U.S. stock returns and oil prices: The tale from daily data and the 2008–2009 financial crisis

Using daily data from January 1999 to December 2011, we examine U.S. stock returns (S&P 500, Dow Jones, NASDAQ, and Russell 2000) based on a wide range of information, including equity VIX volatility, inflation expectations, interest rates, gold prices, and the USD/Euro exchange rate. The focus is on oil price returns, which have been previously found to exert mostly negative effects on U.S. stock returns. Identifying the crisis of 2008–2009 as a significant period of economic contraction and subsequent “recovery”, we check the stability of the stock-oil relationship by GARCH and MGARCH-DCC models. Prior to the financial crisis, stock returns are slightly (negatively) affected by oil prices and by the USD/Euro. For the subsample of mid-2009 onwards, however, stock returns are positively affected by oil prices and a weaker USD/Euro. As with inflation expectations, we interpret these findings as U.S. stocks responding positively to expectations of recovery worldwide. Our proposed explanation is due to the changing correlation between stock markets and oil, either by standard GARCH models or by MGARCH-DCC models allowing the implied correlation to vary over time.     

Oil price uncertainty and U.S. employment growth

This paper studies the effects of oil price changes on U.S. aggregate and sectoral employment growth in the presence of time-varying oil price uncertainty. We estimate a bivariate GARCH-in-Mean VAR model using U.S. monthly data of oil prices and employment growth for the period 1974 m2:2018 m11. Based on the results, we show that an increase in oil prices reduces total employment growth and that in most private sectors, but the public sector is largely unaffected. The effects on employment growth in various ”hub” sectors are also different. Furthermore, employment growths at both aggregate and disaggregate levels respond asymmetrically to positive and negative oil price shocks, which could possibly be attributed to oil price uncertainty. This asymmetric impact is more evident when the model is estimated on the entire sample than on the 1970s sample, implying that the role of oil price uncertainty in accounting for variations in employment growth can differ over time. These findings underline the empirical relevance of oil price uncertainty for the U.S. labor market dynamics.     

Impact of proved reserves on stock returns of U.S. oil and gas corporations using firm-level data

In this paper we investigate the impact of changes in proved reserves on U.S. stock returns using firm level data of the largest U.S. oil and gas companies. The selected sample covers the period 2009 to 2018 incorporating the recent episode of the shale oil and gas revolution. In contrast to previous studies, our results show that changes in proved oil and gas reserves have no significant effect on stock returns. We also give evidence of the impact of reserves on financial returns being dependent on the level of oil prices. Since oil prices fell abruptly after 2014, we show a significantly lower effect of oil reserves in stock returns in this subperiod and, thus, partly explain the overall insignificant effect.     

The effects of terrorism and war on the oil price–stock index relationship

The effects war and terrorism have on the covariance between oil prices and the indices of four major stock markets – the American S&P500, the European DAX, CAC40 and FTSE100 – using non-linear BEKK–GARCH type models are investigated. The findings indicate that the covariance between stock and oil returns is affected by war. A tentative explanation is that the two wars examined here predispose investors and market agents for more profound and longer lasting effects on global markets. On the other hand, terrorist incidents that are one-off unanticipated security shocks, only the co-movement between CAC40, DAX and oil returns is affected and no significant impact is observed in the relationship between the S&P500, FTSE100 and oil returns. This difference in the reaction may tentatively be interpreted as indicating that the latter are more efficient in absorbing the impact of terrorist attacks.     

The causal nexus between oil prices and equity market in the U.S.: A regime switching model

The aim of this paper is to analyse the causal link between monthly oil futures price changes and a sub-grouping of S&P 500 stock index changes. The causal linkage between oil and stock markets is modelled using a vector autoregressive model with time-varying parameters so as to reflect changes in Granger causality over time. A Markov switching vector autoregressive (MS-VAR) model, in which causal link between the series is stochastic and governed by an unobservable Markov chain, is used for inferring time-varying causality. Although we do not find any lead–lag type Granger causality, the results based on the MS-VAR model clearly show that oil futures price has strong regime prediction power for a sub-grouping of S&P 500 stock index during various sub-periods in the sample, while there is a weak evidence for the regime prediction power of a sub-grouping of S&P 500 stock indexes. The regime-prediction non-causality tests on the MS-VAR model show that both variables are useful for making inference about the regime process and that the evidence on regime-prediction causality is primarily found in the equation describing a sub-grouping of S&P 500 stock market returns. The evidence from the conditional non-causality tests shows that past information on the other series fails to improve the one step ahead prediction for both oil futures and stock returns.     

