Financing opportunities for energy firms tighten when oil prices fall, but some feel the crunch more than others.

This study examines the relationship between oil price changes and GDP growth and other macroeconomic variables from the perspective of vulnerability of oil-importing and oil-exporting countries to unexpected oil price shocks driven by tense geopolitical events in three European countries (Norway, Germany and Poland). We apply the structural VAR model and the orthogonalized impulse response functions based on quarterly data from period 1995 to 2024 with respect to two samples: the first spans 1995Q1-2019Q4 (pre-2020 sample) with relatively gradual changes in oil prices and the second spans 1995Q1-2024Q2 (whole sample) with sudden fluctuations in oil prices due to geopolitical developments. A main result of this research is that sudden and unexpected oil price shocks induced by geopolitical events affect economies differently than oil price shocks that happen gradually, both in oil-importing and oil-exporting countries. Different causality patterns and responses in GDP growth in the pre-2020 and the whole sample lead to believe that economies are not more resilient to oil price shocks as has been suggested by some studies which referred to periods not driven by geopolitical events. It is therefore premature to assume that future disruptions in oil prices will not cause a problem for monetary policy. This study contributes to the relevant literature by providing new insight into the debate on the diminishing vulnerability of economy to oil price changes by investigating the effects of unexpected oil price shocks driven by geopolitical tensions. In contrast to previous studies, this paper draws on data including periods with recent geopolitical tensions and covering the homogenous period with regard to energy transformation and major reforms in European countries.

China’s crude oil import has increased sharply since 2002. Its expenditure on oil import now accounts for around 10% of its total commodity import. Thus, there is potential imported inflation or deflation due to oil price fluctuations and China’s central bank may respond to it. We quantitatively analyze the impact of oil prices on China’s benchmark interest rate and monetary supply by a 6-variable structural vector auto-regression model. We draw that: 1) In response to an increase of oil price, China’s central bank generally upgrades interest rate. If oil price rises by 10 US dollars, the 6-month lending base rate will increase by around 0.13 percentage point in 3 months. 2) The effects of price shocks deepen after the oil pricing reform, and specifically, it can explain 19.8% of the variations in monetary policies in one year after October 2008, compared with the 0.83% before October 2001.

Oil shocks exert influence on macroeconomic activity through various channels, many of which imply a symmetric effect. However, the effect can also be asymmetric. In particular, sharp oil price changes "either increases or decreases" may reduce aggregate output temporarily because they delay business investment by raising uncertainty or induce costly sectoral resource reallocation. Consistent with these asymmetric-effect hypotheses, the authors find that a volatility measure constructed using daily crude oil futures prices has a negative and significant effect on future gross domestic product (GDP) growth over the period 1984-2004. Moreover, the effect becomes more significant after oil price changes are also included in the regression to control for the symmetric effect. The evidence here provides economic rationales for Hamilton's (2003) nonlinear oil shock measure: It captures overall effects, both symmetric and asymmetric, of oil price shocks on output.

The shale boom led to greater spillovers from oil investment to aggregate investment.

Even though the effect of oil price shocks on macroeconomics has been extensively investigated, the literature on how efficiency in household energy use affect crude oil price volatility is yet explored. This study unveils whether household energy efficiency lower crude oil price volatility asymmetrically in the United States using the historical and forecast dataset that spans from 1970:Q1-2040:Q1. Applying the multivariate case of Quantile-on-Quantile Regression, the empirical results show that household energy efficiency dampens crude oil price volatility with a stronger connection in quantiles before the median quantiles of crude oil price volatility. However, the effect of household energy efficiency decreases with an increase across quantiles of the crude oil price volatility. The results further show that energy-related CO2 emissions and retail electricity price intensify crude oil price volatility with varying effects across quantiles. These findings are similar to the sensitivity analysis and robustness checks. Overall, the policy implication of our findings is that government and policymakers need to demonstrate unequivocal commitments to improving not only energy-efficient practices at household level but also to mitigate energy-related environmental disasters.

It has been 40 years since the oil crisis of 1973/74. This crisis has been one of the defining economic events of the 1970s and has shaped how many economists think about oil price shocks. In recent years, a large literature on the economic determinants of oil price fluctuations has emerged. Drawing on this literature, we first provide an overview of the causes of all major oil price fluctuations between 1973 and 2014. We then discuss why oil price fluctuations remain difficult to predict, despite economists' improved understanding of oil markets. Unexpected oil price fluctuations are commonly referred to as oil price shocks. We document that, in practice, consumers, policymakers, financial market participants, and economists may have different oil price expectations, and that, what may be surprising to some, need not be equally surprising to others.

Replication data and code for peer-reviewed article published in Energy Economics. Paper published online December 28, 2018.

The prices of fossil fuels increased considerably in 2022 as a result of supply disruptions and rising energy demand. While the crude oil price in 2022 did not reach the levels of the 1979 or 2008 price shocks, natural gas prices rose to record highs.

This is the replication package for the empirical results in "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors" by Antoine A. Djogbenou, Journal of Applied Econometrics, 2023, forthcoming. It includes four folders containing all data files and Matlab codes.

