• Journal of Internet Computing and Services
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• v.16 no.4
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• pp.1-11
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• 2015

This study uses the contingent valuation method to estimate the switching cost for adopting the MVNO service. The findings are as follows.: First, Willingness To Switch(WTS) increases as users' using pattern and perceived degree of MVNO service, but satisfaction with current service provider does not play a significant role in predicting Switching Cost. This means that as amount of money users can save exceeds a certain level, users decide to willingly change their service provider to MVNO regardless of their level of satisfaction with current service provider. Second, there are differences in WTS among service subscribers in SKT, KT and LGU+. It means that there will be a difference in the tendency to switch to MVNO among subscribers of service providers. This study suggest the following mangerial perspective to effectively promote the MVNO and boost the MVNO market for activation of mobile data services.: First, MVNOs are better off applying differentiated pricing scheme at attractive rates than using a differentiation of service product and quality. Second, regulators should consider how to implement an MVNO regulatory policy when there is an asymmetric customer loyalty among MNOs. This research will be used to set the MVNO's pricing strategy and to build up a successful regulatory policies.

• Environmental and Resource Economics Review
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• v.24 no.2
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• pp.311-342
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• 2015

We evaluate two main rationales of massive policy intervention of Lee Administration in the Korean transportation fuel market: high market share of domestic refineries, perceived by the Administration as the result of high market concentration, and asymmetry in price adjustment, perceived as the result of collusion. Domestic refineries, huge in capacity and located at seaports, maintain international competitiveness in price. Considering market openness offering preferential treatment to importers, they set domestic prices competitively on the basis of MOPS prices. Yet, the price competitiveness of domestic refineries is so high that they are able to sustain high market share. We confirm that the Korean before-tax consumer prices of gasoline and diesel are lower than Japan's and the weighted averages of 27 EU countries by as much as 159KRW and 21KRW per liter in the case of gasoline and 170KRW and 63KRW in the case of diesel. Price asymmetry is caused by diverse economic and managerial reasons and, as FTC (2005) states, price asymmetry does not immediately imply exercise of market power or collusion. We analyzed price asymmetry in Korea, Japan and 14 EU countries, and found asymmetry in Korea and 11 EU countries in the case of gasoline and in Korea and 8 EU countries in the case of diesel.

• Asia pacific journal of information systems
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• v.17 no.2
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• pp.83-95
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• 2007

We are motivated by how offline and online firms compete. The Internet made many conventional offline firms build a dynamic online business as another sales channel using their advantages such as brand equity, an existing customer base with comprehensive purchasing data, integrated marketing, economies of scale, and longtime experience with the logistics of order fulfillment and customer service. Even though the hybrid selling using both offline and online channel seems to have advantages over a pure online retailer, all the conventional offline firms are not seen to create an online business. Many conventional offline firms began to launch online business since the Internet era, however, just being online business is not likely to guarantee success. According to Bizate.com's report whether the hybrid channel strategy is successful is still under investigation. For example, consider the classic case of Barnes and Noble versus Amazon.com, Barnes and Noble was already the largest chain of bookstores in the U,S., when Amazon.com was established in 1995, BarnesandNoble.com followed suit in 1997, After suffering losses in its initial years, Amazon finally turned profitable in 2003. In 2004, Amazon's net income was $588 million on revenues of $6.92 billion, while Barnes and Noble earned $143 million on revenues of $4.87 billion, which included BarnesandNoble.com's loss of $21 million on revenues of $420 million. While these examples serve to motivate our thinking, it does not explain when offline firms should venture online. It also does not provide an analytical framework that can generalized to other competitive online-offline situations. We attempt to do this in this paper and analyze a hybrid channel model where a conventional offline firm competes against online firms using its own direct online channels. We are particularly interested in an optimal channel strategy when a conventional offline firm sells its products through its own direct online channel to compete with other rival online firms. We consider two situations where its direct online channel and other online firms are symmetric and asymmetric in the brand effect. The analysis of this paper presents several findings. In the symmetric model where a hybrid firm's online channel is not differentiated from a pure online firm, (i) a conventional offline firm will not launch its online business. In the asymmetric model where a hybrid firm's online channel is differentiated from a pure online firm, (ii) a conventional offline firm can launch its online business if its brand effect is greater than a certain threshold. (iii) there is a positive relationship between its brand effect and online customer costs showing that a conventional offline firm needs more brand effect in order to launch online business as online customer costs decrease. (iv) there is a negative relationship between its brand effect and the number of customers with access to the Internet showing that a conventional offline firm tends to launch its online business when customers with access to the Internet increases.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.755-775
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• 2022

