Macroeconomic Uncertainty and Oil Price Volatility

This paper shows that higher macroeconomic uncertainty causes higher oil price volatility. Regimes of low and high uncertainty are identified in a threshold VAR model in which the effects of structural oil demand and supply shocks are estimated. The results show that higher macroeconomic uncertainty, as measured by global industrial production volatility, significantly increases the sensitivity of oil prices to shocks in oil demand and supply. This occurs as uncertainty lowers the price elasticity of oil demand and supply. The difference in the estimated oil price elasticities is economically meaningful as the price impact of a similar change in oil production might double when it hits the economy in uncertain times. As such, varying uncertainty can explain why oil price volatility is typically higher during periods such as financial crises and recessions, and why oil price volatility changes over time more generally.     

Oil prices,stock returns,and exchange rates: Empirical evidence from China and the United States

Employing the diagonal BEKK model as well as the dynamic impulse response functions, this study investigates the time-varying trilateral relationships among real oil prices, exchange rate changes, and stock market returns in China and the U.S. from February 1991 to December 2015. We highlight several key observations: (i) oil prices respond positively and significantly to aggregate demand shocks; (ii) positive oil supply shocks adversely and significantly affect the Chinese stock market; (iii) oil price shocks persistently and significantly impact the trade-weighted US dollar index negatively; (iv) the US and China stock markets correlate positively just as the dollar index and the exchange rate does; (v) a significant parallel inverse relation exists between the US stock market and the dollar and between the China stock market and the exchange rate; and (vi) the Chinese stock market is more volatile and responsive to aggregate demand and oil price shocks than the US stock market in recent years.     

How should monetary policy respond to changes in the relative price of oil? Considering supply and demand shocks

This paper examines optimal monetary policy in a New Keynesian model where supply and demand shocks affect the price of oil. Optimal policy fully stabilizes core inflation when wages are flexible. The nominal rate rises (falls) in response to the demand (supply) shock. With sticky wages core inflation falls (rises) in response to the demand (supply) shock. Impulse response functions from a VAR estimated with post-1986 U.S. data show minimal movement in core inflation in response to both shocks. The federal funds rate rises (falls) in response to the demand (supply) shock, consistent with the predictions from the theoretical model for policy that stabilizes core inflation.     

Oil structural shocks,bank-level characteristics,and systemic risk: Evidence from dual banking systems

By performing a structural VAR analysis on oil price shocks, we provide an evidence on how the origins of oil price shocks impact the risk level of banks in oil-exporting countries and whether bank-level characteristics can influence the sensitivity of risk to oil shocks. When conducting panel regression analysis, we document the following findings. First, not all shocks have the same effect on bank risk. Due to oil supply shocks, the increase in oil price raises bank risk, whereas the similar increase in price due to economic expansion or oil-market specific demand reduces that risk. Second, the business model (whether the bank is Islamic or conventional), size, income diversification, profitability, and financial leverage influence the bank risk exposure to oil shocks differently. Third, the two major recent crises (global financial crises and COVID-19 pandemic) magnified bank risk exposure to oil supply shocks and speculative oil demand shocks. Overall, the structural oil shocks explain a large fraction of the variation in financial stability in GCC countries.     

Gasoline prices,transport costs,and the U.S. business cycles

The effects of gasoline prices on the U.S. business cycles are investigated. In order to distinguish between gasoline supply and gasoline demand shocks, the price of gasoline is endogenously determined through a transportation sector that uses gasoline as an input of production. The model is estimated for the U.S. economy using five macroeconomic time series, including data on transport costs and gasoline prices. The results show that although standard shocks in the literature (e.g., technology shocks, monetary policy shocks) have significant effects on the U.S. business cycles in the long run, gasoline supply and demand shocks play an important role in the short run.     

