• 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.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.605-618
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• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

• 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.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.947-978
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• 2007

In this paper, we investigated a Value-at-Risk approach to the volatility of two crude oil markets (Brent and Dubai). We also assessed the performance of various VaR models (RiskMetrics, GARCH, IGARCH and FIGARCH models) with the normal and skewed Student-t distribution innovations. The FIGARCH model outperforms the GARCH and IGARCH models in capturing the long memory property in the volatility of crude oil markets returns. This implies that the long memory property is prevalent in the volatility of crude oil returns. In addition, from the results of VaR analysis, the FIGARCH model with the skewed Student-t distribution innovation predicts critical loss more accurately than other models with the normal distribution innovation for both long and short positions. This finding indicates that the skewed Student-t distribution innovation is better for modeling the skewness and excess kurtosis in the distribution of crude oil returns. Overall, these findings might improve the measurement of the dynamics of crude oil prices and provide an accurate estimation of VaR for buyers and sellers in crude oil markets.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.481-504
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• 2006

VaR, a tail-related risk measure is now widely used as a tool for a measurement and a management of financial risks. For more accurate measurement of VaR, recently we are particularly concerned about the approach based on extreme value theory rather than the traditional method based on the assumption of normal distribution. However, many studies about the approaches using extreme value theory was done only for the univariate case. In this paper, we discuss portfolio risk measurements with modelling multivariate extreme value distributions by combining copulas and extreme value theory. We also discuss the estimation of ES together with VaR as portfolio risk measures. Finally, we investigate the relative superiority of EVT-copula approach than variance-covariance method through the back-testing of an empirical data.

• 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.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.59-79
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• 2021

Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

• Korean Journal of Construction Engineering and Management
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• v.10 no.1
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• pp.114-125
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• 2009

In this study, a design value analysis technique that considered stochastic LCC and stochastic performance evaluation was proposed, and by introducing the concept of reliability analysis, a decision making that secured reliability was supported. The results of this study, which was carried out according to the above objectives and methods, are summarized as follows: 1) The design value analysis procedures and value state function, improved in order to carry out a reliable analysis when evaluating alternate proposals that were extracted after the function definition was complete, were formalized, and in order to secure consistency and efficiency for value evaluation procedures, an evaluation index scheme was proposed; 2) Database collection and analysis were done for a bridge's LCC analysis. As for the collection scope of data, literature of previous research done on a bridge's LCC analysis was used as the basis for analysis, and for securing reliability regarding analysis results and dealing with uncertainty of collected data, the MCS technique was applied; 3) Weights and evaluation ranks for performance evaluation of each of the alternate proposals, as well as LCC analysis model, analysis period, discount rate, user expense, safety inspection and safety diagnosis expense conditions for LCC analysis were proposed. Lastly, a feasibility study was done and conclusion was made about "OO grand bridge and connecting road construction work execution design" project centered on value analysis execution case.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.2
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• pp.203-218
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• 2022

The global value chain, as a major feature of the contemporary global economic system, has been mainly led by developed countries. Whereas developing countries have taken the relatively low value-added activities and this made geographical imbalances in value distribution. This imbalance in value distribution, however, began to gradually alleviated. Related to this phenomenon, the purpose of this research is to analyze the factors affecting factors. Focused on the method of upgrading the industry in the global value chain, the impact on the acquisition of value-added in developing countries was analyzed among the various factors to achieve the research purpose. Panel analysis was conducted on all industries, food and tobacco industries, textile and clothing industries, computer and electornics industries, and automobile industries of the OECD Value-Added Trade Data (TiVA). As a result of the analysis, it was confirmed that in all industries, value-added acquisition in developing countries was improved by increased total production, high value-added product production and participation in early stage. The analysis results by detailed industry showed slightly different patterns depending on the characteristics of each industry.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.23-40
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• 2022

Under the global economic system, production activities has formed an international division of labor, which has greatly affected industries in individual countries by global issues such as the U.S-China trade war and neo-protectionism. In particular the risk and change of disconnection of semiconductor value chain caused by COVID-19 are evaluated as offering the crisis and opportunity at the same time to all countries participating in the global electronics industry value chain. Therefore, this study was conducted with the OECD Trade in Value Added(TiVA) based on the time when a detailed analysis of the global chain of the electronic industry is needed. As a result of the analysis, it was confirmed that the global value chain of the electronics industry is gradually expanding and strengthening, and that various countries are emerging as major actors in the global value chain. It was found that the U.S. and Japan are in charge of relatively high value-added activities, while Korea, Taiwan and China are in charge of low value-added activities, although they are large scale.