• 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.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.791-801
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• 2009

Value-at-Risk(VaR) has been used as an important tool to measure the market risk. However, the selection of the VaR models is controversial. This paper proposes VaR forecast combinations using support vector machine quantile regression instead of selecting a single model out of historical simulation and GARCH.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.891-901
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• 2009

In this paper, we investigate the approach to estimate VaR under the transformed GARCH model. The time series are transformed to approximate to the underlying distribution of error terms and then the parameters and the one-sided prediction interval are estimated with the transformed data. The back-transformation is applied to compute the VaR in the original data scale. The analyses on the asset returns of KOSPI and KOSDAQ are presented to verify the accuracy of the coverage probabilities of the proposed VaR.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• 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.

• Korean Management Science Review
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• v.30 no.3
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• pp.55-70
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• 2013

In this study we suggested two optimization models to determine conversion weight of convertible bonds. The problem of this study is same as that of Park and Shim [1]. But this study used Value-at-Risk (VaR) for risk measurement instead of CVaR, Conditional-Value-at-Risk. In comparison with conventional Markowitz portfolio models, which use the variance of return, our models used VaR. In 1996, Basel Committee on Banking Supervision recommended VaR for portfolio risk measurement. But there are difficulties in solving optimization models including VaR. Benati and Rizzi [5] proved NP-hardness of general portfolio optimization problems including VaR. We adopted their approach. But we developed efficient algorithms with time complexity O(nlogn) or less for our models. We applied examples of our models to the convertible bond issued by a semiconductor company Hynix.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1263-1274
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• 2013

VaR (value at risk), which represents the expectation of the worst loss that may occur over a period of time within a given level of confidence, is currently used by various financial institutions for the purpose of risk management. In the majority of previous studies, the probability of return has been modeled with normal distribution. Recently Chen et al. (2010) measured VaR with asymmetric Laplacian distribution. However, it is difficult to estimate the mode, the skewness, and the degree of variance that determine the shape of an asymmetric Laplacian distribution with limited data in the real-world market. In this paper, we show that the VaR estimated with (symmetric) Laplacian distribution model provides more accuracy than those with normal distribution model or asymmetric Laplacian distribution model with real world stock market data and with various statistical measures.

• The Korean Journal of Financial Studies
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• v.10 no.1
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• pp.191-214
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• 2004

VaR에 의한 금융위험의 측정은 국제결제은행 바젤위원회의 내부모델 허용에 힘입어 금융산업에서 표준방식으로 확고한 입지를 차지하고 있다. 본 연구에서는 한국주식시장포트폴리오를 거래투자자산으로 보유한 경우의 VaR를 극단치이론에 입각하여 측정하고 이의 성과를 RiskMetrics의 성과와 비교하여 검토하였다. GPD의 모수적 추정에 의한 VaR의 사후검정결과는 표본내 사후검정이나 표본외 사후검정에서 어떤 신뢰수준에서도 기대되는 범위와 크게 벗어나지 않은 안정된 결과를 보였다. RiskMetrics의 EWMA방식도 역시 표본내와 표본외 사후검정 어느 경우에나 기대되는 범위에서 크게 벗어나지 않았지만 높은 신뢰수준에서는 그 성과가 GPD VaR에 비하여 상대적으로 불안정하였으며 위험의 과소평가 성향을 확인할 수 있었다. 비모수적 GEV추정에 입각한 VaR의 경우에는 위험을 과대평가하고 지나치게 보수적인 성향을 나타내었다. GPD의 모수적 접근에 의한 VaR 측정은 다양한 신뢰수준에서 정확한 검정결과를 보여주고 있으며, 시간적 흐름에 따르는 VaR의 행태도 지나친 변동성을 보이지 않아 외부규제 및 내부통제를 위한 금융위험의 측정지표로서 실용적인 가치가 있음을 확인할 수 있다.

• 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.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.541-559
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• 2015

VaR is now widely used as an important tool to evaluate and manage financial risks. In particular, it is important to select an appropriate volatility model for the rate of return of financial assets. In this study, both univariate and multivariate models are considered to evaluate VaR of the portfolio composed of KOSPI, Hang-Seng, Nikkei indexes, and their performances are compared through back testing techniques. Overall, multivariate models are shown to be more appropriate than univariate models to estimate the portfolio VaR, in particular DCC and ADCC models are shown to be more superior than others.