• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.11a
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• pp.549-552
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• 1999

We have developed an novel vertical-alignment (VA) - $\pi$ cell mode that provides a wide viewing angle and fast response times for negative dielectric anisotropy nematic liquid crystal (NLC) on a homeotropic polyimide (PI) surfaces. We had the good voltage-transmittance curves and low driving voltages were achieved with the novel VA - $\pi$ cell mode without negative compensation film. Iso-viewing angle characteristics using the novel VA - $\pi$ cell mode without negative compensation film of NLC was also successfully observed. As well a fast response time of 31.7ms for the novel VA - $\pi$ cell mode was measured. Consequently, It is seen that by using the novel VA - $\pi$ cell mode the iso-viewing angle, fast response time, and low driving voltage characteristics can be achieved.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.11a
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• pp.545-548
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• 1999

The electro-optical characteristics of photo-aligned vertical-alignment (VA)-liquid crystal display (LCD) with non-polarized ultraviolet (UV) light irradiation of 45\`on homeotropic polyimide (Pl) surface were investigated. LC alignment on photo-aligned VA-LCD is attributed to photo-depolymerized reaction of the polymer with non-polarized UV light irradiation on PI surface. We had good voltage-transmittance (V-T) characteristics of photo-aligned VA-LCD. The response time of photo-aligned VA-LCD was slower than that of the rubbing-aligned VA-LCD. Finally, we suggest that the slow response time of photo-aligned VA-LCD depends on the LC domain.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1453-1463
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• 2016

The most useful financial risk measure may be VaR (Value at Risk) which estimates the maximum loss amount statistically. The VaR tends to be estimated in many industries by using transformed univariate risk including variance-covariance matrix and a specific portfolio. Hong et al. (2016) are defined the Vector at Risk based on the multivariate quantile vector. When a specific portfolio is given, one point among Vector at Risk is founded as the best VaR which is called as an alternative VaR (AVaR). In this work, AVaRs have been investigated for multivariate normal distributions with many kinds of variance-covariance matrix and various portfolio weight vectors, and compared with VaRs. It has been found that the AVaR has smaller values than VaR. Some properties of AVaR are derived and discussed with these characteristics.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.471-485
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• 2010

VaR is used extensively as a tool for risk management by financial institutions. For convenience, the normal distribution is usually assumed for the measurement of VaR, but recently the method using extreme value theory is attracted for more accurate VaR estimation. So far, GEV and GPD models are used for probability models of EVT for the VaR estimation. In this paper, the PP model is suggested for improved VaR estimation as compared to the traditonal EV models such as GEV and GPD models. In view of the stochastic process, the PP model is regarded as a generalized model which include GEV and GPD models. In the empirical analysis, the PP model is shown to be superior to GEV and GPD models for the performance of VaR estimation.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.219-234
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• 2010

In index investing according to KOSPI, we estimate Value at Risk(VaR) from the extreme losses of the daily returns which are obtained from KOSPI. To this end, we apply Block Maxima(BM) model which is one of the useful models in the extreme value theory. We also estimate the extremal index to consider the dependency in the occurrence of extreme losses. From the back-testing based on the failure rate method, we can see that the model is adaptable for the VaR estimation. We also compare this model with the GARCH model which is commonly used for the VaR estimation. Back-testing says that there is no meaningful difference between the two models if we assume that the conditional returns follow the t-distribution. However, the estimated VaR based on GARCH model is sensitive to the extreme losses occurred near the epoch of estimation, while that on BM model is not. Thus, estimating the VaR based on GARCH model is preferred for the short-term prediction. However, for the long-term prediction, BM model is better.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.471-481
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• 2013

The current VaR Model based on J. P. Morgan's RiskMetrics has problem that actual loss exceeds VaR under unstable economic conditions because the current VaR Model can't re ect future economic conditions. In general, any corporation's stock price is determined by the rm's idiosyncratic factor as well as the common systematic factor that in uences all stocks in the portfolio. In this study, we propose an One-factor VaR Model for stock portfolio which is decomposed into the common systematic factor and the rm's idiosyncratic factor. We expect that the actual loss will not exceed VaR when the One-factor Model is implemented because the common systematic factor considering the future economic conditions is estimated. Also, we can allocate the stock portfolio to minimize the loss.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.189-208
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• 2006

In this paper we are concerned with estimation of tail related risk measures for heteroscedastic financial time series and VaR limits that VaR tells us nothing about the potential size of the loss given. So we use GARCH-EVT model describing the tail of the conditional distribution for heteroscedastic financial series and adopt Expected Shortfall to overcome VaR limits. The main results can be summarized as follows. First, the distribution of stock return series is not normal but fat tail and heteroscedastic. When we calculate VaR under normal distribution we can ignore the heavy tails of the innovations or the stochastic nature of the volatility. Second, GARCH-EVT model is vindicated by the very satisfying overall performance in various backtesting experiments. Third, we founded the expected shortfall as an alternative risk measures.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.133-136
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• 2000

We investigated the improvement of viewing angle using a patterned double twisted (PDT) vertical-alignment (VA) cell mode on a homeotropic alignment layer. Good voltage-transmittance curves for negative dielectric anisotropic nematic liquid crystal (NLC) using the PDT-VA cell without a negative compensation film were obtained. The viewing angle of the PDT-VA cell without a negative compensation film was wider than that of a conventional VA cell.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.11a
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• pp.133-136
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• 2000

We investigated the improvement of viewing angle using a patterned double twisted (PDT) vertical-alignment (VA) cell mode on a homeotropic alignment layer. Good voltage-transmittance curves for negative dielectric anisotropic nematic liquid crystal (NLC) using the PDT-VA cell without a negative compensation film were obtained. The viewing angle of the PDT-VA cell without a negative compensation film was wider than that of a conventional VA cell.