• Journal of the Korean Society of Costume
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• v.53 no.7
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• pp.83-94
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• 2003

The purpose of this study was to investigate the relationship between the clothing consumption value and consumer satisfaction which were based on the purchase places such as department store and market, and to examine the influence of the clothing consumption value and demographic variables on the consumer satisfaction. The subjects were 364 females ranging in ages from twenties to fifties who dwelt in Seoul and in the suburbs of Seoul. Four factors of clothing consumption value derived by factor analysis: 'functional value', 'emotional value', 'epistemic value', and 'conditional value'. The clothing consumption value and satisfaction of shopping system, purchase system, and consumption system of buyers at department store showed higher than that of buyers at market. Emotional value was most important in predicting the consumer satisfaction of buyers at department store, followed by epistemic value (-) and conditional value. Conditional value was most important in predicting the satisfaction of buyers at market, followed by emotional value and the academic background of buyers. Generally the higher the emotional and conditional value, the higher the consumer satisfaction. and the consumer satisfaction was influenced by epistemic value negatively.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.171-180
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• 2016

Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.883-890
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• 2010

Since Fisher's exact test is conducted conditional on the observed value of the margin, there are two kinds of the exact power, the conditional and the unconditional exact power. The conditional exact power is computed at a given value of the margin whereas the unconditional exact power is calculated by incorporating the uncertainty of the margin. Although the sample size is determined based on the unconditional exact power, the actual power which Fisher's exact test has is the conditional power after the experiment is finished. This paper investigates differences between the conditional and unconditional exact power Fisher's exact test. We conclude that such discrepancy is a disadvantage of Fisher's exact test.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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• pp.133-146
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• 1994

Conditional constraints are frequently used to represent relations. To use these conditional constraints, it is necessary to develop an appropriate logic in which these conditional constraints can be represented and manipulated. Nevertheless, there has been little research that addresses interval-based conditional constraints. The proposed approach addresses the use of conditional constraints involving intervals in constraint networks. Two algorithms are presented: (1) a propagation algorithm for an interval-based conditional constraint, which is similar to one for an exact-value conditional constraint; (2) a propagation algorithm for interval-based conditional constraints which satisfy some conditions. The former can be applied to any conditional constraint. However, with the former algorithm, conditional constraints are usually categorized into the cases that they cannot be propagated. After investigating several methods in which most conditional constraints can be propagated, we propose the latter algorithm under certain condition that usually results in smaller resulting design space comparing to the former.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.589-596
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• 2011

The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

• Korean Management Science Review
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• v.28 no.2
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• pp.1-16
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• 2011

In this study we suggested two optimization models to answer a question from an investor standpoint : how many convertible bonds should one convert, and how many keep? One model minimizes certain risk to the minimum required expected return, the other maximizes the expected return subject to the maximum acceptable risk. In comparison with Markowitz portfolio models, which use the variance of return, our models used Conditional Value-at-Risk(CVaR) for risk measurement. As a coherent measurement, CVaR overcomes the shortcomings of Value-at-Risk(VaR). But there are still difficulties in solving CVaR including optimization models. For this reason, we adopted Rockafellar and Uryasev's[18, 19] approach. Then we could approximate the models as linear programming problems with scenarios. We also suggested to extend the models with credit risk, and applied examples of our models to Hynix 207CB, a convertible bond issued by the global semiconductor company Hynix.

• Journal of Distribution Science
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• v.19 no.7
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• pp.87-100
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• 2021

Purpose: This paper examined the role of conditional value in the green automotive industry. The relationships of conditional value's four factors, consumers' attitudes and consumers' intention to purchase green cars were investigated. The conditional value was extended by examining the effect of fuel prices. Research design, data, and methodology: This study is quantitatively designed. All variables were measured using a 7-point Likert-scale; 425 questionnaires were collected from the respondents in Malaysia. SmartPLS was utilized to examine the proposed nine hypotheses. Result: The results demonstrate a positive relationship between attitude and intention toward green cars. Additionally, the results of the relationships were as follows: fuel prices was the most significant predictor of Malaysian consumers' attitudes and consumers' intention to purchase green cars, followed by environmental consequences and government policy. However, retail sales promotions did not show a significant effect on both consumers' attitudes and intentions. Conclusion: The study's findings suggest that the Malaysian government should implement an integrated package that includes a fuel pricing policy that restricts the purchase of non-green cars, as well as a set of financial incentives for purchasing green cars. Moreover, it is valuable to conduct public awareness campaigns about the negative consequences of current consumption patterns.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.789-795
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• 2014

In a demand response (DR) market run by independent system operators (ISOs), load aggregators are important market participants who aggregate small retail customers through various DR programs. A load aggregator can minimize the allocation cost by efficiently allocating its demand response resources (DRRs) considering retail customers' characteristics. However, the uncertain response behaviors of retail customers can influence the allocation strategy of its DRRs, increasing the economic risk of DRR allocation. This paper presents a risk-based DRR allocation method for the load aggregator that takes into account not only the physical characteristics of retail customers but also the risk due to the associated response uncertainties. In the paper, a conditional value-at-risk (CVaR) is applied to deal with the risk due to response uncertainties. Numerical results are presented to illustrate the effectiveness of the proposed method.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.413-422
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• 2019

The purpose of this study is to analyze the purchase intention of outdoor products for 40s - 50s consumers The factors influencing the purchase intention are various factors, but the service quality and the consumption value are analyzed from an exploratory viewpoint. The quality of service was set as detailed, tangibles, responsiveness, and reliability. Consumption value was set as functional value, rare value, and conditional value. As a result, tangibles, responsiveness, and reliability of the service quality, the functional value influences the purchase intention, but the rare value and the conditional value of the consumption value indicate the purchasing intention. The result of this analysis shows that service quality is a very important factor for consumers' purchase intention in 40's - 50's consumers, but consumption value is not very important factor in purchase intention.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.3
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• pp.501-505
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• 2012

In this paper, general classes of continuous distributions are characterized by considering the conditional expectations of functions of upper record statistics. The specific distribution considered as a particular case of the general class of distribution are Exponential, Exponential Power(EP), Inverse Weibull, Beta Gumbel, Modified Weibull(MW), Weibull, Pareto, Power, Singh-Maddala, Gumbel, Rayleigh, Gompertz, Extream value 1, Beta of the first kind, Beta of the second kind and Lomax.