Browsing by Author "Dionisio, Andreia"

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    Extant linkages between Shanghai crude oil and US energy futures: Insights from spillovers of higher-order moments
    (Elsevier, 2024-08) Banerjee, Ameet Kumar; Dionisio, Andreia; Şensoy, Ahmet; Goodell, John W.
    This study is epicentral to analyzing the impact of futures volatility on portfolio and risk management, as extant literature indicates the challenges of using economic variables that fall short of forecasting volatility beyond lagged values. Further, higher moments may be better adaptive to signaling distress during market upheavals. This paper sources data from Bloomberg from March 26, 2018–April 28, 2023, to examine the dynamic spillovers of higher moments among Shanghai International Energy Exchange and US energy futures contracts by constructing realized skewness and kurtosis. Using nonlinear techniques of mutual information and time-varying vector autoregression (TVP-VAR), we show that realized skewness and kurtosis offer significant information on spillover transmission between the two futures markets, primarily through the crises of COVID-19 and the Russia and Ukraine war. Further, we identify that the risks embedded in these future contracts have increased significantly. Our results have important implications for policymakers, investors, and risk managers.
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    Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment
    (Elsevier, 2022-12) Banerjee, Ameet Kumar; Akhtaruzzaman, Md; Dionisio, Andreia; Almeida, Dora; Sensoy, Ahmet
    The paper examines how various COVID-19 news sentiments differentially impact the behaviour of cryptocurrency returns. We used a nonlinear technique of transfer entropy to investigate the relationship between the top 30 cryptocurrencies by market capitalisation and COVID-19 news sentiment. Results show that COVID-19 news sentiment influences cryptocurrency returns. The nexus is unidirectional from news sentiment to cryptocurrency returns, in contrast to past findings. These results have practical implications for policymakers and market participants in understanding cryptocurrency market dynamics under extremely stressful market conditions.