Lagged Oil Price Research Articles

The impacts of oil price shocks and jumps on China's nonferrous metal markets

Existing studies have been concerned with the relationship between oil price and nonferrous metal markets. However, little attention has been paid to the impacts of lagged oil price shocks and volatility spillover effects of oil price jumps on China's nonferrous metal markets. This paper employs the ARMA-EGARCH-ARJI model to capture the volatility clustering and dynamic jumps in oil price fluctuation and the ARMA-EGARCH model to study the impacts of oil price changes on China's nonferrous metal markets. The results indicate that both the shocks and jumps of oil price affect China's nonferrous metal markets. First, the nonferrous metal markets are negatively affected by the expected shocks and unexpected positive shocks, while the impacts of unexpected negative shocks are positive on most nonferrous metal markets. Second, the impacts of oil price shocks are characterized by time-lag, and both the current and lagged oil price shocks affect the nonferrous metal markets. Third, oil price jumps lead to overreaction of volatility in nonferrous metal markets. This study not only contributes to advancing the existing literature, but also merits particular attention from both policy makers and market participants.

The Role of CAPM and Oil Prices to Analyze the Firm’s Stock Return

This paper attempts to analyze the applicability of CAPM and the effect of oil prices on firm’s stock in case of Pakistan. To examine this research objective, we use the yearly data of 148 listed firms over the period of 2006 to 2015. We employ three different estimation techniques, panel correlated standard error estimation (PCSE), Driscoll and Kraay (DK) estimation and common correlated effects pooled (CCEP), to analyze the relationship between oil variables and firm’s stock return. Moreover, we further estimate the variables by using robust estimation techniques to validate the empirical results. The estimations report the inapplicable of market premium in case of Pakistani firms. However, the oil price and lagged oil prices provides the evidence of negative and statistically significance in textile, sugar, cement, chemical and engineering sectors. On contrary, the oil prices and lagged oil prices have positive and significant impact on stock return in transportation and energy sectors. In conclusion, it is difficult to escape the conclusion that oil price has higher influence on stock returns as compared to the market premium and nearly all the manufacturing sectors are inversely affected by the oil price rise.

The Role of CAPM and Oil Prices to Analyze the Firm’s Stock Return

This paper attempts to analyze the applicability of CAPM and the effect of oil prices on firm’s stock in case of Pakistan. To examine this research objective, we use the yearly data of 148 listed firms over the period of 2006 to 2015. We employ three different estimation techniques, panel correlated standard error estimation (PCSE), Driscoll and Kraay (DK) estimation and common correlated effects pooled (CCEP), to analyze the relationship between oil variables and firm’s stock return. Moreover, we further estimate the variables by using robust estimation techniques to validate the empirical results. The estimations report the inapplicable of market premium in case of Pakistani firms. However, the oil price and lagged oil prices provides the evidence of negative and statistically significance in textile, sugar, cement, chemical and engineering sectors. On contrary, the oil prices and lagged oil prices have positive and significant impact on stock return in transportation and energy sectors. In conclusion, it is difficult to escape the conclusion that oil price have higher influence on stock returns as compared to the market premium and nearly all the manufacturing sectors are inversely affected by the oil price rise.

Analysing volatility spillover between the oil market and the stock market in oil-importing and oil-exporting countries: Implications on portfolio management

This study analyses the volatility spillover between the oil market and the stock market of oil-importing and oil-exporting countries using daily data over the period from January 2010 to December 2016. The study also explores the portfolio and hedging implications based on dynamic conditional correlation (DCC) and corrected DCC (cDCC) GARCH models. For the analysis, we have used symmetric and asymmetric versions of DCC and cDCC models. Specifically, in the symmetric version of DCC and cDCC, the estimations are based on GARCH (1,1), and in the asymmetric version of DCC and cDDC, the estimations are based on GJR-GARCH (1,1), FIGARCH (1,1) and FIEGARCH (1,1) models, and for each case, we have explored the portfolio and hedging implications. Overall, the evidence indicates that oil-importing countries are severely affected by lagged oil price shocks, and there is less evidence of interdependence between stock markets for both oil-importing and oil-exporting countries. Further, we find that the lagged volatility in the oil market and stock market has a statistically significant impact on the current volatility in its respective markets. The results from the asymmetric analysis show that the magnitudes of the negative shocks are higher than those of the positive shocks. The overall results from portfolio optimization reveal that investors in oil-exporting countries should hold more oil assets in the portfolio to hedge the risk.

