Title

Russia’s Oil Production Policies: A Game Theoretical Approach to Russian Responses During Global Oil Price Shocks

Lead Author Major

Mathematical Economics

Lead Author Status

Sophomore

Format

Oral Presentation

Faculty Mentor Name

Dr. Sharmila King

Faculty Mentor Email

sking1@pacific.edu

Faculty Mentor Department

Economics

Additional Faculty Mentor Name

Dr. Manizha Sharifova

Additional Faculty Mentor Email

msharifova@pacific.edu

Additional Faculty Mentor Department

Economics

Additional Faculty Mentor Name

Dr. Farley Staniec

Additional Faculty Mentor Email

fstaniec@pacific.edu

Additional Faculty Mentor Department

Economics

Abstract/Artist Statement

Russia is one of the largest oil producers in the world, as well as one of the largest economies. It produces over $181 billion USD worth of crude oil and natural gas that is exported to the rest of the world each year. However, in the face of falling oil prices, Russia’s real GDP growth has been declining from a growth of 4.4% in 2010 to 2.5% in 2015. In this tenuous environment, Russia’s domestic oil policy will be drastically affected far into the future as oil prices continue to change. The objective of my research is to analyze the best policy options the Russian government could implement in response to falling oil prices using a game theoretic approach. I estimate a vector autoregressive (VAR) model and run a counterfactual simulation of the different scenarios of Russia’s responses and the outcomes. Furthermore, my research creates predictions of Russian responses to oil price changes based on the estimated VAR model and empirical data collected. These predictions outline the best outcomes of multiple what-if scenarios through conducting a best-response analysis of a simultaneous game and finding the Nash Equilibrium. Using this game theoretic approach, my research is able to compare multiple possible economic scenarios and the best policy responses. Variables used to model the effects of oil prices on Russia will include Russia’s oil production and export data, growth rate of Gross Domestic Product, real exchange rate, unemployment rates, the current account balance of Russia, and volatility indexes. The VAR model will be based on Sims (1980), Bernanke et al.’s (1997) and Tuzova et al. (2016) VAR models of oil price shocks.

Location

DeRosa University Center, Room 211

Start Date

29-4-2017 1:00 PM

End Date

29-4-2017 1:20 PM

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Apr 29th, 1:00 PM Apr 29th, 1:20 PM

Russia’s Oil Production Policies: A Game Theoretical Approach to Russian Responses During Global Oil Price Shocks

DeRosa University Center, Room 211

Russia is one of the largest oil producers in the world, as well as one of the largest economies. It produces over $181 billion USD worth of crude oil and natural gas that is exported to the rest of the world each year. However, in the face of falling oil prices, Russia’s real GDP growth has been declining from a growth of 4.4% in 2010 to 2.5% in 2015. In this tenuous environment, Russia’s domestic oil policy will be drastically affected far into the future as oil prices continue to change. The objective of my research is to analyze the best policy options the Russian government could implement in response to falling oil prices using a game theoretic approach. I estimate a vector autoregressive (VAR) model and run a counterfactual simulation of the different scenarios of Russia’s responses and the outcomes. Furthermore, my research creates predictions of Russian responses to oil price changes based on the estimated VAR model and empirical data collected. These predictions outline the best outcomes of multiple what-if scenarios through conducting a best-response analysis of a simultaneous game and finding the Nash Equilibrium. Using this game theoretic approach, my research is able to compare multiple possible economic scenarios and the best policy responses. Variables used to model the effects of oil prices on Russia will include Russia’s oil production and export data, growth rate of Gross Domestic Product, real exchange rate, unemployment rates, the current account balance of Russia, and volatility indexes. The VAR model will be based on Sims (1980), Bernanke et al.’s (1997) and Tuzova et al. (2016) VAR models of oil price shocks.