• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.63-71
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• 2020

In this paper, we propose the new time-varying coefficient GARCH-in-Mean model. The benefit of our model is to allow the risk-return parameter in the mean equation to vary over time. At the end of 2019 to the beginning of 2020, the world witnessed two shocking events: COVID-19 pandemic and 2020 oil price war. So, we decide to use the daily data from December 2, 2019 to May 29, 2020, which cover these two major events. The purpose of this study is to find the dynamic movement between risk and return in four major oil markets: Brent, West Texas Intermediate, Dubai, and Singapore Exchange, during COVID-19 pandemic and 2020 oil price war. For the European oil market, our model found a significant and positive risk-return relationship in Brent during March 26-April 21, 2020. For the North America oil market, our model found a significant positive risk return relationship in West Texas Intermediate (WTI) during March 12-May 8, 2020. For the Middle East oil market, we found a significant and positive risk-return relationship in Dubai during March 12-April 14, 2020. Lastly, for the South East Asia oil market, we found a significant positive risk return relationship in Singapore Exchange (SGX) from March 9-May 29, 2020.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.835-844
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• 2010

Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.417-424
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• 2013

The purpose of this study is to find out the effect of macroeconomic variables on the hotel profitability and suggest the reasonable way to handle them. To achieve this purpose, seven macroeconomic variables were used as an independent variable. These were the index of industrial production, West Texas Intermediate, the consumer price index, the unemployment rate, the money supply, the trade balance and the exchange rate. And ROA and ROE of total hotels were used as a dependant variable respectively. As the result of regression, it was found that the index of industrial production and the exchange rate had a significant and positive effect on ROA. And West Texas Intermediate, the consumer price index and the unemployment rate had a significant and negative effect on ROA. Also the consumer price index and the unemployment rate had a significant and negative effect on ROE and the exchange rate had a significant and positive effect on ROE. Through the analysis two key variables were found to be very important ones. These were the unemployment rate and the exchange rate. So the hotel managers need to emphasize on the good price of domestic hotel products and supply the various productions and services to the guests when the exchange rate is increased. But when the unemployment rate is increased, the hotel managers should consider to supply the middle price products with the hight price products.

• Journal of Distribution Science
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• v.12 no.3
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• pp.75-83
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• 2014

Purpose - The oil price affects company value, which is the present value of the expected cash flow, by affecting the discount rate and cash flow. This study examines the nonlinear relationships between oil price and stock price using the AlphaShares Chinese Volatility Index as the threshold. Research design, data, and methodology - Data comprise daily closing values of the Shanghai Stock Exchange Composite Index, Shenzhen Stock Exchange Composite Index, and Hang Seng Index of ChinaWest Texas Intermediate crude oil spot price and AlphaShares Chinese Volatility Index from May 25, 2007 to May 24, 2012. The Threshold Error Correction Model is used. Results - The results demonstrate different relationships between the stock price index and oil price under different investor sentiments; however, the stock price index and oil price could adjust to a long-term equilibrium the long-term causality tests between them were all significant. Conclusions - The relationship between the WTI and HANG SENG Index is more significant than the Shanghai Composites Index and Shenzhen Composite Index, when using the AlphaShares Chinese Volatility Index (ASC-VIX) as the investor sentiment variable and threshold.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• Environmental and Resource Economics Review
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• v.19 no.3
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• pp.485-508
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• 2010

This paper examines the long memory property and investigates cointegration in the dynamics of crude oil markets. For these purposes, we apply the joint ARMA-FIAPARCH model with structural break and the vector error correction model (VECM) to three daily crude oil prices: Brent, Dubai and West Texas Intermediate (WTI). In all crude oil markets, the property of long memory exists in their volatility, and the ARMA-FIAPARCH model adequately captures this long memory property. In addition, the results of the cointegration test and VECM estimation indicate a bi-directional relationship between returns and the conditional variance of crude oil prices. This finding implies that the dynamics of returns affect volatility, and vice versa. These findings can be utilized for improving the understanding of the dynamics of crude oil prices and forecasting market risk for buyers and sellers in crude oil markets.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.99-113
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• 2021

This study to verify the influence of the macroeconomic factors to sportainment industry and also to find the value of use. For this, 'Dow Jones Industrial Average (DJIA)', 'West Texas intermediate (WTI)', and 'Gold Price (GP)' were selected from macroeconomic factors, and the 'Stock Price' of NIKE and Adidas for sportainment industry factor. The transaction data for 20 years (5,285 trade days) were analyzed through a two-step extraction process. Durbin-Watson regression analysis was performed to prove the influence and predict. From these analyses, the first, the Macroeconomics factors were found to have a significant effect on the sportainment industry. The second, each different levels of regression equations were found by the time setting, the environmental characteristics of each time period, and mutual relation between factors. Finally, it was found that the regression equation between specific period can be used for the future prediction in sportainment industry.

• Journal of Korea Trade
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• v.27 no.5
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• pp.167-198
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• 2023

Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises. Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat. Findings - The results of the co-movement analysis were as follows: First, it was shown that the co-movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed. Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.352-361
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• 2013

The purpose of this study is to analyse the impact of macroeconomic and non-macroeconomic forces on the management performance of the air transport firms and offer the useful information to the managers. To conduct the regression analysis, eight macroeconomic and non-macroeconomic variables were selected individually as an independent variable. Macroeconomic variables were the return of corporate bond, West Texas Intermediate, the unemployment rate, the money supply, the trade balance, the won to USD exchange rate, the consumer price index and the index of industrial production. And non-macroeconomic variables were Taiwan earthquake, the Asian economic crisis, the 911 terrorist attacks in the US, the Iraq war, Beijing Olympic, the outbreak of a swine flu epidemic, the 1st presidential election and the 2nd presidential election. And ROA was selected as a dependent variable. As the result of analysis, it was found that the changing rates of won to USD exchange rate and consumer price index affected the changing rate of ROA significantly. And also as the result of analysing the impact of two significant macroeconomic variables and eight non-macroeconomic variables on the changing rate of ROA, it was found that the Asian economic crisis and the outbreak of a swine flu epidemic had a negative impact on it. Therefore managers should take note of a change in macroeconomic and non-macroeconomic variables carefully to improve the management performance.

• Environmental and Resource Economics Review
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• v.24 no.4
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• pp.657-677
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• 2015

We employ a 3 regime-threshold vector error correction models (TVECM) to investigate the nonlinear dynamic adjustments of three marker crude oil prices such as WTI (West Texas Intermediate), Brent and Dubai. Especially we deal with 3 combinations of oil prices including WTI-Brent, WTI-Dubai and Brent-Dubai in order to analyze the dynamic adjustments of the prices based on the effects of the price spreads among these crude oil prices. Our daily spot prices data run from 2001.1.3 to 2014.12.31. We found that each combination is cointegrated over the period. WTI had dropped significantly in 2010 which had affected the movements of the spreads. To accomodate this fact, we divide the period into two sub-periods: 2000.1.3-2009.12.31 and 2010.1.1-2014.12.31. It is found that each combination is cointegrated in both sub-periods. Moroever, in the first sub-period, all three oil prices are shown to follow nonlinear dynamic adjustments. In the second sub-period, however, TVECM is better than VECM(vector error correction model) for WTI-Dubai and Brent-Dubai while VECM performs better for WTI-Brent. The transaction costs are estimated to be reduced for the second sub-period for WTI-Dubai and Brent-Dubai compared to the first sub-period.