To do so we employ intra-day data – for the period August 1, 2003 to August 5, 2015 – on Brent Crude oil prices for the 1-month, 2-month and 3-month futures contracts, as well as, spot prices of Brent Crude Oil (ICE Futures Europe). As aforementioned, we focus on oilprice realized volatility, as this can be constructed by ultra-high frequency (i.e., intra-day data) and we concentrate on short- and medium-run forecasts; that is, forecasts for 1-day to 66-days ahead. Main findings indicate that futures-based forecasts (especially the models based on the 1- month futures contracts) can be accurate predictors of oilpricevolatility, significantly improving the forecasting accuracy of the no-change forecasts by more than 70%. Furthermore, based on the Model Confidence Set, the futures-based models that exhibit the highest predictive accuracy are those that either relax the proportionality restriction or both the unbiasedness assumption and the proportionality restriction. We further show that futures-based forecasts exhibit very high directional accuracy that significantly exceeds the 68% level for the full sample estimation and the 90% level during turbulent periods. On a final note, results are suggestive of the fact that futures-based forecasts are also very useful for oilpricevolatility predictions during crisis periods (e.g., Global Financial Crisis of 2007-09 and the oil collapse period of 2014-15).
This study examined the impact of oilpricevolatility on the fiscal behaviour of the government in Nigeria using data for the period 1970 to 2013. Modern econometric approaches/techniques were deployed. Our findings showed that real oil prices affected government expenditure dynamics and that there was a long run relationship between real oil prices and government spending, non-oil growth, inflation and discount rate differential. However, there was observed robustness of the estimates to different non-linear transformation of the real oil prices and inclusion of additional variables. And very instructive, the asymmetric effect of oilprice shocks on the government spending was not significant. Nevertheless, real oil prices had significant forecasting power for government spending over the long run horizons. By and large, we concluded that there was important linkages between oilprice and the government spending movement in Nigeria. Given that crude oil is an exhaustible resource, it portends dire implication on budget management in Nigeria in the future. Hence, the imperatives of diversifying the sources of foreign exchange inflow and revenue by reinvigorating the huge agricultural potentials which has been ignored since the discovery of oil in the 1970s as well as take advantage of its rich untapped solid minerals deposit. Also, there is the need for effective management of oil windfall in mitigating the effect(s) of shocks. Nigeria should also invest heavily in infrastructural development especially in the provision of steady power and good road networks in order to create the enabling environment for an enduring industrial development.
As most of the macroeconomic variables of Pakistan’s economy are affected by the oilpricevolatility, so, it is an important issue and need proper attention of authorities. Here are a few suggestions to protect the economy from vulnerability of oilpricevolatility. First, the dependency on imported crude oil should be lowered. Second, special incentives should be given to investors to attract the private investment in the energy sector, especially in coal, wind and solar energy. Third, strong political determination is needed to initiate medium and long term hydro power projects.
However, there are few previous empirical studies on oilpricevolatility and its impacts on international trade. To fill this gap, this paper empirically investigates whether the spikes, and in particular the fluctuations, in oil prices discourage international trade. To the best of our knowledge, this paper is the first attempt to examine the impacts of oilpricevolatility on international trade using a large panel data set with 84 countries from 1980 to 2008. We first use a structural VAR model with new identification assumptions proposed by Kilian (2009) to identify three different structural innovations in the crude oil market: oil supply shock, global aggregate demand shock, and oil-market-specific demand shock. We then show that the increase in oil prices due to oil supply shocks discourages trade while the increase in oil prices due to oil-specific demand has positive impacts on trade. The impacts of a positive global aggregate demand shock is negative but insignificant.
Contrary to the current practice that mainly considers stand-alone statistical loss functions, the aim of the paper is to assess oilpricevolatility forecasts based on objective-based evaluation criteria, given that different forecasting models may exhibit superior performance at different applications. To do so, we forecast implied and several intraday volatilities and we evaluate them based on financial decisions for which these forecasts are used. In this study we confine our interest on the use of such forecasts from financial investors. More specifically, we consider four well established trading strategies, which are based on volatility forecasts, namely (i) trading the implied volatility based on the implied volatility forecasts, (ii) trading implied volatility based on intraday volatility forecasts, (iii) trading straddles in the United States Oil Fund ETF and finally (iv) trading the United States Oil Fund ETF based on implied and intraday volatility forecasts. We evaluate the after-cost profitability of each forecasting model for 1-day up to 66-days ahead. Our results convincingly show that our forecasting framework is economically useful, since different models provide superior after-cost profits depending on the economic use of the volatility forecasts. Should investors evaluate the forecasting models based on statistical loss functions, then their financial decisions would be sub-optimal. Thus, we maintain that volatility forecasts should be evaluated based on their economic use, rather than statistical loss functions. Several robustness tests confirm these findings.