Common shocks,common transmission mechanisms and time-varying connectedness among Dow Jones Islamic stock market indices and global risk factors

The purpose of this paper is twofold. First, we examine the importance of permanent versus transitory shocks as well as their domestic and foreign components in explaining the business cycle fluctuations of seven Dow Jones Islamic stock markets (DJIM), namely U.S., U.K., Canada, Europe, Asia-Pacific, Japan and GCC, over the period from April 2003 to November 2018, using the permanent-transitory (P-T) decompositions approach of Centoni et al. (2007). Second, we investigate the spillover mechanisms of these shocks across Islamic stock markets and a set of global risk factors, using the Diebold and Yilmaz (DY) (2012) approach. The P-T decomposition results show that the DJIM U.S., U.K., Europe and GCC indices are sensitive to both domestic and foreign shocks, while the DJIM Canada, Japan and Asia-Pacific are most sensitive to domestic shocks. The empirical results of the DY approach indicate that: (i) the return and volatility spillover intensity increase during financial turmoil, supporting evidence of the contagion phenomenon, (ii) the DJIM U.S. is the main transmitter of return and volatility spillovers, while the DJIM GCC is identified as the main receiver of both return and volatility spillovers, (iii) the seven Dow Jones Islamic stock indices are weakly linked to movements of global risk factors, and (iv) there is evidence of possible portfolio diversification between the selected Islamic stock markets and the oil commodity market.     

The international transmission channels of US supply and demand shocks: Evidence from a non-stationary dynamic factor model for the G7 countries

We employ a multi-country non-stationary dynamic factor model to assess spillover effects and transmission channels of US supply and demand shocks on a variety of macroeconomic variables in individual non-US G7 countries. We find that trade, financial and confidence channels all play a significant role in the international transmission of US shocks. However, the results point to substantial heterogeneities of shock transmission across the individual G7 economies. In particular, we find negative transmission effects for Italy and Japan as the only two G7 countries not well integrated into global value chains. Moreover, the exchange rate responses of Germany, France and Italy turn out to be far less pronounced in comparison to the other G7 economies which we relate to their membership of the euro area and their coordinated monetary policies prior to the establishment of the euro. Whereas we document a close comovement of stock market dynamics across the G7 countries, we find credit and real estate markets to be less synchronized. We do not find the effects and transmission channels to be fundamentally affected by the post-2008 economic environment.     

Speculation in the Oil Market

The run‐up in oil prices since 2004 coincided with growing investment in commodity markets and increased price co‐movement among different commodities. We assess whether speculation in the oil market played a role in driving this salient empirical pattern. We identify oil shocks from a large dataset using a dynamic factor model. This method is motivated by the fact that a small‐scale vector autoregression is not informationally sufficient to identify the shocks. The main results are as follows. (i) While global demand shocks account for the largest share of oil price fluctuations, speculative shocks are the second most important driver. (ii) The increase in oil prices over the last decade is mainly driven by the strength of global demand. However, speculation played a significant role in the oil price increase between 2004 and 2008 and its subsequent collapse. (iii) The co‐movement between oil prices and the prices of other commodities is mainly explained by global demand shocks. Our results support the view that the recent oil price increase is mainly driven by the strength of global demand but that the financialization process of commodity markets also played a role. Copyright © 2014 John Wiley & Sons, Ltd.     

Oil,Gas, or Financial Conditions-Which One Has a Stronger Link with Growth?

Using the models of Diebold-Yilmaz (2012) and Barunik and Krehlik (2018) and monthly U.S. data from January 1992 to May 2019 (329 observations), this study estimates the return and volatility connectedness transmitted from commodity markets (natural gas and crude oil) and the Kansas City financial stress index to macroeconomic indicators (GDP and CPI). As a research target, crude oil has received significant attention. Although natural gas plays an important role in the energy markets as an environment-friendly alternative, it has not been studied extensively. We find the different spread speed of shocks to return and volatility variables through the total spillover index. We focus on both crude oil and natural gas and find that after the bankruptcy of the Lehman Brothers on September 19, 2008, there was a significant jump in the total return spillover from 35.09% to 46.91%, peaking in October 2008. Furthermore, in the frequency domain, we find that the total long-term return spillover index had the highest proportion during the global financial crisis. When the total spillover is concentrated on the high frequencies, it means the system will have an impact mostly in the short term. When it is concentrated on the lower frequencies, it shows that shocks are persistent and works in the long term among the system. It could give some information to the policymakers.     

