• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1661-1671
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• 2016

Risk analysis is a systematic study of uncertainties and risks we encounter in business, engineering, public policy, and many other areas. Value at Risk (VaR) is one of the most widely used risk measurements in risk management. In this paper, the Korean Composite Stock Price Index data has been utilized to model the VaR employing the classical ARMA (1,1)-GARCH (1,1) models with normal, t, generalized hyperbolic, and generalized pareto distributed errors. The aim of this paper is to compare the performance of each model in estimating the VaR. The performance of models were compared in terms of the number of VaR violations and Kupiec exceedance test. The GARCH-GPD likelihood ratio unconditional test statistic has been found to have the smallest value among the models.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.753-771
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• 2016

Value at Risk (VaR) is widely used as an important tool for risk management of financial institutions. In this paper we discuss estimation and back testing for VaR of the portfolio composed of KOSPI, Dow Jones, Shanghai, Nikkei indexes. The copula functions are adopted to construct the multivariate distributions of portfolio components from marginal distributions that combine extreme value theory and GARCH models. Volatility models with t distribution of the error terms using Gaussian, t, Clayton and Frank copula functions are shown to be more appropriate than the other models, in particular the model using the Frank copula is shown to be the best.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.1-13
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• 2002

Estimating the Value-at-Risk (VaR) of a non-listed or newly listed company in stock market is impossible due to lack of stock exchange data. This study employes Case-Based Reasoning (CBR) for estimating VaR's of those companies. CBR enables us to identify and select existing companies that have similar financial and non-financial characteristics to the unlisted target company. The VaR's of those selected companies can give estimates of VaR for the target company. We developed a system called VAS-CBR and showed how well the system estimates the VaR's of unlisted companies.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.163-185
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• 2013

In this paper, we investigate the need to employ long-memory volatility models in terms of Value-at-Risk(VaR) estimation. We estimate the VaR of the KOSPI returns using long-memory volatility models such as FIGARCH and FIEGARCH; in addition, via back-testing we compare the performance of the obtained VaR with short memory processes such as GARCH and EGARCH. Back-testing says that there exists a long-memory property in the volatility process of KOSPI returns and that it is essential to employ long-memory volatility models for the right estimation of VaR.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1171-1180
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• 2014

We considered saddlepoint approximations to VaR (value at risk) and ES (expected shortfall) which frequently encountered in finance and insurance as the measures of risk management. In this paper we supposed univariate and multivariate skew-normal distributions, instead of traditional normal class distributions, as underlying distribution of linear portfolios. Simulation results are provided and showed the suggested saddlepoint approximations are very accurate than normal approximations.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.283-288
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• 2005

Value at Risk(VaR) has been proven useful in finance literature as a tool of risk management(cf. Jorion(2001)). This article is concerned with introducing VaR to various Korean financial time series. Five daily data sets with sample period ranging from 2000 and 2004 such as KOSPI, KOSPI 200, KOSDAQ, KOSDAQ 50 and won-dollar exchange rate are analyzed using GARCH modeling and in turn VaR is obtained for each data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.43-57
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• 1999

The recent trend is that risk management has more and more its importance. Neverthless, Korea's risk management is not developed. Even most banks does gap, duration in ALM for risk management, development and operation of VaR stressed at BIS have elementary level. In the case of Fallon and Pritsker, Marshall, gamma model is superior to delta model and Monte Carlo Simulation is improved at its result, as sample number is increased. And, nonparametric model is superior to parametric model. In the case of Korea's stock portfolio, VaR of Monte Carlo Simulation and Full Variance Covariance Model is less than that of Diagonal Model. The reason is that VaR of Full Variance Covariance Model is more precise than that of Diagonal Model. By the way, in the case of interest rate, result of monte carlo simulation is less than that of delta-gamma analysis on 95% confidence level. But, result of 99% is reversed. Therefore, result of which method is not dominated. It means two fact at forecast on volatility of stock and interest rate portfolio. First, in Delta-gamma method and Monte Carlo Simulation, assumption of distribution affects Value at Risk. Second, Value at Risk depends on test method. And, if option price is included, test results will have difference between the two. Therefore, If interest rate futures and option market is open, Korea's findings is supposed to like results of other advanced countries. And, every banks try to develop its internal model.

• The Korean Journal of Emergency Medical Services
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• v.12 no.3
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• pp.5-15
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• 2008

Purpose : This study is a descriptive research to provide basic factors of posttraumatic stress in Firefighters. This study was carried out to develop the effective program for the fire fighters to cope with the posttraumatic stress following the disasters. Methods : The questionnaires were collected among fire fighters who serviced in K and I community from April 1 to June 30 in 2008. Total 304 questionnaires were analyzed by SPSS WIN program for descriptive statistics, Pearson's correlation coefficient and t-test. Results : 1. 48.0% of 300 fire fighters were at the age of 31-40, and 42.3% were under 30. 2. Work burden had a significant difference of 2.30 in low-risk group, 2.60 in high-risk group(t-value=-3.85, p=0.00). However, life event had no significant difference 0.79 event in low-risk group, 1.41 event in high-risk group(t-value=-2.27, p=0.24). 3. Concerning posttraumatic stress factors, there was positive correlation between mobilization impact level r=0.38(P<0.01), work burden r=0.38(p<0.01), and life event r=0.27(p<0.01). 4. According to the Symptom Check List-Revised(SCL-90-R), somatization had a significant differences(t-value=5.46, p=0.00), obsessive-compulsive(t-value=7.16, p=0.00), interpersonal sensitivity(t-value=6.15, p=0.00), depression(t-value=6.62, p=0.00), anxiety (t-value=7.33, p=0.00), hostility(t-value=5.94, p=0.00), phobia anxiety(t-value=6.85, p=0.00), paranoid ideation(t-value=5.55, p=0.00), psychotism(t-value=6.52, p=0.00) in low-risk and high-risk group. Conclusion : As a consequence, mobilization impact, work burden, and life event were the influential factors on posttraumatic stress. Also, high-risk group revealed significantly higher score on all 9 scales. The information obtained from surveys made recommendation to develop the intervention of stress management to control mobilization impact and posttraumatic stress.

• Journal of the Korean Society of Food Culture
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• v.22 no.1
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• pp.32-42
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• 2007

The purpose of this study was to investigate how menu quality, human ${\cdot}$ amenity service quality, perceived risk affected quality ${\cdot}$ price menu value, social ${\cdot}$ emotion menu value and how quality ${\cdot}$ price menu value and social ${\cdot}$ emotion menu value influenced satisfaction. Also this study investigated how satisfaction affected loyalty. The model was tested in hotel restaurants settings of five-star hotels using a sample of customers visiting and enjoying menus in Daegu metropolitan city and Gyeongju city. Empirical results confirmed that not only do menu quality and human ${\cdot}$ amenity service quality increase quality ${\cdot}$ price menu value and social ${\cdot}$ emotion menu value but that perceived risk reduces social ${\cdot}$ emotion menu value. It was also found that significant antecedents of satisfaction were quality ${\cdot}$ price menu value and social ${\cdot}$ emotion menu value. Also, loyalty was also found to be a significant consequences of satisfaction.