• 제목/요약/키워드: value at risk

검색결과 942건 처리시간 0.026초

전환사채 주식전환을 위한 조건부 VaR 최적화 (Conditional Value-at-Risk Optimization for Conversion of Convertible Bonds)

  • 박구현;심은택
    • 경영과학
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    • 제28권2호
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    • pp.1-16
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    • 2011
  • In this study we suggested two optimization models to answer a question from an investor standpoint : how many convertible bonds should one convert, and how many keep? One model minimizes certain risk to the minimum required expected return, the other maximizes the expected return subject to the maximum acceptable risk. In comparison with Markowitz portfolio models, which use the variance of return, our models used Conditional Value-at-Risk(CVaR) for risk measurement. As a coherent measurement, CVaR overcomes the shortcomings of Value-at-Risk(VaR). But there are still difficulties in solving CVaR including optimization models. For this reason, we adopted Rockafellar and Uryasev's[18, 19] approach. Then we could approximate the models as linear programming problems with scenarios. We also suggested to extend the models with credit risk, and applied examples of our models to Hynix 207CB, a convertible bond issued by the global semiconductor company Hynix.

조건부 Value-at-Risk와 Expected Shortfall 추정을 위한 준모수적 방법들의 비교 연구 (Comparison of semiparametric methods to estimate VaR and ES)

  • 김민조;이상열
    • 응용통계연구
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    • 제29권1호
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    • pp.171-180
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    • 2016
  • 바젤 위원회는 시장위험의 측정 도구로 Value-at-Risk(VaR)와 expected shortfall(ES)을 사용할 것을 제안하였다. 여러 문헌에서 VaR와 ES의 다양한 추정 방법들이 연구 되었다. 본 연구에서는 준모수적인 방법인 conditional autoregressive value at risk(CAViaR), conditional autoregressive expectile(CARE) 방법들, 그리고 Gaussian 준최대가능도 추정량(QMLE)를 이용한 방법을 사후 검정을 통해서 비교하고자 한다. 각 방법의 타당성을 확인하기 위해서, VaR에 대한 사후 검정은 unconditional coverage(UC)와 conditional coverage(CC) 검정을 사용하고 ES에 대한 검정은 붓스트랩 방법을 사용한다. S&P500 지수와 현대 자동차 주식가격 지수에 대하여 실증 자료 분석이 수행되었다.

FUZZY RISK MEASURES AND ITS APPLICATION TO PORTFOLIO OPTIMIZATION

  • Ma, Xiaoxian;Zhao, Qingzhen;Liu, Fangai
    • Journal of applied mathematics & informatics
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    • 제27권3_4호
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    • pp.843-856
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    • 2009
  • In possibility framework, we propose two risk measures named Fuzzy Value-at-Risk and Fuzzy Conditional Value-at-Risk, based on Credibility measure. Two portfolio optimization models for fuzzy portfolio selection problems are formulated. Then a chaos genetic algorithm based on fuzzy simulation is designed, and finally computational results show that the two risk measures can play a role in possibility space similar to Value-at-Risk and Conditional Value-at-Risk in probability space.

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평균/VaR 최적화 모형에 의한 전환사채 주식전환 비중 결정 (Determination Conversion Weight of Convertible Bonds Using Mean/Value-at-Risk Optimization Models)

  • 박구현
    • 경영과학
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    • 제30권3호
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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.

Value at Risk Forecasting Based on Quantile Regression for GARCH Models

  • Lee, Sang-Yeol;Noh, Jung-Sik
    • 응용통계연구
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    • 제23권4호
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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.

서포트벡터기계를 이용한 VaR 모형의 결합 (Combination of Value-at-Risk Models with Support Vector Machine)

  • 김용태;심주용;이장택;황창하
    • Communications for Statistical Applications and Methods
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    • 제16권5호
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    • pp.791-801
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    • 2009
  • VaR(Value-at-Risk)는 시장위험을 측정하기 위한 중요한 도구로 사용되고 있다. 그러나 적절한 VaR 모형의 선택에는 논란의 여지가 많다. 본 논문에서는 특정 모형을 선택하여 VaR 예측값을 구하는 대신 대표적으로 많이 사용되는 두개의 VaR 모형인 역사적 모의실험과 GARCH 모형의 예측값들을 서포트벡터기계 분위수 회귀모형을 이용하여 결합하는 방법을 제안한다.