The effects of oil price shocks on inflation in the G7 countries

This study examines the effects of three types of oil price shocks on inflation in the G7 countries with a new method of isolating oil price shocks. Based on monthly data from January 1997 to January 2019, we find that each oil price shock has the largest effect on U.S. inflation among the G7 countries and each country’s response to oil price shocks is different. Moreover, a rolling-window analysis shows that supply shocks, demand shocks and risk shocks have dynamic effects on inflation. The effect of supply shocks on inflation is strong before the financial crisis, but weakens during the crisis. However, the effect of demand shocks increases sharply in this time. The effect of risk shocks mainly occurs during the financial crisis and the European debt crisis. In addition, this study uses two ways to verify the robustness of the results. Our empirical results have important implications for policymakers and manufacturers, since the results provide a good explanation for the response of inflation in the G7 countries to the oil price shocks from different sources.     

Spillover effects in oil-related CDS markets during and after the sub-prime crisis

This paper investigates the return and volatility spillover effects across oil-related credit default swaps (CDSs), the oil market, and financial market risks for the US during and after the subprime crises. The empirical analysis is based on monthly return and realized volatility data from February 2004 to April 2020. We estimate both static and dynamic generalized dynamic spillover measures based on vector autoregressive (VAR) models. Our full sample empirical findings show that the oil market is the primary source of risk transmission for all the oil-related credit default swaps, while the bond market is the highest source of risk transmission to the stock market and vice versa. We also provide evidence that the regulated monopoly US utility sector has the least role in volatility transmission. Furthermore, the bailout program conducted by the US Treasury and Federal Reserve helped stabilize the US financial market through the purchase of toxic assets after the subprime financial crisis. We find strong evidence that the federal funds rate hike cycles lessen total risk transmission throughout the US bond market. Finally, our findings assert that oil price shocks have a significant effect on the oil-related CDSs in some sub-periods via the demand and supply transmission channels.     

House price synchronization across the US states: The role of structural oil shocks

This paper analyzes the impact of disentangled oil shocks on the synchronization in housing price movements across all the US states plus DC. Using a Bayesian dynamic factor model, the house price movements are decomposed into national, regional, and state-specific factors. We then study the impact of oil-specific supply and demand, inventory accumulation, and global demand shocks on the national factor using linear and nonlinear local projection methods. The impulse response analyses suggest that oil-specific supply and consumption demand shocks are most important in driving the national factor. Moreover, as observed from the regime-specific local projection model, these two shocks are found to have a relatively stronger impact in a bearish rather than a bullish national housing market. Our results have important policy implications.     

THE ROLE OF INVENTORIES AND SPECULATIVE TRADING IN THE GLOBAL MARKET FOR CRUDE OIL

We develop a structural model of the global market for crude oil that for the first time explicitly allows for shocks to the speculative demand for oil as well as shocks to flow demand and flow supply. The speculative component of the real price of oil is identified with the help of data on oil inventories. Our estimates rule out explanations of the 2003–2008 oil price surge based on unexpectedly diminishing oil supplies and based on speculative trading. Instead, this surge was caused by unexpected increases in world oil consumption driven by the global business cycle. There is evidence, however, that speculative demand shifts played an important role during earlier oil price shock episodes including 1979, 1986 and 1990. Our analysis implies that additional regulation of oil markets would not have prevented the 2003–2008 oil price surge. We also show that, even after accounting for the role of inventories in smoothing oil consumption, our estimate of the short‐run price elasticity of oil demand is much higher than traditional estimates from dynamic models that do not account for for the endogeneity of the price of oil. Copyright © 2013 John Wiley & Sons, Ltd.     

Identifying credit demand,financial intermediation,and supply of funds shocks: A structural VAR approach

In this paper, we identify three exogenous shocks to credit market: demand for credit, supply of funds into the financial system, and the willingness to lend of financial institutions (financial intermediation), and also, determine the contribution of these shocks to fluctuations in the credit market and overall economic activity. We estimate a structural vector autoregression model where the three credit shocks are identified with a set of sign restrictions motivated by a simple partial equilibrium model of financial intermediation. We find that the credit demand shock explains significantly the variations in the long-term loan rate proxied by the Moody’s Baa corporate bond yield, while the supply of funds shock contributes to most of the fluctuations in the short-term commercial paper rate. The financial intermediation shock drives most of the fluctuations in the quantity of loans as well as the spread between the Baa and commercial paper rates. Of the credit shocks, we find that the financial intermediation shock has the largest impact on real economic activity. In fact, our analysis implies that the sharp decline in output during the 2007–2009 financial crisis is largely attributable to the financial intermediation shock, along with shocks originating outside of the financial system.     

