• International Area Studies Review
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• v.21 no.4
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• pp.19-39
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• 2017

The purpose of this paper is to investigate the impact of terms of trade and its volatility on real GDP and inflation. I estimate the linear and dynamic panel model including variables such as real GDP, inflation, terms of trade, capital stock, employment and education. The sample countries consist of OECD 26 countries and panel data ranges from 1990 to 2015. The empirical results show that terms of trade and its volatility do not affect the real GDP significantly. Even if the terms of trade has a negative relationship with real GDP, the magnitude of the estimated coefficients was very small. This result seems to be related with the industry structure and domestic demand structure of the member countries. On the other hand, terms of trade and its volatility have the significant impact on inflation. When the terms of trade and its volatility increase, the inflation increases.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.261-277
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• 2003

The Korean Stock Market Surveillance System is desinged to control the volatility of stocks by drawing investor's attention and suppressing disguised demand, when stocks run up so rapidly in short period of time. Yet the Surveillance System has not been under empirical examination about its role and evolved in line with the Price Limit System. This study looks at the security returns under surveillance designation for 1995 -2001 period. The results indicate that the volatility of stocks has not been affected after surveillance designation. The constraints against the disguised demand, however, seems to limit the security returns rather than volatilities. These findings raises a question about the role of The Korean Stock Market Surveillance System for the control of volatility. The Surveillance System needs to be examined thoroughly about its role, function, and its conditions. Otherwise, the shareholders with less information could be placed at a disadvantage. This paper suggests that the system should be amended in an effort to make the volatility of stocks under control.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6084-6091
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• 2014

This paper presents the GJR GARCH model (Glosten et. al, 1993) to analyze the influences of volume volatilities on price volatilities in the fishery market. For the analysis, this study used the monthly price and volume data of aquacultural flatfish in Jeju. As a result, empirical analysis suggested volatility clustering. The persistency parameter(${\lambda}$) was estimated to be approximately 1 in aquacultural flatfish. The results showed that there is a significant negative relationship between the conditional variance of supply and that of price for aquacultural flatfish. This means that the general law of supply is valid. Finally, the empirical analysis was that an asymmetric coefficient (${\gamma}$) of GJR GARCH model was negative (-). This means that the higher volatility of volume leads to lower price volatility. That is, it is useful to make government policies that can adjust the volume (stockpiling, stabilizing supply and demand).

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.606-608
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• 2003

경쟁적인 전력시장에서의 전력 가격은 전력에 대한 수요(Demand)와 공급(Supply)에 의한 시장원리에 의해 결정되기 때문에, 매 시각의 전력가격은 정해져 있지 않고 시장에 의해 결정된다. 이와 같이 전력가격이 가지는 가변성 및 불확실성(Volatility) 때문에 향후 전력 시장에 참여하는 사업자의 수입(Revenue) 및 수익 규모의 불확실성이 커지는 한편, 전력을 사용하는 소비자의 에너지비용도 그 불확실성이 매우 크게 증가하게 마련이다. 이러한 전력가격의 가변성(Volatility)과 불확실성(Uncertainty)이 대두됨에 따라, 외국의 경우, 가정용 부하와 같은 소규모 전력소비자를 제외한 대부분의 대규모 소비자(산업용, 상업용, 일반용 전력소비자)들은 전력가격이나 자신이 사용하고 있는 전력의 소비패턴과 같은 정보(Information)와 전략(Strategy)이 없는 경우, 예전에 비해 상당히 높은 전기요금을 지불해야 하는 상황에 처하게 되었다. 본 논문에서는 이러한 경쟁적 환경에서 직접부하제어자원을 활용하여 비용 및 위험을 관리하는 해외사례를 집중적으로 분석하고자 한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.266-272
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• 2011

Real Time Pricing(RTP) is used not only to stabilize the price volatility in electricity market, but to hedge the price risk for Load Serving Entity(LSE). This paper presents an efficient method to reduce the risk of the price volatility in real-time electricity market. For designing the RTP, load patterns of customer are calculated by applying the demand elasticity and customer's utility is also analyzed to compute the RTP revenue through the risk-attribute of the LSE. In the end, the distribution of the LSE's profits can be evaluated to lead the optimal RTP value, depending on the level of customer's participation. Results from the case study based on PJM data are reported to illustrate the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.212-217
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• 2020

The purpose of this study was to analyze the volatility and properties of a time series for tangerine prices in Jeju using the GARCH model of Bollerslev(1986). First, it was found that the time series for the rate of change in tangerine prices had a thicker tail rather than a normal distribution. At a significance level of 1%, the Jarque-Bera statistic led to a rejection of the null hypothesis that the distribution of the time series for the rate of change in tangerine prices is normally distributed. Second, the correlation between the time series was high based on the Ljung-Box Q statistic, which was statistically verified through the ARCH-LM test. Third, the results of the GARCH(1,1) model estimation showed statistically significant results at a significance level of 1%, except for the constant of the mean equation. The persistence parameter value of the variance equation was estimated to be close to 1, which means that there is a high possibility that a similar level of volatility will be present in the future. Finally, it is expected that the results of this study can be used as basic data to optimize the government's tangerine supply and demand control policy.

• Korea Trade Review
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• v.43 no.3
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• pp.169-198
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• 2018

The purpose of this paper is to investigate the impact of exchange rate volatility on the export of Korean machinery intermediate goods to East Asian countries using the export demand model. In order to secure the validity of the estimation of the exchange rate volatility for the export of machinery intermediate goods, various methods of volatility measurement are used including the GARCH model, the moving average standard deviation and the 12-month fixed average standard deviation. The long-term relationship between variables was analyzed by applying the panel cointegration tests and DOLS & FMOLS panel estimations. Analysis results found that prior to the global financial crisis in 2008, the total exports of machinery and exchange rate volatility positively affect the exports of intermediate goods such as general machinery, electronic machinery and transportation equipment, but did not affect the exports of precision machinery intermediate goods. After the global financial crisis, however, exchange rate volatility negatively affected total exports and the exports of all machinery intermediate goods. When analyzing the period before and after the global financial crisis, it had a positive impact on exports of precision machinery intermediate goods and a negative effect on total exports and the exports of other machinery intermediate goods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.1
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• pp.49-67
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• 2014

This paper proposes a real time pricing model for smart grid considering consumers' behavior, real time price elasticity, and exogenous price. Based on the proposed model, we found the weight of utility over cost is the most sensitive factor compared to other factors. Also, if the electricity price is set to be changed too sensitively to energy consumption, it is warned that real time pricing sometimes can cause increment of peak-time demand and volatility. Finally, real time pricing could be less efficient when the difference between the maximum and the minimum consumption level is small.