Search results

The purpose of this paper is to explore the dynamic influence of WTI crude oil returns on the stock returns of China’s traditional energy sectors, including oil and gas exploitation, coal mining and processing, petroleum processing and coking, electricity, heat production and supply and mining services.

Hong’s information spill-over test and the DP Granger causality test are applied to investigate the relationship between the two markets. Moreover, a rolling window is introduced into the above two tests to capture time-varying characteristics of the influence of WTI crude oil returns.

The empirical results indicate that, first, there exists significant bidirectional linear causality between WTI crude oil returns and China’s traditional energy sectoral stock returns, but the nonlinear causality appears weaker. Second, the influence of WTI crude oil returns on traditional energy sectoral stock returns has time-varying characteristics and industry heterogeneity both in the linear and nonlinear cases. Finally, the decline of WTI crude oil prices may strengthen its linear influence on the stock returns of traditional energy sectors, while the excessive rise of market values in traditional energy sectors may weaken the linear and nonlinear influence of WTI on them.

The general nexus between international crude oil market and China’s traditional energy stock market is explored both in the linear and nonlinear perspectives. In particular, the dynamic linear and nonlinear influence of WTI crude oil returns on China’s traditional energy sectoral stock returns and its industry heterogeneity are analysed in detail.

High inflation levels remain a challenge in macroeconomic stabilization policies among developing economies. Oil price is identified as an important driver of inflation. In the wake of high and unstable international oil prices, the question regarding the relationship between inflation and crude oil prices, and its implication for economic welfare has become a fundamental empirical issue.

This question is explored by estimating a non-linear autoregressive distribution lags (NARDL) model of inflation-oil nexus that examined the asymmetric response of inflation to oil price changes. The study then derived the welfare implication of the asymmetric responses, with implications for the petroleum pricing regime in Ghana.

The study found that inflation responds asymmetrically to oil prices in the long-run but not in the short-run. The welfare cost associated with the asymmetric response increases with increasing rate.

The findings of this study have some implications for petroleum product pricing in Ghana. Recently, Ghana has moved from regulating petroleum prices to the automatic adjustment system. By this policy, petroleum prices change in tandem with the crude oil prices and exchange rates on the international market. Whiles this policy might be comparatively efficient, the evidence of asymmetric response of inflation to changes in oil prices raises some issues about the welfare effect of the policy.

The paper contributes to the literature on the inflation-oil price nexus by investigating critical questions that remain puzzling. These questions include; Does inflation respond asymmetrically to the positive and negative shock of equal magnitude in oil prices? Does inflation response to the asymmetry changes in oil prices have any implications for the welfare of the country? Is the effect of oil price changes pernicious?

This chapter develops a no-arbitrage, futures equilibrium cost-of-carry model to demonstrate that the existence of cointegration between spot and futures prices in the New York Mercantile Exchange (NYMEX) crude oil market depends crucially on the time-series properties of the underlying model. In marked contrast to previous studies, the futures equilibrium model utilizes information contained in both the quality delivery option and convenience yield as a timing delivery option in the NYMEX contract. Econometric tests of the speculative efficiency hypothesis (also termed the “unbiasedness hypothesis”) are developed and common tests of this hypothesis examined. The empirical results overwhelming support the hypotheses that the NYMEX future price is an unbiased predictor of future spot prices and that no-arbitrage opportunities are available. The results also demonstrate why common tests of the speculative efficiency hypothesis and simple arbitrage models often reject one or both of these hypotheses.

While numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely exist. In order to conceal this void, the purpose of this paper is to investigate whether including OVX in the realized volatility (RV) models improve the accuracy of predictions.

At the empirical stage, the authors employ several measures to frame the RV of crude oil futures returns. In particular, the authors use three different range-based RV estimators recommended by Parkinson (1980), Rogers and Satchell (1991) and Alizadeh et al. (2002), respectively.

The findings reveal that the information content of crude OVX helps to provide more accurate volatility predictions in comparison to the base-line RV model which contains only historical oil volatilities. Besides, the forecast encompassing test further suggests that the modified RV model (when OVX is introduced in the base-line RV model) forecast encompasses the conventional RV forecast in majority of the cases.

Since forecasting oil price volatility plays a vital role in portfolio optimization, derivatives pricing, optimum asset allocation decisions and risk management, the findings of this study thus carry important implications for energy economists, investors and policymakers.

This paper adds to the existing literature, since it is one of the initial studies to explore whether OVX is informative about the realized variance of the US oil market returns. The findings recommend that the information content of oil implied volatilities should be taken into account when modeling the US oil market volatility. In addition, range-based measures should be utilized while estimating the RV.

The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times.

The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries.

The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

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.

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.

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.

Macroeconomic factors are important for multifactor asset pricing at the industry level. Apart from oil prices, the market portfolio is a significant pricing factor in all industry excess returns. Exchange rates are also an influential factor for excess returns in the banking and diversified financials industries, and the term premium as a proxy for future real activity is a priced factor in the energy, insurance and retailing industries.

While past studies have provided some evidence that oil prices constitute a source of systematic asset price risk and that exposure varies across industries, no recent work is known in the Australian context.