Corporate bond market interdependence: Credit spread correlation between and within U.S. and Canadian corporate bond markets

This study investigates the correlation and interdependence between and within the U.S. and Canadian corporate bond markets. The empirical framework adopted allows credit spreads to depend on common systematic risk factors derived from structural models and incorporates dynamic conditional correlations (DCC) between spreads. Results show that there is a surprisingly weak correlation between the two markets in normal times. However, during crises, there is a sudden and strong increase in the correlation between U.S. and Canadian credit spreads. The analysis of credit spread correlation within each market also shows an unusual increase in credit spread correlations between sectors and between risk classes in the U.S. during the 2007–2009 global financial crisis. This increase persists over the post-crisis period. By contrast, in Canada, credit spread correlations between sectors remain remarkably stable over time, suggesting an interdependence of credit spreads within the Canadian market.     

Large time‐varying parameter VARs: A nonparametric approach

In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time‐varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs. When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and compares well with large (parametric) Bayesian VARs with time‐varying parameters. The tool can also be used for structural analysis. As an example, we study the time‐varying effects of oil price shocks on sectoral U.S. industrial output. According to our results, the increased role of global demand in shaping oil price fluctuations largely explains the diminished recessionary effects of global energy price increases.     

The time varying effect of oil price shocks on euro-area exports

In this paper we provide novel evidence on changes in the relationship between the real price of oil and real exports in the euro area. By combining robust predictions on the sign of the impulse responses obtained from a theoretical model with restrictions on the slope of the oil demand and oil supply curves, we identify oil supply and foreign productivity shocks in a time varying VAR with stochastic volatility. We find that from the 1980s onwards the relationship between oil prices and euro area exports has become less negative conditional on oil supply shortfalls and more positive conditional on foreign productivity shocks. Using the theoretical model we show that our empirical findings can be accounted for by (i) stronger trade relationship between the euro area and emerging economies (ii) a decrease in the share of oil in production and (iii) increased competitive pressures in the product market.     

THE ROLE OF TIME‐VARYING PRICE ELASTICITIES IN ACCOUNTING FOR VOLATILITY CHANGES IN THE CRUDE OIL MARKET

There has been a systematic increase in the volatility of the real price of crude oil since 1986, followed by a decline in the volatility of oil production since the early 1990s. We explore reasons for this evolution. We show that a likely explanation of this empirical fact is that both the short‐run price elasticities of oil demand and of oil supply have declined considerably since the second half of the 1980s. This implies that small disturbances on either side of the oil market can generate large price responses without large quantity movements, which helps explain the latest run‐up and subsequent collapse in the price of oil. Our analysis suggests that the variability of oil demand and supply shocks actually has decreased in the more recent past, preventing even larger oil price fluctuations than observed in the data. Copyright © 2012 John Wiley & Sons, Ltd.     

Slowdown of credit flows in Jordan in the wake of the global financial crisis: Supply or demand driven?

This paper estimates a disequilibrium model of credit supply and demand to evaluate the relative role of these factors in the slowdown of credit flows in the Jordanian economy in the wake of the global financial crisis. The empirical analysis suggests that the credit stagnation is mainly driven by the restricted credit supply amid tighter monetary policy conditions in Jordan relative to the United States, as evidenced by the widened interest differential between the Central Bank of Jordan (CBJ) re-discount and the U.S. Federal Reserve funds rates. Although it appears that demand side factors related to the slowdown of economic activity have also had an impact, their role has been relatively modest. The estimation results imply that economic policies targeted towards stimulating the supply of credit are likely to be a more effective tool for expanding credit flows relative to demand stimulating policies.     

The nonlinear effect of oil price shocks on financial stress: Evidence from China

This paper examines the nonlinear effects of different types of oil price shocks on China’s financial stress index (FSI). For this purpose, we use newly proposed framework by Ready (2018) to decompose oil prices into supply, demand and risk shocks. Then, we use a Markov regime-switching (MRS) model to investigate the nonlinear effects of these oil price shocks on China’s FSI. The empirical results show that the effects of three oil price shocks are nonlinear under different regimes. In particular, oil supply shocks mainly have a significantly positive effect on China’s FSI in the low-volatility state; demand shocks have negative effects on China’s FSI in different regimes, but this effect is larger in the low-volatility state; the effect of risk shocks on China’s FSI is the opposite, and it is positive in the high-volatility state but negative in the low-volatility state.