Panel evidence on the ability of oil returns to predict stock returns in the G7 area

Most studies on the ability of oil returns to predict stock returns use time series data for multiple countries. The data therefore have a panel structure. Yet, oddly enough, this structure is never really exploited in the estimation, but researchers tend to instead rely on country-by-country application of existing time series methods. This practice is wasteful, because it does not explore all available information. The present paper can be seen as a reaction to this. The purpose is to reevaluate the existing empirical evidence for the G7 countries using state-of-the-art panel data techniques. The results show that for the panel as a whole lagged oil price returns has a significantly negative effect on current stock returns. The evidence at the individual country level is less strong and varies depending on the extent to which the countries rely on oil import.

Oil subsidies and the risk exposure of oil-user stocks: Evidence from net oil producers

Using a sample of 828 oil-user firms from 14 net oil-producing countries spanning from January 2004 to December 2015, we show that stock returns of oil-user companies increase with lagged oil price returns and decrease with lagged oil price volatility. Furthermore, the evidence suggests that oil-user stocks operating in countries with larger fuel subsidies tend to be more exposed to oil returns but not oil volatility. Intuitively, when the oil price increases (decreases), oil-user stocks that operate in countries with larger oil subsidies gain (lose) more than oil-user stocks in countries with smaller fuel subsidies. However, both types of stocks experience losses when the oil market becomes more volatile, with no statistically significant difference between their losses. Our evidence implies a diversification benefit for international investors to reduce their exposure to oil risk. Our results are robust because of the use of alternative proxies, econometric methodologies, and model specifications.

Investments on the Norwegian Continental Shelf - an Empirical Analysis

Investments in oil and gas fields are regressed against variables on panel field-data from the start of oil and gas production on the Norwegian continental shelf in 1968 until 2016. Two alternative models track the observed investments aggregated across fields from 1970 until 2016 relatively accurately, except for the period 2012-2015. These years were marked by an almost world-wide recession in the aftermath of the financial crisis in 2008 and by the increase in production of shale-gas in the US. However when using data until 2010 in the estimation of the model, the fixed effect regression predicts rather accurately the development of aggregated across fields from 2011-2016. By using data only from 1995 until 2016 in the estimation of the fixed effect model the observed development after 2011 is also well tracked. The models imply rather strong and significant effect of the lagged oil price (Brent Blend) on investments. When data for the shorter period 1995-2016 is used, we get significant asymmetric price effects on investments, implying that an increase in the oil price has more positive effects on investments in periods with rising oil prices. In periods with declining prices the price history has a rather strong dampening impact on the effects of prices increases on investment. We also find strong and significant negative effects of lower expected remaining reserves on investments.

Impact of oil prices on firm stock return: industry-wise analysis

The study investigates the impact of oil prices on firm-level stock returns in case of Pakistan over the period 1998–2014, as this relationship is neglected by the previous literature. By using the panel data estimation, the results of full sample indicate significant positive effect of oil price changes on firm stock returns in the same period, whereas the lagged oil price changes have significant negative effect on firms’ stock return. Moreover, the industry-level analysis also confirms the similar findings; results indicate significant positive impact of oil price on firms’ stock return in full sample, textile, chemical and miscellaneous industry, while the lagged oil price changes negatively affect the stock returns of full sample and all the industries except tobacco, jute and vanaspati industries. The study confirms that rise in oil price transfers a positive signal in the stock market that boosts the firm-level stock returns in Pakistan. In contrast to the negative shocks, the stock returns are significantly affected by the positive oil price shocks.

Oil price effects over individual Portuguese stock returns

During the last decades, the world energy dependence increased significantly. Understanding how companies depend on oil prices is essential, especially for countries highly dependent or importers. The work intends to investigate the relationship between oil price changes and Portuguese listed companies’ returns. Using the generalized autoregressive conditional heteroskedasticity model, we conclude that nearly 20 % of the companies are significantly affected by oil prices, finding also evidence that these effects are asymmetric, depending on the company’s current situation in the market (result attributed to the lack of liquidity and the small number of firms included within the sample). There exists some differences among economic sectors in the way they are impacted by oil price changes, although not so much significant. The results show that the bigger the company, the higher the probability of being significantly affected by oil price changes. Results suggest that lagged oil price positive shocks increase Portuguese companies’ returns, by opposition to the current oil price change. Findings highlight the key role played by aggregate demand-side oil price shocks over the financial economic activity, showing sector and individual companies’ differences, thus inducing the possibility of results being highly dependent over the economic context faced by the country under analysis, that firms are more sensitive to oil prices when the equity market is busiest and that oil price increases affect companies returns in a negative way, but price decreases cause more positive than negative effects over company returns.