Huson Joher  considered the existence of a no linear relationship between oilpricevolatility and equity market uncertainty. He concludes that some sec- tors are responding quickly to volatility. John Elder et al.  studied the effects of oilprice uncertainty in Canada and their study exposes that ambiguity about oil prices incline to emphasize the negative reaction of output to positive oil shocks. Evangelia (2009) considered the connection between oil prices and eco- nomic movement in Greece during the period 1982 to 2008. Her research finds that there is a negative correlation between oil prices and economic activity during periods of rapid oilprice changes and high oilprice change volatility.
To test for a nonlinear effect of Crude oilpricevolatility on Nigeria’s Naira (₦)| per US dollar ($) exchange rate, this paper used the nonparametric method known as the BDS test. The BDS test developed by Brock, Dechert and Scheinkman (1987) (Brock, 1987). To determine whether a nonlinear model is suitable for the data. According to (Brooks, 2008), the decision should come from the financial theory; nonlinear model should be used where financial theory suggests that the relationship between the variable requires a nonlinear model. Notwithstanding, linear vs. nonlinearity choice can be made partly on statistical grounds deciding whether a linear specification is sufficient to describe all of the most important features of the data at hand. Although there are quite some tests for detecting a nonlinear pattern in time series data for researchers. According to (Zivot and Wang, 2006) BDS is unarguably the most popular test for nonlinearity. The econometric specification of the test can be expressed below as 𝐵𝐷𝑆 𝑚.𝑀 (𝑟) = √𝑀 𝐶 𝑚 (𝑟) −𝐶1
Since the development of the Diebold and Yilmaz (2009) spillover index and the Baker et al. (2016) economic policy uncertainty (EPU), many studies have assessed the relationship between the latter and oil prices/volatility (Antonakakis et al., 2014; Kang et al., 2017). Others have examined the predictive information of EPU on oilprice/volatility forecasts (Bekiros et al., 2015; Degiannakis and Filis, 2017, 2018). Findings suggest that EPU transmits spillover effects to the oil market and contains predictive information.
The empirical results show that the dynamic conditional correlation (DCC) and the bivariate AIGARCH (1, 1) model is appropriate in evaluating the relationship of the Thailand’s and the Malaysian’s stock markets with two factors of gold price and oilprice markets. The empirical result also indicates that the Thailand’s and the Malaysian’s stock markets is a positive relation. The average estimation value of correlation coefficient equals to 0.4732, which implies that the two stock markets is synchronized influence. Besides, the empirical result also shows that the Thailand’s and the Malaysian’s stock markets have an asymmetrical effect. The return volatility of the Thailand and the Malaysian stock markets receives the influence of the positive and negative values of the gold price and the oilpricevolatility rates.
In the events of rising real exchange rate volatility caused by real oilpricevolatility, the country’s trade balance can be affected. If real exchange rate volatility adversely affects both exports and imports, the trade balance will be improved when the size of the impact of volatility on exports is relatively smaller than the size of the impact of volatility on imports. Otherwise, the trade balance will be harmed. Even though the central bank can implement sound monetary policy measures to stabilize some major currencies, such as the US dollar, Japanese yen, and Euro currency, fluctuations of nominal oilprice cannot be controlled. Therefore, it seems necessary that policy makers should encourage firms to rely more on new energy (hydroelectric and wind power) so that crude oilprice will not be the main cause of real exchange rate volatility. In addition, some measures that will enhance competitiveness of exporting firms may deem necessary. Encouraging energy efficiency instead of energy
The goal of this study is to empirically estimate a model that helps to explain the behaviour of stock pricevolatility, movements in oil prices and real exchange rates in Nigeria using quarterly data from 1990 to 2012. Statistical and econometric techniques such as the Error Correction Mechanism (ECM) and the Bi-variate GARCH model were used to test for the relationships and to check if volatility in oil prices are transmitted to stock prices in Nigeria. The study showed that oilpricevolatility generates and stimulates stock prices volatility in Nigeria. The authors recommended that the excess crude oil revenues should be transform into physical capital and infrastructure rather than distribute the windfalls to the state and local government, a situation that ensures easy transmission of oil prices into the Nigerian economy.
