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원-팩터 모형을 이용한 KOSPI200지수 구성종목의 최적 포트폴리오 구성 및 VaR 측정

Optimal portfolio and VaR of KOSPI200 using One-factor model

  • Ko, Kwang Yee (Department of Statistics, Chonnam National University) ;
  • Son, Young Sook (Department of Statistics, Chonnam National University)
  • 투고 : 2014.12.30
  • 심사 : 2015.02.06
  • 발행 : 2015.03.31

초록

J. P. Morgan의 RiskMetrics을 기반으로 하는 현행 VaR 모형은 구조적으로 예측된 미래의 경기상황을 반영할 수가 없다. 본 연구에서는 주가의 변동요인인 워너 확률과정을 기업의 고유요인과 경기변동요인으로 구분한 원-팩터 (One-factor) 모형을 제안하여 미래 경기변동 공통요인을 미리 예측하여 반영함에 따라 장기적인 주식 보유기간에도 선제적인 리스크관리를 실시할 수 있도록 한다. 또한 미래 경기변동요인이 예측값으로 고정됨에 따라 포트폴리오를 구성하는 주가들이 서로 독립성을 만족하게 되여 포트폴리오의 분산을 최소화하는 각 주식의 투자금액을 결정하는 것은 물론 포트폴리오 VaR가 개별 VaR의 합으로 분해되어 목표로 하는 최대손실금액에 따른 포트폴리오의 구성을 효율적으로 실시할 수가 있다.

he current VaR model based on the J.P. Morgan's RiskMetrics structurally can not reflect the future economic situation. In this study, we propose a One-factor model resulting from the Wiener stochastic process decomposed into a systematic risk factor and an idiosyncratic risk factor. Therefore, we are able to perform a preemptive risk management by means of reflecting the predicted common risk factors in the model. Stocks in the portfolio are satisfied with the independence to each other because the common factors are fixed by the predicted value. Therefore, we can easily determine the investment in each stock to minimize the variance of the portfolio. In addition, the portfolio VaR is decomposed into the sum of the individual VaR. So we can effectively implement the constitution of the portfolio to meet the target maximum losses.

키워드

참고문헌

  1. Basel Committee on Banking Supervision. (1995). An internal model-based approach to market risk capital requirements, BIS, Basel, Switzerland.
  2. Basel Committee on Banking Supervision. (2005). International convergence of capital measurement and capital standards, BIS, Basel, Switzerland.
  3. Bessis, J. (2002). Risk management in bankin, John Wiley & Sons, New York.
  4. Byun, B. G., Yoo, D. S. and Lim, J. T. (2013). Validity assessment of VaR with Laplacian distribution. Journal of the Korean Data & Information Science Society, 24, 1263-1274. https://doi.org/10.7465/jkdi.2013.24.6.1263
  5. Choi, I., Kang, D., Lee, J., Kang, M., Song, D., Shin, S. and Son, Y. S. (2012). Prediction of the industrial stock price index using domestic and foreign economic indices. Journal of the Korean Data & Information Science Society, 23, 271-283. https://doi.org/10.7465/jkdi.2012.23.2.271
  6. Gordy, M. (2003). A risk-factor model foundation for ratings-based bank capital rules. Journal of Financial Intermediation, 12, 199-232. https://doi.org/10.1016/S1042-9573(03)00040-8
  7. Johansson, F., Seiles, M. J. and Tjarnberg, M. (1999). Measuring Downside Portfolio Risks. Journal of Portfolio Management, Fall, 96-107.
  8. Jorion, P. (2007). Value at risk, 3rd edition, McGraw-Hill, New York.
  9. J. P. Morgan, (1996). RiskMetrics - Technical document, 4th edition, New York.
  10. Neftci, Salih F. (2000). Value at risk calculations, Extreme events and tail Estimations. Journal of Derivatives, Spring, 23-38.
  11. Park, K. H., Ko, K. Y. and Beak, J. S. (2013). An One-factor VaR model for stock portfolio. The Journal of the Applied Statistics, 26, 471-481.
  12. Vasicek, O. (2002). The distribution of loan portfolio value. Risk, 15, 160-162.

피인용 문헌

  1. Properties of alternative VaR for multivariate normal distributions vol.27, pp.6, 2016, https://doi.org/10.7465/jkdi.2016.27.6.1453
  2. The GARCH-GPD in market risks modeling: An empirical exposition on KOSPI vol.27, pp.6, 2016, https://doi.org/10.7465/jkdi.2016.27.6.1661
  3. 가중 포트폴리오에서의 CTE vol.28, pp.1, 2017, https://doi.org/10.7465/jkdi.2017.28.1.119
  4. 정규혼합모형의 오차를 갖는 GARCH 모형을 이용한 옵션가격결정에 대한 실증연구 vol.28, pp.2, 2015, https://doi.org/10.7465/jkdi.2017.28.2.251
  5. Multivariate CTE for copula distributions vol.28, pp.2, 2015, https://doi.org/10.7465/jkdi.2017.28.2.421