Oil sensitivity and its asymmetric impact on the stock market

We develop a two-step methodology to facilitate an examination of the impact of oil shocks on stock returns. Oil price volatility is monitored in this study through the use of a regime-switching model, with the presence of jumps subsequently being taken into consideration to examine the asymmetric effects of oil prices on stock returns. Our analysis provides quite conclusive results based upon the use of a regime-switching model with consideration of jumps; that is, when there are significant fluctuations in oil prices (West Texas Intermediate; WTI), the resultant unexpected asymmetric price changes lead to negative impacts on S&P 500 returns. However, the same result does not hold in a regime of lower oil price fluctuations. We therefore suggest that the achievement of a well diversified portfolio should involve the consideration of oil price shocks, which, as a consequence, should also help to improve the accuracy of hedging against oil price risks.     

Armed conflicts in the Middle East and international oil company returns

We investigate the effect of armed conflicts in the Middle East, which are representative geopolitical events, from an event study perspective. Specifically, we analyze the impact of conflicts in the Middle East on the stock returns of international oil companies in the countries engaged in the conflicts. We examine the stock returns of the top nineteen oil companies in nine countries during four conflicts from 1990 to 2011. By applying the market model of the event study methodology, we find that the abnormal returns of the companies in countries that did participate in the conflicts were higher than those of the companies in countries that did not participate. Furthermore, among the companies in countries that participated, those in the U.S. had higher stock returns than those outside the U.S. These findings suggest that political factors, such as military engagements, can have a statistically significant impact on oil company returns.     

Jump dynamics and volatility: Oil and the stock markets

Our study distinguishes itself from the prior studies within the oil and financial literature by not only examining the asymmetric effects of oil prices on stock returns, but also exploring the importance of structure changes in this dependency relationship. We retrieve daily data on S&P 500 and West Texas Intermediate (WTI) oil transactions covering the period from 1 January 1992 to 7 November 2006, and then transform the available data into daily returns. In contrast to the extant literature, in this study, consideration of expected, unexpected and negative unexpected oil price fluctuations is incorporated into the model of stock returns; we also focus on the ways in which oil price volatility, as opposed to general macroeconomic variables, can influence the stock market. We go on to implement the ARJI (Autoregressive Conditional Jump Intensity) model with structure changes, from which we conclude that high fluctuations in oil prices have asymmetric unexpected impacts on S&P 500 returns.     

Crude oil moments and PNG stock returns

We examine the risk-neutral moments of crude oil and their relationship to stock returns in the Petroleum and Natural Gas (PNG) industry. We find substantial overlaps in the association between returns and S&P 500- and crude oil higher moments. Net of these overlaps, PNG stocks share a significant negative relationship with crude volatility and positive relationships with crude skewness and kurtosis. Large cap stocks and those with a history of hedging exhibit negative loadings on crude volatility. However, after controlling for S&P 500- and crude oil returns and their risk-neutral moments, there is little evidence that PNG stocks systematically and significantly price either S&P 500- or crude oil volatility. We document a weak pricing of crude skewness, but find no evidence for the pricing of the implied higher moments of market returns.     

Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

This paper investigates the role of permanent and transitory shocks, within the framework of common cycles and common trends, in explaining stock and oil prices. We perform a multivariate variance decomposition analysis of monthly data on the West Texas Intermediate (WTI) oil price and the S&P 500. The dataset used in the study spans a long period of 150 years and therefore contains a rich history to examine both the short- and long-run comovement properties of oil and stock prices. Given that the oil and stock markets might comove both in the short- and long-run, it is of interest to see the relative impacts of transitory and permanent shocks on both variables. We find that (log) oil price and (log) S&P 500 share a common stochastic trend for our full sample of September 1859 to July 2015, but a common cycle only exists during the post-WW II period. Full and post-WW II samples have quite different common feature estimates in terms of the impact of permanent and transitory shocks as measured by the impulse responses and forecast error variance decompositions. We also find that in the short-run oil is driven mostly by cycles (transitory shocks) and stock market is mostly driven by permanent shocks. But, permanent shocks dominate in the long-run.     

Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?

This paper investigates the inter-connectedness between WTI oil price returns and the returns of listed firms in the US energy sector. Specifically, we focus on the issue of whether firm-level idiosyncratic information matters. A generalised dynamic factor model is used to separate common components from idiosyncratic components in these energy stocks. Systemic connectedness is then estimated following Diebold and Yilmaz (2014). Our empirical results demonstrate the important role of industrial-level common information in understanding the oil–stock relationship. A number of interesting points include: the US energy sector is the net contributor to WTI price changes, but the effect is mainly driven by industrial-level common information; the oil and gas industry dominates other industries in the energy sector; the dynamic analysis shows that although idiosyncratic information is mostly independent of oil shocks, individual energy stock returns do respond to WTI price movements.     

Relationships between oil price shocks and stock market: An empirical analysis from China

This paper investigates the interactive relationships between oil price shocks and Chinese stock market using multivariate vector auto-regression. Oil price shocks do not show statistically significant impact on the real stock returns of most Chinese stock market indices, except for manufacturing index and some oil companies. Some “important” oil price shocks depress oil company stock prices. Increase in oil volatility may increase the speculations in mining index and petrochemicals index, which raise their stock returns. Both the world oil price shocks and China oil price shocks can explain much more than interest rates for manufacturing index.     

Which oil shocks really matter in equity markets?

This paper examines the relationship between structural oil shocks and US equity markets. The recent oil shock decomposition of Ready (2018) is reconsidered and refined, providing a clearer delineation between shocks to equity market discount rates and aggregate demand, leading to an oil shock specification which attributes substantially more explanatory power to the latter in explaining equity market variation. Providing links with the literature dating back to Kilian and Park (2009), an explicit role is given to precautionary demand shocks using an independent measure constructed from oil futures data, reducing the role of the supply shocks obtained as the final residual in the recursive identification scheme. In an extended sample that allows an analysis of the oil/equity market relationship since the global financial crisis, the modified aggregate demand shocks have approximately twice as much explanatory power for stock return variation than the demand shocks of Ready (2018). The importance of these shocks in driving oil price changes and equity market volatility has only increased since the financial crisis, with the role of supply shocks diminishing. Once these demand effects are accounted for, there is little relationship between precautionary demand shocks and equity returns, in contrast to the existing literature.     

Risk appetite and oil prices

We demonstrate that oil prices and their volatility are no longer determined solely by real economic shocks to supply of and demand for oil, but are also driven by shocks originating in the economic uncertainty and risk appetite of investors that prevail in the equity market. The contribution of the latter factor has become particularly remarkable since the mid-2000s. To establish these results, we dismantle the squared VIX index, determined in the S&P 500 options market, into the conditional variance in stock returns (to proxy for economic uncertainty) and the equity variance risk premium (to proxy for risk appetite). Using threshold-GARCH, structural vector auto regression and causality models, we provide evidence about the link between risk appetite, oil price returns and volatility. Furthermore, investors' appetite for risk drives changes in the OVX, which measures perceptions about future oil volatility, but not vice versa. Our results provide a better understanding of the relationship between oil, the VIX and its two proposed components. In particular, we show that changes in the risk appetite of investors are an important determinant not only for the price of equities but also for that of the most important energy resource – oil.     

Does the source of oil price shocks matter for South African stock returns? A structural VAR approach

In this paper, we investigate the dynamic relationship between different oil price shocks and the South African stock market using a sign restriction structural VAR approach for the period 1973:01 to 2011:07. The results show that for an oil-importing country like South Africa, stock returns only increase with oil prices when global economic activity improves. In response to oil supply shocks and speculative demand shocks, stock returns and the real price of oil move in opposite directions. The analysis of the variance decomposition shows that the oil supply shock contributes more to the variability in real stock prices. The main conclusion is that different oil price shocks affect stock returns differently and policy makers and investors should always consider the source of the shock before implementing a policy and making investment decisions.     

