• Title/Summary/Keyword: VaR

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CTE with weighted portfolios (가중 포트폴리오에서의 CTE)

  • Hong, Chong Sun;Shin, Dong Sik;Kim, Jae Young
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.1
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    • pp.119-130
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    • 2017
  • In many literatures on VaR and CTE for multivariate distribution, these are estimated by using transformed univariate distribution with a specific ratio of many kinds of portfolios. Even though there are lots of works to define quantiles for multivariate distributions, there does not exist a quantile uniquely. Hence, it is not easy to define the VaR and CTE. In this paper, we propose the weighted CTE vectors corresponding to various ratio combinations of many kinds of portfolios by extending the researches on the alternative VaR and integrated multivariate CTE based on multivariate quantiles. We extend relation equations about univariate CTEs to multivariate CTE vectors and discuss their characteristics. The proposed weighted CTEs are explored with some data from multivariate normal distribution and illustrative examples.

GARCH Model with Conditional Return Distribution of Unbounded Johnson (Unbounded Johnson 분포를 이용한 GARCH 수익률 모형의 적용)

  • Jung, Seung-Hyun;Oh, Jung-Jun;Kim, Sung-Gon
    • The Korean Journal of Applied Statistics
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    • v.25 no.1
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    • pp.29-43
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    • 2012
  • Financial data such as stock index returns and exchange rates have the properties of heavy tail and asymmetry compared to normal distribution. When we estimate VaR using the GARCH model (with the conditional return distribution of normal) it shows the tendency of the lower estimation and clustering in the losses over the estimated VaR. In this paper, we argue that this problem can be resolved through the adaptation of the unbounded Johnson distribution as that of the condition return. We also compare this model with the GARCH with the conditional return distribution of normal and student-t. Using the losses exceed the ex-ante VaR, estimates, we check the validity of the GARCH models through the failure proportion test and the clustering test. We nd that the GARCH model with conditional return distribution of unbounded Johnson provides an appropriate estimation of the VaR and does not occur the clustering of violations.

Risk Spillover between Shipping Company's Stock Price and Marine Freight Index (해운선사 주가와 해상운임지수 사이의 위험 전이효과)

  • Choi Ki-Hong
    • Journal of Korea Port Economic Association
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    • v.39 no.1
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    • pp.115-129
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    • 2023
  • This study analyzed the risk spillover of BDI on shipping company stock prices through the Copula-CoVaR method based on daily data from January 4, 2010, to October 31, 2022. The main empirical analysis results and policy implications are as follows. First, copula results showed that there was a weak dependence between BDI and shipping company stock prices, and PAN, KOR, and YEN were selected as the most fitting model for dynamic Student-t copula, HMM was selected as the rotated Gumbel copula, and KSS was selected as the best model. Second, in the results of CoVaR, it was confirmed that the upside (downside) CoVaR was significantly different from the upside (downside) VaR in all shipping companies. This means that BDI has a significant risk spillover on shipping companies. In addition, as for the risk spillover, the downside risk is generally lower than the upside risk, so the downside and upside risk spillover were found to be asymmetrical. Therefore, policymakers should strengthen external risk supervision and establish differentiated policies suitable for domestic conditions to prevent systematic risks from BDI shocks. And investors should reflect external risks from BDI fluctuations in their investment decisions and construct optimal investment portfolios to avoid risks. On the other hand, investors propose that the investment portfolio should be adjusted in consideration of the asymmetric characteristics of up and down risks when making investment decisions.

A study on synthetic risk management on market risk of financial assets(focus on VaR model) (시장위험에 대한 금융자산의 종합적 위험관리(VaR모형 중심))

  • 김종권
    • 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.

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Value at Risk of portfolios using copulas

  • Byun, Kiwoong;Song, Seongjoo
    • 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.

Value at Risk Forecasting Based on Quantile Regression for GARCH Models

  • Lee, Sang-Yeol;Noh, Jung-Sik
    • The Korean Journal of Applied Statistics
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    • v.23 no.4
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    • pp.669-681
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    • 2010
  • Value-at-Risk(VaR) is an important part of risk management in the financial industry. This paper present a VaR forecasting for financial time series based on the quantile regression for GARCH models recently developed by Lee and Noh (2009). The proposed VaR forecasting features the direct conditional quantile estimation for GARCH models that is well connected with the model parameters. Empirical performance is measured by several backtesting procedures, and is reported in comparison with existing methods using sample quantiles.

An Estimation of VaR under Price Limits

  • Park, Yun-Sook;Yeo, In-Kwon
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.825-835
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    • 2004
  • In this paper, we investigate the estimation of the value at risk(VaR) when stock prices are subjected to price limits. The mixture of probability mass functions and beta density functions is proposed to derive the distribution of asset returns. The analyses of real data show that the proposed distribution is appropriate to explain the VaR when the price limits exist in the data.

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EMC Certification of PV Inverter DC Port (태양광 인버터 DC 포트 EMC 인증)

  • Min, Joonki;Ham, Seungyoel;Ra, Byunghun
    • Proceedings of the KIPE Conference
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    • 2015.07a
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    • pp.119-120
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    • 2015
  • 태양광 산업의 발전에 따라 계통연계형 태양광인버터의 EMC 관련 문제에 대해 CISPR 11 Ed. 6.0에서 태양광인버터의 DC포트에 대한 전도성 장해 허용기준을 추가로 제정하였고, 30MHz 이하 전도대역에서 20kVA이하, 75kVA이하 및 75kVA 초과하는 용량의 산업용 및 가정용에 대한 기준을 소개한다.

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Estimation and Performance Analysis of Risk Measures using Copula and Extreme Value Theory (코퓰러과 극단치이론을 이용한 위험척도의 추정 및 성과분석)

  • Yeo, Sung-Chil
    • 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.

The GARCH-GPD in market risks modeling: An empirical exposition on KOSPI

  • Atsmegiorgis, Cheru;Kim, Jongtae;Yoon, Sanghoo
    • 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.