Available online: https://pen2print.org/index.php/ijr/ P a g e | 815 Initial analysis of data: The time plot in fig 1 shows the original series. The time plot of crude oilprice in Nigeria (in USD/barrel) from 1997 to 2014, which is a graph that describes a point moving with the passage of time, the time plot shows some sudden changes, particularly the big shut in 2008. The volatility in oil prices is ordinarily quite high because the underlying demand and supply curves are so inelastic. Nothing unusual about the time plot and there appears to be no need to do any data adjustment, the data are clearly non-stationary as the series wanders up and down for long periods. Consequently, we will take a first difference of the data to make it stationary. The Fig 2 is the correlogram (ACF and PACF) of the crude oil price’s the data series before differencing. Autocorrelation and Partial Autocorrelation function plot can be used to detect non randomness in data and also to identify an appropriate time series model if they are not stationary. Fig 2 shows that the autocorrelation function decays slowly to zero which means the time series is non-stationary. These clearly reveal that non stationarity is inherent in the data.
Impact of commodity price risk on stock return remains an important forecasting parameters across stock markets of developed and emerging markets. In recent times the subdued oilprice poses a challenge to the economic imbalance among oil producing countries, and thus non-oil diversification has been adopted as an economic solution. Amongst the GCC countries, the intensity of non-oil diversification has been found to be higher in the UAE which prompted to conduct a separate study of impact of oilpricevolatility on stock returns of Abu Dhabi Securities Market General Index and various sectoral indices. This study examines whether UAE stock returns are still associated with changes in oilprice as reported in earlier research despite significant improvements in non-oil sector GDP contributions. The empirical assessment is based on weekly returns of the Abu Dhabi Stock Market General Index and four sectoral indices, namely, banking, industrial, energy and real estate in relation to variations in weekly WTI prices for the period between 1 st week of 2012 to 29 th week of 2019, i.e., for a period of 392 weeks applying Vector Error Correction model and Granger Causality test. It is found that there exists both long run and short run association between oilpricevolatility and stock return except model misspecification in respect of industrial and energy sectors arising out of serial correlation. Two lagged weekly oilprice movements are found to be strong explanatory variables of stock returns.
Some studies distinguish price uncertainty from mere price change, assuming that price uncertainty has specific effects on the economy. Ferderer (1996) calculates monthly oilpricevolatility as a standard deviation of daily price changes and argues that volatility has an explanatory power that can estimate fluctuations in U.S. economic output. Ahmed and Wadud (2011), on the other hand, employ an Exponential General Auto Regression Conditional Heteroscadasticity (EGARCH) model to estimate monthly oilpricevolatility, apply the SVAR model to 1986 to 2009 monthly data, and thus analyze the effects of the oilprice shock on Malaysia’ s industry. They suggest that oilpricevolatility negatively affects Malaysian industrial production. Notably, they point out that oilpricevolatility lowers price levels over the long term, and the Malaysian authorities respond to this with an expansionary monetary policy to stimulate the economy. The Malaysian case is an example of how a government responds to the effects of an international oilprice shock. In general, developing countries like Nigeria, as opposed to industrialized countries, have only limited financial tools to implement financial policy, so it is worth looking at how the monetary authority of such a resource rich country responds to an international oilprice shock.
Oil is becoming as an important determinant which affects the macroeconomic activities in unusual patterns among various parts of the world particularly since the first oil crisis in 1973. Also Petroleum products are recognized to be the essential source of energy and power throughout the world and gaining massive importance as a tool for survival and security of developed nations. The research study targets to explore the impact of oilprice and its volatility on CPI in case of Pakistan from the period 1980:M1 to 2014:M12. In this study we used the financial time series econometrics techniques; first applied the Box-Cox transformation on the data which suggested log transformation is required for all series. As data used will be monthly, Beaulieu and Miron (1992) seasonal unit root test is used to test stationarity of the data. All variables hold unit root at zero frequency and become stationary at first difference. Further to confirm if co-integration relationship exists between the variables we have estimated Engle and Granger (1987) two-step method. And finally Bivariate EGARCH model is applied to scrutinize the impact of oilpricevolatility on CPI. This model is estimated by using Maximum Likelihood Method proposed by Bollerslev and Wooldridge (1992). The results of Bivariate EGARCH model concluded that positive relationship between oil prices and CPI. We have also found the asymmetric impact of news on the change in consumer price index. In case of Pakistan, it is positive and significant statistically; which suggests that positive news tends to intensify the CPI volatility more than the negative news.