Does the S&P500 index lead the crude oil dynamics? A complexity-based approach

Taking the complex property of nonlinear feedback connectivity into consideration, the goal of this paper is to apprehend the interdependences between the financial and energy sectors. Our contribution is both theoretical and methodological. We conduct a multivariate analysis employing nonlinear tools, namely the Partial Transfer Entropy and the Asymmetric Mackey-Glass causality test. In particular, we build a system comprising the petroleum complex (crude oil, gasoline and heating oil), the S&P500 index and the 1-month futures-spot spread for crude oil. By adopting a rolling-window approach, we observe a persistent lead-lag relationship between the S&P500 index and the market participants' expectations for crude oil, from 2004 to 2009. Depending on the bubble period in the stock market, it appears that the resulting coupling becomes subject to the deterioration of global economic activity, induced by large common shocks.     

Oil price shocks,competition, and oil & gas stock returns — Global evidence

Extant literature suggests that oil price shocks have a strong impact on the macroeconomy and the stock market. However, relatively less is known about the effect of country-level determinants, competition, and asymmetrical relationship in affecting the oil & gas stock return at the firm-level. Using a comprehensive firm-level monthly data from 70 countries spanning 1983 to 2014, we find: (i) macroeconomic stress negatively impact firm-level returns; (ii) oil price shocks positively impact firm-level returns; (iii) firms located in high oil producing countries are more sensitive to global uncertainty and oil price shocks; (iv) firms located in non-competitive industries are less sensitive to oil price shocks; and (v) firms located in non-competitive industries are less affected by the drop in oil price, as compared to firms that are located in highly competitive industries. Our results remain qualitatively similar using a battery of robustness checks.     

Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States

The relationship between oil and stock markets is a hot topic, but little research has focused on the time-varying asymmetric volatility spillover in a quantitative manner. In this study, we use a new spillover directional measure and asymmetric spillover measures to investigate the dynamic asymmetric volatility spillover between oil and stock markets during the period of 2007 to 2016. Using the intra-day data of WTI future prices, the S&P 500 index, and the Shanghai stock market composite index, we find that there exists an asymmetric spillover effect between the oil market and stock markets and that bad volatility spillovers dominate good volatility spillovers for most of the sampling period. In addition, participants are more pessimistic about the oil market than they are about the stock market. We further investigate the presence of asymmetric response to volatility shocks using the asymmetric generalized dynamic conditional correlation (AG-DCC) model; the results also show strong evidence of asymmetries in volatility shocks between the oil and stock markets due to bad volatility.     

Forecasting oil and stock returns with a Qual VAR using over 150 years off data

The extant literature suggests that oil price, stock price and economic activity are all endogenous and the linkages between these variables are nonlinear. Against this backdrop, the objective of this paper is to use a Qualitative Vector Autoregressive (Qual VAR) to forecast (West Texas Intermediate) oil and (S&P500) stock returns over a monthly period of 1884:09 to 2015:08, using an in-sample period of 1859:10–1884:08. Given that there is no data on economic activity at monthly frequency dating as far back as 1859:09, we measure the same using the NBER recession dummies, which in turn, can be easily accommodated in a Qual VAR as an endogenous variable. In addition, the Qual VAR is inherently a nonlinear model as it allows the oil and stock returns to behave as nonlinear functions of their own past values around business cycle turning points. Our results show that, for both oil and stock returns, the Qual VAR model outperforms the random walk model (in a statistically significant way) at all the forecasting horizons considered, i.e., one- to twelve-months-ahead. In addition, the Qual VAR model, also outperforms the AR and VAR models (in a statistically significant manner) at long-run horizons for oil returns, and short- to medium-run horizons for stock returns.     

How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks

This paper focuses on how explicit structural shocks that characterize the endogenous character of international oil price change affect the output volatility of the U.S. crude oil and natural gas mining industries. To this end, we employ a modified structural vector autoregressive model (SVAR) to decompose real oil-price changes into four components: U.S. supply shocks, non-U.S. supply shocks, aggregate demand shocks, and oil-specific demand shocks mainly driven by precautionary demand. The results indicate that output volatility of the U.S. crude oil and natural gas mining industry has significantly negative responses to U.S. supply shocks, aggregate demand shocks, and oil-specific demand shocks, while lacks significant response to non-U.S. supply shocks. Variance decomposition and historical decomposition confirm that U.S. supply shocks occupy most explaining variations in output volatility among the four structural oil shocks. Moreover, the oil-specific demand shocks explain more variation than that of aggregate demand shocks for the crude oil mining industry, but the opposite is true for the natural gas mining industry.