This package contains the dataset as well as links to the Stata commands needed to compute the statistics and results in the empirical section of Hany Abdel-Latif, Rehab A. Osman & Heba Ahmed (2018) Asymmetric impacts of oil price shocks on government expenditures: Evidence from Saudi Arabia, Cogent Economics & Finance DOI: 10.1080/23322039.2018.1512835 https://www.tandfonline.com/doi/abs/10.1080/23322039.2018.1512835

(1) "data20180831.xls": The excel file containing quarterly data from 1990q1 to 2017q2 (date) on oil price (oilprce), the log of oil price (lnoil), government expenditures on education (deuce), government expenditures on health (health), time trend (time)

(2) Stata program and codes: To estimate the ARDL model and obtain the results in the paper, you need to download and install the Stata user program and codes written by Kripfganz, S. and D. C. Schneider (2016). ardl: Stata module to estimate autoregressive distributed lag models. Presented July 29, 2016, at the Stata Conference, Chicago.Available on http://www.kripfganz.de/stata/ardl.html To estimate the nonlinear ARDL (NARDL) model and obtain the results in the paper, you need to download and install the Stata user program and codes written by by Marco Sunder (version 26jan2012). Available at http://www.marco-sunder.de/stata/

The computations were done in Stata 13 using Mac.

It would be appreciated if the paper and use of the dataset are acknowledged.

Hany Abdel-Latif
h.abdel-latif@swansea.ac.uk

August, 31, 2018

Using time-varying BVARs, we find a substantial decline in the shortrun price elasticity of oil demand since the mid-1980s. This finding helps explain why an oil production shortfall of the same magnitude is associated with a stronger response of oil prices and more severe macroeconomic consequences over time, while a similar oil price increase is associated with smaller output effects. Oil supply shocks also account for a smaller fraction of real oil price variability in more recent periods, in contrast to oil demand shocks. The overall effects of oil supply disruptions on the US economy have, however, been modest.

Previous literature suggests that the pass-through of oil price shocks to inflation rates became weaker since the 1970s. I use a time-varying parameter VAR to show that this trend has recently been reversed with headline and core inflation rates responding more sensitive to oil price shocks. Based on a counterfactual analysis, I offer evidence that increasingly important second round effects propagated via inflation expectations play a key role for these dynamics. Finally, I illustrate that oil price shocks in general and this expectation channel more specifically contributed substantially to the recent surge in inflation rates.

Using US micro-level data on banks, we document a negative effect of high oil prices on US banks' balance sheets, more negative for highly leveraged banks. We set and estimate a general equilibrium model with banking and oil sectors that rationalizes those findings through the financial accelerator mechanism. This mechanism amplifies the effect of oil price shocks, making them non-negligible drivers of the dynamics of US banks' intermediation activity and of the US real economy. Macroprudential policy, in the form of a countercyclical capital buffer, can meaningfully address oil price fluctuations and reduce the volatility they cause in the US economy.

We find that oil supply shocks decrease average real wages, particularly skilled wages, and increase wage dispersion across regions, particularly unskilled wage dispersion. In a model with spatial energy intensity differences and nontradables, labor demand shifts, while explaining the response of average wages to oil supply shocks, have counterfactual implications for the response of wage dispersion. Only an additional response in labor supply can explain this latter fact, highlighting the importance of general equilibrium effects in a spatial context. We provide additional empirical evidence of regionally directed worker reallocation and housing prices consistent with our spatial model. Finally, we show that a calibrated version of our model can quantitatively match the estimated effects of oil supply shocks.

Linear Granger causality between oil price shocks and investor sentiment.

Estimation for asymmetric effects of oil-specific demand shock on investor sentiment.

{"Abstract of associated article: There is not one primary energy market and the price of oil is not a good proxy for many alternative energy prices. Therefore, this paper explores the effects of alternative energy price shocks on economic activity, and examines the relative performance of these alternative measures in predicting state level economic activity using the Davidson and MacKinnon J-test. The alternative energy price series considered are as follows: gasoline, diesel, natural gas, heating oil, and electricity. Alternative measures of energy price shocks produce different patterns of impulse responses than oil price shocks. Additionally, overwhelming evidence indicates that alternative energy price models, excluding models containing the price of gasoline, outperform the baseline model containing the price of oil for many states, particularly at short-to-mid forecast horizons. Using alternative energy prices should lead to a more accurate modeling of the energy price–macroeconomy relationship."}

This paper examines the effects of oil demand and supply shocks on emerging market economies in Latin America using a Bayesian vector autoregressive (VAR) model that combines zero and sign restrictions. Our results highlight the importance of separately identify the oil market shocks. The higher price of oil driven by increased global demand produces higher output growth in Brazil, Colombia, and Chile. The results are more persistent for Brazil and Colombia likely due to increased income from oil exports, as both economies are net oil exporters. The better times in the domestic economies result in lower uncertainty and appreciation of the exchange rate in all countries in the sample. Oil supply shocks and oil-specific demand shocks are not statistically significant for most variables. Our results provide important insights into the appropriate exchange rate policy in emerging market economies.

Graph and download economic data for Spot Crude Oil Price: West Texas Intermediate (WTI) (WTISPLC) from Jan 1946 to Oct 2025 about WTI, crude, oil, price, and USA.