• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.351-364
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• 2023

Purpose - This study examines the effects of Dubai oil price and the volatility on the asymmetry of domestic gasoline price adjustment. Additionally, the study investigates the effects of "Altteul" gas-station and tax-cut policies on asymmetry. Design/methodology/approach - Firstly, the study calculates proxies for asymmetry and volatility of each window(every 3-month) by error-correction model and GARCH(1, 1) using daily domestic gas price and Dubai oil price from 2008/04/15 to 2022/12/31. Secondly, the study investigates the effects of the increasing rate of Dubai oil price, volatility, "Altteul" gas-station and tax-cut policies on asymmetry. The autoregressive distributed lag regression model is employed for estimations. Findings - The study finds that changes in the increasing rate of Dubai oil price and both types of volatility of Dubai oil price increase asymmetry. While "Altteul" gas-station and tax-cut policies decrease asymmetry. Additionally, the study fails to find that asymmetry in the Korean gasoline market in the estimation with total observations. Research implications or Originality - An increase in Dubai oil price volatility means an increase in cost uncertainty for gas-station owners. Since cost uncertainty is a kind of financial risk, the increase in volatility reinforces the asymmetry. The study provides supporting evidence for the idea.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.175-194
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• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.75-106
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• 2007

In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.