'Categorical Economic Policy Uncertainties and Tail Risk in Energy Markets: A Connectedness Analysis'

The Global Studies Seminar presents Alex Scarcioffolo.

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The Global Studies Seminar presents “Categorical Economic Policy Uncertainties and Tail Risk in Energy Markets: A Connectedness Analysis” by Denison University’s Assistant Professor of Data Analytics Alex Scarcioffolo. 

Managing market risk is vital for energy companies to protect themselves against catastrophic losses due to unpredictable drops in prices. This paper analyzes information transmission between energy market risks and various US policy uncertainty indexes from 1994 to 2023. Policy uncertainties in monetary, fiscal, taxes, regulation, and trade are considered, along with the Value at Risk (VaR) of oil and natural gas markets. Results suggest that oil and natural gas are net receivers of information from shocks on policy uncertainties. Regulation and tax uncertainties are the main transmitters of shocks to extreme losses in the oil market, while monetary uncertainty is the leading risk for natural gas. While the information transmission from energy market risks to categorical EPUs is generally low, trade policy uncertainty receives considerably more information from oil and gas VaRs than the other categorical EPU indexes, possibly reflecting the importance of the energy market in reducing the US trade deficit. 

Scarcioffolo brings a dynamic approach to the intersection of data analytics and economics. Specializing in applied economics within the realms of energy commodities and finance, his research delves into the intricacies of market integration and the dynamics of price and risk analysis. Holding a Doctor of Philosophy from West Virginia University, Scarcioffolo has a rich background in teaching various courses ranging from Microeconomics, Macroeconomics, Growth, Forecasting, and Data Analytics. His enthusiasm for exploring and teaching is equally balanced by his dedication to nurturing his students’ ability to think critically and analyze data effectively.


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