Assessing for Time Variation in Oil Risk Premia: An Adcc-Garch-Capm Investigation (original) (raw)
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Keywords: Volatility spillover Oil market Stock markets Oil-importing and oil-exporting countries Portfolio and hedging implications Symmetric and asymmetric DCC-GARCH modelsJEL classification: F65 G11 A B S T R A C T 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.
OIL PRICES AND STOCK MARKET CORRELATION: A TIME-VARYING APPROACH
2013
This paper examines the influence of oil prices on stock market time-varying correlation. Five stock market indices from both oil-importing (US, UK and Germany) and oilexporting economies (Canada and Norway) are considered for the period 1988-2011. The findings from the DCC-GARCH framework suggest that the effects of oil price changes on stock market correlation are not constant over time and they depend on the status of the economy, i.e. whether it is oil-importing or oil-exporting. In addition, utilising the identification of oil price shocks in [1], [2] and [3] it is found that the aggregate demand shocks and precautionary demand shocks tend to exercise a negative effect on stock market correlation, whereas no effects from the supply-side oil price shocks can be reported. These findings have important implications for international portfolio diversifications and risk management.
An Introduction to Oil Market Volatility Analysis
Modeling and forecasting crude oil price volatility is crucial in many financial and investment applications. The main purpose of this paper is to review and assess the current state of oil market volatility knowledge. It highlights the properties and characteristics of the oil price volatility that models seek to capture, and discuss the different modeling approaches to oil price volatility. Asymmetric response to price change, persistence and mean reversion, structural breaks, and possible market spillover of volatility are discussed. To complement the discussion, WTI futures price data is used to illustrate these properties using non-parametric and conditional modeling methods. The GARCH-type models usually applied in the oil price volatility literature are also explored. We additionally examine the exogenous factors that may influence volatility in the oil markets.
Modeling Price Volatility of Nigerian Crude Oil Markets Using GARCH Model: 1987-2017
Volatility and the risk-return trade off of crude oil or crude oil market participation is essential to National Investment, decision making, marketing, and the determination of the financial strength of Nations among other things. Therefore, this research study was targeted at modeling price volatility and the risk-return related to crude oil export in Nigerian crude oil market using the first order asymmetric and symmetric univariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family model in three distributional assumptions namely, Normal, student's-t and generalized error distribution. To achieve this target, three objectives with three research questions and two hypotheses were raised for the study. The data for the study was extracted from the Central Bank of Nigeria online statistical database starting from January, 1987 to June, and 2017. The results from the statistical analysis reveal that the markets were optimistic of their investment and other trade related activities. Sequel to that, there were high probabilities of gains than losses. Although, the variables use in these markets were extremely volatiles and shows evidence there exists positive risk first-rated meaning that investments or investors deserved rewards for holding risky assets. In estimation, first order symmetric GARCH model (GARCH, (1,1) in student's-t error assumption gave a better fit than the first order Asymmetric GARCH model (EGARCH (1,1)) in Normal error distributional assumptions. However, the selected models were subjected to several diagnostic test such as ARCH effect test, test for serial correlation and QQ-plot in order to validate their fitness which was confirmed to be appropriate. And recommendations were made to the Government to look for new ways to diversify the economy from total dependence on oil to non-crude oil such as agriculture, manufacturing and mining sector. For investors or marketers in this markets, they were advice to be mindful in trading in a highly volatile period especially when there is evidence of high standard deviation in the descriptive statistic of the return series and in modeling volatility of price return of certain micro/ macroeconomic variable the leverage effect of such variable should be properly estimated using asymmetric GARCH model.
Environmental Science and Pollution Research, 2022
The interaction between oil and stock market returns is one of the most important relationships that have a significant influence on the economy of any country all over the world. Therefore, this paper investigates the impact of crude oil prices on the Chinese stock market and selected industries by using the VAR-DCC-GARCH model over the period from December 26, 2001, to April 30, 2019. The empirical results show that the impact of Brent crude oil prices on the Shanghai Composite Index and selected industries is significant. However, there are some variations in these relationships and the degree of influence on each differs during different sample periods. Brent crude oil prices exert substantial influence on some specific industries, like mining, chemical, nonferrous metals, and steel. Whereas, the volatility spillover effect of Brent crude oil prices is stronger within the mining, chemical, steel, nonferrous metal, building materials, building decoration, electrical equipment, electrical equipment, textile and garment, light manufacturing, public utility, and transportation industries than within other industries. When oil prices change abruptly, the risk of spillover impacts of oil prices on stock markets will also increase. In conclusion, the impact of Brent crude oil prices on the Chinese stock market is generally positive. Furthermore, the subsequent volatility of Chinese stock market prices will, in turn, influence the volatility spillover of Brent crude oil prices on the indexes. The result is an ongoing back and forth of changes in price volatilities.