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The purpose of this paper is to examine the effects of several structural shocks in oil prices on the Vietnamese economy and answer three key research questions: Is there a relationship between oil price shocks and macroeconomic indicators in Vietnam? How do different types of oil price impulses affect Vietnamese inflation and economic performance? To what extent do structural shocks in oil prices explain variations in Vietnam’s macroeconomic indicators?

Lower triangular Cholesky decomposition is performed on a short-term impact matrix in a two-block structural vector autoregressive model. The data set is defined monthly, from January 2000 to December 2021. The contributions of structural shocks in oil prices to the domestic variances are analysed using variance decomposition methods. In this study, both forecast error variance decomposition and historical decomposition are used.

The consequences of oil price fluctuations on Vietnamese output and inflation depend on different sources of oil price shocks. In comparison, oil supply shocks have an insignificant effect on both domestic industrial output and consumer price index inflation; however, positive shocks in aggregate and precautionary oil demands increase these domestic indicators substantially and sustainably. An analysis of variance decompositions reveals that supply-side oil shocks have very limited explanatory power for variations in domestic variables. Nevertheless, the contributions of unanticipated demand-side booms to domestic variations in the past and projected forecasts are considerable.

The findings from this research uncover potential risks for Vietnam’s economic prospects if the consequences of oil price shocks are not managed effectively.

Given the lack of economic sensitivity to supply-side oil shocks and the strong response to shifts in oil demands, greater pressure on the domestic economy is likely when Vietnam increases its dependence on oil imports.

It has been said that oil price shocks affect stock market returns. However, empirical studies remain inconclusive regarding the nexus between oil price shocks and stock market returns. Consequently, the purpose of this study is to investigate the asymmetric impact of oil price shocks on stock returns in Nigeria.

A two-stage Markov regime-switching approach is used to examine the asymmetric effects of three different structural oil shocks on stock returns. The oil shocks, which include oil supply shock, aggregate demand shock and oil-specific demand shock, are derived using structural vector autoregressive. Monthly data that spans the period between January 1990 and December 2018 are deployed for estimation.

The linear estimation results show that only oil demand shock negatively and significantly affects the stock market returns. The Markov-switching regime results reveal that oil supply shock has a significant positive impact on the stock returns in a low-volatility state, whereas oil-specific demand shock negatively impacts the stock returns in a high-volatility state.

There is a need for policymakers and investors to take cognizance of not only the positive outcomes of a relatively stable state of oil price but also the negative consequences of a high-volatility state when formulating policy and making investment decisions, respectively.

This study differs from other similar studies in Nigeria that have examined the asymmetric relationship between oil price shocks and stock market return by using a two-stage Markov regime-switching approach. To the best of the authors’ knowledge, this is the first attempt at using this methodology.

UK regional data on GDP and the GDP deflator are analysed to extract information on underlying demand and supply shocks as well as aggregate demand and supply shocks. Identification is achieved using long run restrictions, based on a theoretical model. The main results are that the supply shocks are almost completely symmetric across UK regions and that there is no evidence of these shocks being propagated slowly across the regions.

This paper aims to investigate the relationship between oil price shocks and world food prices between 1974 and 2018.

The authors use the SVAR model to disentangle the oil price into supply, aggregate demand and oil-specific demand shocks and apply the detrended cross-correlations analysis to measure the association between oil price shocks and food returns/volatility and analyze contagion effects between oil and food markets.

The results show that the correlations between oil and food prices depend on whether oil prices changes are driven by supply or demand shocks. Particularly, food returns (volatility) are positively (negatively) more dependent on the oil price changes driven by aggregate demand (oil specific demand) shocks. Further analysis dealing with contagion analysis between oil and food markets shows a contagion effect during the food crisis of 2006–2008. Oil-specific demand shocks are the main source of this phenomenon.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

The results of this study are important for policymakers and investors. They should account for the oil price fluctuations differently depending on whether the oil price shocks are driven by the demand or supply side. Moreover, they should anticipate an increase (decrease) in food prices due to a positive (negative) oil shock. In addition, special attention should be accorded to the world oil demand. Finally, when a food crisis occurs, markets operators should focus more on the specific oil-demand shocks, as it is the most contributor to possible contagion effects between oil and food markets.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

This study aims to measure the relationship between demand and exchange rate shocks in the tourism industry.

A panel data set is constructed covering the period between 1995 and 2017, and the data set includes the top 26 countries that host 10 million tourists and above in the world as of 2017. The standard errors of the series are used as an indicator of shocks. First, the cross-sectional dependency, stationarity and the homogeneity of the series are examined; second, a panel cointegration analysis is implemented; third, long-term panel cointegration coefficients are analyzed with Dynamic Common Correlated Effects (DCCE) approach; and, finally, Dumitrescu and Hurlin’s (2012) Granger non-causality test is used to detect the causality.

The preliminary analyses show that the variables are cross-sectional dependent and heterogeneous and are stationary in their first difference; hence, the effects of the shocks are temporary. On the other hand, as a result of the panel cointegration analysis, it is found that both series are cointegrated over the long-term. However, the long-term coefficients estimated with the DCCE approach are found not to be statistically significant. Finally, as a result of the Dumitrescu and Hurlin’s (2012) Granger non-causality test, it is concluded that there is a causality running from exchange rate shocks to demand shocks.

To the best of the authors’ knowledge, the cointegration between the tourism demand shocks and exchange rates shocks has not been investigated before, and therefore, this study is considered to be a pioneering study that will contribute to the literature.

The purpose of this paper is to re‐examine the structural origins of international crude oil price fluctuation.