A study that is quite distinct is this of Efimova and Serletis (2014). Similar to the previous studies, they also use oil spot prices to model and forecast the 1-day ahead oil conditional volatility using univariate GARCH-type models, as well as, multivariate models such as BEKK, DCC and VARMA-GARCH. Nevertheless, it is the first paper to consider the inclusion of an additional asset class in order to assess if this yields better forecasts for the oilpricevolatility. More specifically, all previous papers which have estimated multivariate models have considered prices only from other energy markets (e.g. heating oil, gasoline, etc). By contrast, Efimova and Serletis (2014) include the S&P500 daily returns to their models. Their findings corroborate with these of the previous literature, suggesting that the univariate models are able to produce more accurate forecasts and that the inclusion of the S&P500 daily returns did not produce better forecasts.
Throughout history the new technologies and discoveries revolutionized the way we live. The discovery, the oil, has been critical for society, becoming the world’s most profitable and essential industry transforming itself from domestic to international business. The aim of this paper, above all is to analyze the role of oil and its pricevolatility in world economy. The ongoing changes and transformations in world oil industry tend to have a great effect not only on the oil- importing countries but also on oil-exporting nations. The demand or supply-triggered oilpricevolatility differ in its effects to world economic activity. Although it may have different effect for the oil importing nations in comparison to oil exporting nations, still inflationary pressure may be a common feature. A number of points relevant to the study are put forward highlighting pros and cons of issues discussed. The paper also elaborates the environmental concerns, deriving from the increase of oil consumption and the necessity (globally) to increase efforts in finding a decent,(environmentally friendly) replacement for oil.
Abraham (2015) used quarterly data and adopted the GARCH model as well as a multivariate VAR analysis to investigate the impact of oilprice shocks on the Nigerian economy. The impulse response functions show that oilprice shocks have immediate and prolonged effect on all the macroeconomic variables considered. He concluded that oilprice shocks have a direct impact on real GDP, total monetary assets and credit to private sector and as such urgent and serious efforts should be made to cut back on government expenditure, increase the tax base, diversify the economy and improve the overall efficiency and scope of other existing non-oil revenue sources, so as to ameliorate the impact of falling oil prices. This implies that oil revenue constitutes a greater percentage of the total output in Nigeria. Therefore sufficient attention should be given to it. This result was inconsistent with the findings of Oriakhi & Iyoha (2010) who found an indirect causality running from oilpricevolatility to real GDP. Having employed a VAR model to a quarterly data he concluded that oilprice changes determine real exchange rate, real import and government expenditure level directly, but indirectly on real GDP, real money supply and inflation through the instrumentality of government expenditure. Therefore, the reverse causality between this two works could be as a result of the periods under consideration and possible structural changes and political changes in the system. Katsuya (2010) in assessing the Impact of OilPriceVolatility on Macroeconomic activity in Russia using the VAR model with a quarterly series spanning from 1994:Q1 to 2009:Q3, giving 63 observations found that the Russian economy is greatly vulnerable to oilprice changes. This is because a little change in it triggers reasonable changes on GDP and exchange rate both in the short and long run with a marginal increase in inflation only in the short run. He therefore recommends the needs to diversify by increasing foreign direct investment (FDI) and improve domestic investment.
Oilprice fluctuations have received important considerations for their presumed role on macroeconomic variables. Higher oil prices may reduce economic growth, generate stock exchange panics and produce inflation which eventually leads to monetary and financial instability. It will also lead to high interest rates and even a plunge into recession (Mckillop, 2004). Sharp increases in the international oil prices and the violet fluctuations of the exchange rate are generally regarded as the factors discouraging economic growth (Jin, 2008). A very good example is the period of the global financial crisis, the price of oil fell by about two thirds from its crest of $147.0 per barrel in July 2008 to $41.4 at end of December 2008. Before the crises, oilprice was high, exchange rate was stable but with the dawn of the global financial crisis (GFC) oilprice crashed and the exchange rate caved-in, depreciating by more than 20 per cent. Since oilpricevolatility directly affects the inflow of foreign exchange into the country, there is a need to investigate if it has direct impact on the Naira exchange rate volatility (Englama et al, 2010).
Licensed under Creative Common Page 325 respective contributions are very close to each other throughout the periods. It is observed here that the contributions of real GDP, exchange rate volatility and money supply to the forecast error variance in oilpricevolatility increase over the years while the contribution of inflation rate oscillates in the horizons (it increases from 0% in the first year to its peak of 4.96% in the fourth year and then decreases in the longer horizons The simple meaning of this is that exchange rate volatility has significant influence on oilpricevolatility than every other variables in the model in the short horizon, While real gross domestic product influences oilpricevolatility more in the model in the longer horizon.