Risk-Based Allocation of Demand Response Resources Using Conditional Value-at Risk (CVaR) Assessment

  • Kim, Ji-Hui;Lee, Jaehee;Joo, Sung-Kwan
    • Journal of Electrical Engineering and Technology
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    • 제9권3호
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    • pp.789-795
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    • 2014
  • In a demand response (DR) market run by independent system operators (ISOs), load aggregators are important market participants who aggregate small retail customers through various DR programs. A load aggregator can minimize the allocation cost by efficiently allocating its demand response resources (DRRs) considering retail customers' characteristics. However, the uncertain response behaviors of retail customers can influence the allocation strategy of its DRRs, increasing the economic risk of DRR allocation. This paper presents a risk-based DRR allocation method for the load aggregator that takes into account not only the physical characteristics of retail customers but also the risk due to the associated response uncertainties. In the paper, a conditional value-at-risk (CVaR) is applied to deal with the risk due to response uncertainties. Numerical results are presented to illustrate the effectiveness of the proposed method.

Estimating the Credit Value-at-Risk of Korean Property and Casuality Insurers

  • Hong, Yeon-Woong;Suh, Jung-Soo
    • Journal of the Korean Data and Information Science Society
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    • 제19권4호
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    • pp.1027-1036
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    • 2008
  • Value at Risk(VaR) is a fundamental tool for managing market risks. It measures the worst loss to be expected of a portfolio over a given time horizon under normal market conditions at a given confidence level. Calculation of VaR frequently involves estimating the volatility of return processes and quantiles of standardized returns. In this paper, we introduced and applied the CreditMetrics model to estimate the credit VaR of Korean Property and Casuality insurers.

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The Effect of Risk-Based Efficiency Value on Firm Value: A Case Study in Indonesia

  • JUNIAR, Asrid;FADAH, Isti;UTAMI, Elok Sri;PUSPITASARI, Novi
    • The Journal of Asian Finance, Economics and Business
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    • 제8권5호
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    • pp.231-239
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    • 2021
  • The purpose of this study is to analyze the effect of risk efficiency, financial decisions, and financial performance on firm value due to advances in financial reporting technology. This research was conducted on all banking sub-sector companies listed on the Indonesian capital market during a period of eight years, namely 2012-2019 which were selected using the purposive sampling method. The advancement of financial reporting technology is measured by two indicators based on the Internet financial reporting approach. Risk efficiency is measured using three indicators with a risk proxy relative efficiency approach using value at risk. Financial decisions are measured by two indicators that represent funding decisions and investment decisions. Financial performance is measured by two indicators with the profitability approach, and firm value is measured by two indicators based on the investor perception approach. The data analysis technique in this study used multivariate analysis with SEM-PLS. The empirical findings of this study are the advances in financial reporting technology, financial decisions, and risk-based efficiency value have a significant effect on firm value, while financial performance does not have a significant effect on firm value. Banking companies reduce risk to achieve efficiency and result in lower profits.

정성적 위험분석 단계에서 중간위험 집중형 위험도 산정 방법 (Risk Value Calculation Method for Moderate Risk Concentration Type at Qualitative Risk Analysis Phase)

  • 김선규
    • 한국건설관리학회논문집
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    • 제16권2호
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    • pp.38-45
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    • 2015
  • 건설위험관리 프로세스의 위험분석단계는 정성적 및 정량적 위험분석단계로 세분화되는데, 정성적 위험분석이 주된 역할을 하고 정량적 위험분석은 보조적인 역할을 담당한다. 그런데 이제까지 정성적 위험분석단계에서 위험도를 계량화하는 방법으로 적용되어온 위험도 산정 공식은 발생확률과 영향을 단순히 곱하는 식으로서 결과 값들은 저위험도에 편중된 분포를 나타낸다. 이에 대한 대안으로 고위험도에 편중되는 산정 공식이 제안되었으나, 위험도 분포가 저위험도 또는 고위험도에 편중하게 될 경우 대부분의 자연현상이 정상분포에 가깝다는 통계학적인 일반논리에 부합되지 않는다. 본 연구에서는 위험도의 분포가 중앙에 집중되는 새로운 위험도 산정방법을 제안하고자 한다. 이를 통해 위험도 분포가 자연현상의 정상분포와 유사한 형식으로 표현됨으로써 위험에 대응하는 수준이 고위험도 또는 저위험도에 치우지지 않고 중간위험도에서 합리적으로 선택될 수 있게 하고자 한다. 나아가 위험도 산정방법에 대한 추가적인 선택사항을 제공함으로써 위험분석 방법의 융통성과 합리성을 향상시키는데도 일조하고자 한다.