The paper establishes a structural vector autoregression model based on the generalized supply and demand analysis of crude oil price fluctuation and performance the structural decomposition of price shocks with impulse response analysis of those factors.

It is found that four kinds of structural shocks derived from the generalized supply and demand analysis are the essential determinants of crude oil prices fluctuation. On one hand, similar to Kilian's results, the supply side shocks – both the exogenous geopolitical ones and other oil supply shocks have little influence. Whereas, the demand side shocks – both the aggregate demand shock and the oil market specific demand shock have prominent effects. On the other hand, with the expanded sample range, it is found that the dynamic characteristic of the impulse response of oil price to demand side factors is not only incompatible with the basic economic theory, but also clashes with Kilian's statement based upon his research. It is conjured that the incompatibility comes from the ignorance of the finer decomposition of demand side factors. To decompose those demand side factors further, the US dollar liquidity was added into the model. The results show that the impact of US dollar liquidity on the fluctuation of oil prices cannot be ignored. The argument that ascribes the soaring international crude oil price to China's economic growth lacks theoretical and empirical evidence.

The paper contributes marginally to the research on the structural origins of international crude oil price fluctuation and sheds light on the possibility of finer decomposition of demand side oil shocks.

Using quarterly data for a sample of 17 industrial countries, the purpose of this paper is to study asymmetry in the face of monetary shocks compared to government spending shocks.

The paper outlines demand and supply channels determining the asymmetric effects of monetary and fiscal policies. The time‐series model is presented and an analysis of the difference in the asymmetric effects of monetary and fiscal shocks within countries is presented. There then follows an investigation of the relevance of demand and supply conditions to the asymmetric effects of monetary and fiscal shocks. The implications of asymmetry are contrasted across countries.

Fluctuations in real output growth, price inflation, wage inflation, and real wage growth vary with respect to anticipated and unanticipated shifts to the money supply, government spending, and the energy price. The asymmetric flexibility of prices appears a major factor in differentiating the expansionary and contractionary effects of fiscal and monetary shocks. Higher price inflation, relative to deflation, exacerbates output contraction, relative to expansion, in the face of monetary shocks. In contrast, larger price deflation, relative to inflation, moderates output contraction, relative to expansion in the face of government spending shocks. The growth of output and the real wage decreases, on average, in the face of monetary variability in many countries. Moreover, the growth of real output and the real wage increases, on average, in the face of government spending variability in many countries. Asymmetry differentiates the effects of monetary and government spending shocks within and across countries. The degree and direction of asymmetry provide a new dimension to differentiate between monetary and fiscal tools in the design of stabilization policies.

The paper's evidence sheds light on the validity of theoretical models explaining asymmetry in the effects of demand‐side stabilization policies. Moreover, the evidence should alert policy makers to the need to relax structural and institutional constraints to maximize the benefits of stabilization policies and minimize the adverse effects on economic variables.

This paper aims to better understand if speculative activity is a factor or even the main factor in the run-up of oil prices in the spot market, particularly in the recent price bubble that occurred in the period from mid-2003 to 2008.

The methodology used is based on an existing vector autoregressive model proposed by Kilian and Murphy (2014), which is a structural model of the global market for crude oil that accounts for flow demand and flow supply shocks and speculative demand oil shocks.

From the output of the authors’ structural model, the authors ruled out speculation as a factor of rising oil prices. The authors have found instead that the rapid oil demand caused by an unexpected increase in the global business cycle is the most accurate culprit. Despite the change of perspective in the speculative component, the authors’ conclusions concur with the findings of Kilian and Murphy (2014) and others.

As far as the authors are aware, this is the first time that a study has used as a spread oil variable, a speculative component of the real price, replacing the oil inventories considered by Kilian and Murphy (2014). Another contribution is that the model used allows estimating traditional oil demand elasticity in production and oil supply elasticity in spread movements, casting doubt on existing models with perfect price-inelastic output for crude oil.

Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact on the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference.

This paper aims to investigate the impact of oil price shocks on the Turkish sovereign yield curve factors.

To extract the latent factors (level, slope and curvature) of the Turkish sovereign yield curve, we estimate conventional Nelson and Siegel (1987) model with nonlinear least squares. Then, we decompose oil price shocks into supply, demand and risk shocks using structural VAR (structural VAR) models. After this separation, we apply Engle (2002) dynamic conditional correlation GARCH (DCC-GARCH (1,1)) method to investigate time-varying co-movements between yield curve factors and oil price shocks. Finally, using the LP (local projections) proposed by Jorda (2005), we estimate the impulse-response functions to examine the impact of different oil price shocks on yield curve factors.

Our results demonstrate that the various oil price shocks influence the yield curve factors quite differently. A supply shock leads to a statistically significant increase in the level factor. This result shows that elevated oil prices due to supply disruptions are interpreted as a signal of a surge in inflation expectations since the cost channel prevails. Besides, unanticipated demand shocks have a positive impact on the slope factor as a result of the central bank policy response for offsetting the elevated inflation expectations. Finally, a risk shock is associated with a decrease in the curvature factor indicating that risk shocks influence the medium-term bonds due to the deflationary pressure resulting from depressed economic conditions.

Our results provide new insights to understand the driving forces of yield curve movements induced by various oil shocks to formulate appropriate policy responses.

The study contributes to the literature by two main dimensions. First, the recent oil shock identification scheme of Ready (2018) is modified using the “geopolitical oil price risk index” to capture the changes in the risk perceptions of oil markets driven by geopolitical tensions such as terrorism and conflicts and sanctions. The modified identification scheme attributes more power to demand shocks in explaining the variation of the oil price compared to that of the baseline scheme. Second, it provides recent evidence that distinguishes the impact of oil demand and supply shocks on Turkey's yield curve.