This study empirically analyzes that the asymmetry of domestic gasoline price adjustment to the crude oil price changes can vary depending on the level of gasoline price using quantile autoregressive distributed lag model. The data used are the weekly average Dubai price, domestic gasoline price at refiners and gas stations from the first week of May 2008 to the second week of October 2022. The study estimates three price transmission channels: changes in gas station gasoline prices in response to changes in Dubai oil prices, changes in refiners gasoline prices in response to changes in Dubai oil prices, and changes in gas station prices relative to refiners gasoline prices. As a result, the price adjustment of refiner's gasoline price with respect to Dubai oil price appears asymmetrically across all quantiles of gasoline price, whereas the adjustment of gas station prices for Dubai oil price and refiner's gasoline price tend to be more asymmetric as the quantile of gasoline price increases. Such a result is presumed to be due to changes in the inventory cost of gas stations. When the burden of inventory cost is high, gas stations have an incentive to more actively pass the increased buying price on their selling price.

• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.83-102
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• 2009

Banks traditionally focus on the financial services against the uncertain future liquidity needs, i.e. saving as well as lending. As the business model of banks has been shifted from the originate to hold model to the originate to distribute model since the enactment of Gramm-Leach-Bliley Financial Services Modernization Act in 1999, the financial services encompass information gathering and generating, underwriting and risk sharing through packaging claims for the investors, in addition to the payment and settlement services. Ensued are the financial market integration and diversification of financial services, with which the accessibility to financial services is arguably significantly enhanced. Such integration and diversification necessarily entails the risk of contagion due to the non-fulfilling service over the several other financial services, which would be contained easily under the separate financial services. This paper addresses the pricing of fees for the integrated financial services through which the contagion could spread when the users of financial service are not immune to the failure to fulfill their obligation due to the economic turmoil. Consequently the information asymmetry about the clients is unavoidable. Higher fees could drive out the otherwise good clients out of the pool of customers for the financial services. Then, the risk could be exacerbated due to the proliferation of bad clients who are vulnerable to the financial distress and liquidity crunch. So the banks should take into account the interactional effect of the fees between/among the non interest based activities and interest based activities under the information asymmetry. Contrary to our general perception, the current analysis demonstrates that the bank should focus on the reduction of cost associated with good clients rather than that of bad clients.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.485-499
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• 1999

Auctions are appealing market-type mechanisms because they can be deployed to solve the twin problems of resources pricing and allocation. Nonetheless the effectiveness of an auction mechanism in radio spectrum property rights should not be taken for granted. Policymakers need to be aware of the complexity of introducing market discipline in an area where none existed before. Auction design is critical to the success of the allocation process. However, a poorly designed auction mechanism can have detrimental effects on the spectrum rights allocation process. This study discusses some of the key elements and issues of auction design of radio spectrum rights for its efficient allocation. Particularly this study discusses, based on the existing auction theory and other countries' experiences, such issues as bidding rule, value interdependency and sequence of auction, information structure and asymmetric bidder, and wealth constraints and imperfect capital market.

• Korea Real Estate Review
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• v.27 no.4
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• pp.63-76
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• 2017

The purpose of this study is to analyze the effect of the results of a nationwide academic evaluation of middle schools and high schools on apartment prices in Ulsan City by using a hedonic pricing model. The results of the middle school and high school achievement test, the College Scholastic Ability Test (CSAT) score for high school, the national united evaluation score, and the number of successful applicants to prestigious universities have a significant effect on the apartment price formation with a positive relationship. In addition, different kinds of academic evaluation score have asymmetric effects on apartment price determination. The results of the high school achievement evaluation are more important than the results of the middle school achievement evaluation in the apartment price determination. Among the achievement evaluation results, the ratio of the students with the higher education level is more important than the ratio of the students with the lower basic education level. Furthermore, the CSAT score for Natural Sciences is more important than the CSAT score for the Humanities course.

• International Area Studies Review
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• v.14 no.3
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• pp.62-84
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.