each other.

Keywords: Artificial intelligence; Portfolio diversification; Green bonds; NASDAQ AI; 4th industrial revolution; Cryptocurrencies (search for similar items in EconPapers)
JEL-codes: G11 G15 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (94)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:159:y:2020:i:c:s0040162520310143

DOI: 10.1016/j.techfore.2020.120188

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