Oil prices and UK industry-level stock returns

In this article, we study whether the behaviour of oil prices can be used as a reliable predictor for the disaggregated industry-level stock market indices. We find strong evidence for the relevance of changes in oil price as a predictor for the returns of UK industry portfolios, while this relevance is heterogeneous across industries. In an out-of-sample framework, we find that both the contemporaneous and lagged oil price changes do predict UK industry stock market returns. The predictive power is more transient for the latter case, and mostly appearing after allowing for time variation in the relative performance. In addition, we find some evidence of asymmetry in the oil–stock price relationships.

Does crude oil price play an important role in explaining stock return behavior?

Employing the MS-ARJI-GJR-GARCH-X model, in which the parameters for the jump process, the asymmetric GARCH effect and the impacts of oil price shocks are regime-dependent, this paper analyzes the impact of crude oil price shock on stock return dynamics. Empirical results reveal three interesting findings. First, incorporating the asymmetric GARCH effect and the oil price shock can substantially improve fitting ability. Second, the GARCH and jump components show very different behaviors during turbulent and stable periods. Third, the effects of current and past oil price shocks differ. The conditional mean, mean of jump size and variance of jump size immediately respond to a current oil price shock. A one-period lagged oil price shock, no matter whether positive or negative, can affect the transition probability that the stock market will remain conditional in the next period. Moreover, the effects of lagged positive and negative shocks on transition probabilities are very different.

Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries

The paper investigates the time-varying correlation between stock market prices and oil prices for oil-importing and oil-exporting countries. A DCC-GARCH-GJR approach is employed to test the above hypothesis based on data from six countries; Oil-exporting: Canada, Mexico, Brazil and Oil-importing: USA, Germany, Netherlands. The contemporaneous correlation results show that i) although time-varying correlation does not differ for oil-importing and oil-exporting economies, ii) the correlation increases positively (negatively) in respond to important aggregate demand-side (precautionary demand) oil price shocks, which are caused due to global business cycle's fluctuations or world turmoil (i.e. wars). Supply-side oil price shocks do not influence the relationship of the two markets. The lagged correlation results show that oil prices exercise a negative effect in all stock markets, regardless the origin of the oil price shock. The only exception is the 2008 global financial crisis where the lagged oil prices exhibit a positive correlation with stock markets. Finally, we conclude that in periods of significant economic turmoil the oil market is not a “safe haven” for offering protection against stock market losses.

A comparative analysis of oil as a risk factor in Australian industry stock returns, 1980‐2006

Purpose – This paper aims to examine the impact of crude oil prices on Australian industry stock returns. With rising energy prices, it is important to consider oil as a pricing factor in asset pricing models.Design/methodology/approach – Multifactor static and dynamic models consider crude oil and other macroeconomic factors as pricing factors in industry excess returns from January 1980 to August 2006. The macroeconomic factors comprise the market portfolio, oil prices, exchange rates and the term premium. The industries consist of banking, diversified financials, energy, insurance, media, property trusts, materials, retailing and transportation.Findings – Oil prices are an important determinant of returns in the banking, energy, materials, retailing and transportation industries. The findings also suggest oil price movements are persistent. Nonetheless, the proportion of variation in excess returns explained by the contemporaneous and lagged oil prices appears to have declined during the sample period....

A Comparative Analysis of Oil as a Risk Factor in Australian Industry Stock Returns, 1980-2006

This paper uses a multifactor model to examine the role of crude oil as a pricing factor in Australian excess industry returns over the period January 1980 to August 2006. A dynamic model is also specified to provide insights into the relationship between the stock market and past oil price movements. The macroeconomic factors comprise the market portfolio, oil prices, exchange rates and the term premium. The nine industries include banking, diversified financials, energy, insurance, media, property trusts, materials, retailing and transportation. The results indicate that oil prices are an important determinant of returns in the banking, energy, materials, retailing and transportation industries. The findings also suggest that the effects of oil price movements are persistent - retail excess returns, for example, are negatively related to current and one and three-month lagged oil price changes. Nonetheless, the proportion of variation in excess returns explained by the contemporaneous and lagged oil prices appears to have declined during the sample period.