• Title/Summary/Keyword: Value at Risk

Search Result 79, Processing Time 0.102 seconds

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

  • Park, Koo-Hyun;Shim, Eun-Tak
    • Korean Management Science Review
    • /
    • v.28 no.2
    • /
    • pp.1-16
    • /
    • 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.

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

  • Kim, Minjo;Lee, Sangyeol
    • The Korean Journal of Applied Statistics
    • /
    • v.29 no.1
    • /
    • pp.171-180
    • /
    • 2016
  • Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.

Value at Risk Forecasting Based on Quantile Regression for GARCH Models

  • Lee, Sang-Yeol;Noh, Jung-Sik
    • The Korean Journal of Applied Statistics
    • /
    • v.23 no.4
    • /
    • pp.669-681
    • /
    • 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.

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
    • /
    • v.19 no.4
    • /
    • pp.1027-1036
    • /
    • 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.

  • PDF

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

  • Park, Koohyun
    • Korean Management Science Review
    • /
    • v.30 no.3
    • /
    • pp.55-70
    • /
    • 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.

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

  • Kim, Yong-Tae;Shim, Joo-Yong;Lee, Jang-Taek;Hwang, Chang-Ha
    • Communications for Statistical Applications and Methods
    • /
    • v.16 no.5
    • /
    • pp.791-801
    • /
    • 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.

FUZZY RISK MEASURES AND ITS APPLICATION TO PORTFOLIO OPTIMIZATION

  • Ma, Xiaoxian;Zhao, Qingzhen;Liu, Fangai
    • Journal of applied mathematics & informatics
    • /
    • v.27 no.3_4
    • /
    • pp.843-856
    • /
    • 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.

  • PDF

Estimating VaR(Value-at-Risk) of non-listed and newly listed companies using Case Based Reasoning (사례기반추론을 이용한 비상장기업 및 신규상장기업의 VaR 추정)

  • 최경덕;노승종
    • Journal of Intelligence and Information Systems
    • /
    • v.8 no.1
    • /
    • pp.1-13
    • /
    • 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.

  • PDF

Value-at-Risk Estimation of the KOSPI Returns by Employing Long-Memory Volatility Models (장기기억 변동성 모형을 이용한 KOSPI 수익률의 Value-at-Risk의 추정)

  • Oh, Jeongjun;Kim, Sunggon
    • The Korean Journal of Applied Statistics
    • /
    • v.26 no.1
    • /
    • pp.163-185
    • /
    • 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.

Comparison of Dimension Reduction Methods for Time Series Factor Analysis: A Case Study (Value at Risk의 사후검증을 통한 다변량 시계열자료의 차원축소 방법의 비교: 사례분석)

  • Lee, Dae-Su;Song, Seong-Joo
    • The Korean Journal of Applied Statistics
    • /
    • v.24 no.4
    • /
    • pp.597-607
    • /
    • 2011
  • Value at Risk(VaR) is being widely used as a simple tool for measuring financial risk. Although VaR has a few weak points, it is used as a basic risk measure due to its simplicity and easiness of understanding. However, it becomes very difficult to estimate the volatility of the portfolio (essential to compute its VaR) when the number of assets in the portfolio is large. In this case, we can consider the application of a dimension reduction technique; however, the ordinary factor analysis cannot be applied directly to financial data due to autocorrelation. In this paper, we suggest a dimension reduction method that uses the time-series factor analysis and DCC(Dynamic Conditional Correlation) GARCH model. We also compare the method using time-series factor analysis with the existing method using ordinary factor analysis by backtesting the VaR of real data from the Korean stock market.