DOI QR코드

DOI QR Code

Quadratic GARCH Models: Introduction and Applications

이차형식 변동성 Q-GARCH 모형의 비교연구

  • Park, Jin-A (Department of Statistics, Sookmyung Women's University) ;
  • Choi, Moon-Sun (Department of Statistics, Sookmyung Women's University) ;
  • Hwan, Sun-Young (Department of Statistics, Sookmyung Women's University)
  • 박진아 (숙명여자대학교 통계학과) ;
  • 최문선 (숙명여자대학교 통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Received : 20101000
  • Accepted : 20101200
  • Published : 2011.02.28

Abstract

In GARCH context, the conditional variance (or volatility) is of a quadratic function of the observation process. Examine standard ARCH/GARCH and their variant models in terms of quadratic formulations and it is interesting to note that most models in GARCH context have contained neither the first order term nor the interaction term. In this paper, we consider three models possessing the first order and/or interaction terms in the formulation of conditional variances, viz., quadratic GARCH, absolute value GARCH and bilinear GARCH processes. These models are investigated with a view to model comparisons and applications to financial time series in Korea

다양한 GARCH류 모형들의 변동성 함수를 살펴보면 흥미롭게도 거의 대부분 모형에서 수익률의 일차항( rst or der term)이나 수익률과 변동성의 교차항(interaction term)이 나타나지 않는다. 일차항과 교차항은 변동성의 비대칭성을 설명하는 역할을 할 수 있으며 $h_t$의 회귀분석식의 형태로 볼 때 변동성 함수의 일반적인 이차형식(quadratic form)을 구성한다고 할 수 있다. 본 논문에서는 변동성과 수익률들 사이의 교차항 및 일차항을 포함한 이차형식(quadratic form) 변동성 모형들을 소개하고, 국내 금융시계열 자료에 적용한 후 비교 분석하고자 한다.

Keywords

References

  1. 홍선영, 최성미, 박진아, 백지선, 황선영 (2009). 지속-변동성을 가진 비대칭 TGARCH 모형을 이용한 국내금융시 계열 분석, <한국통계학회논문집>, 16, 605-614.
  2. Black, F. (1976). Studies of Stock Price Volatilities Changes, Proceedings of the 1976 Business Meeting of the Business and Economic Statistics Section, American Statistical Association, 177-181.
  3. Bollerslev, T. (1986). Generalized autoregressive heteroskedasticity, Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  4. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom in ation, Econometrica, 50, 987-1008. https://doi.org/10.2307/1912773
  5. Engle, R. F. and Ng, V.K. (1993). Measuring and testing the impact of news on volatility, Journal of Finance, 48, 1749-1778. https://doi.org/10.2307/2329066
  6. Glosten, L. R., Jagannathan, R. and Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48, 1779-1801. https://doi.org/10.2307/2329067
  7. Haas, M. (2009). Persistence in volatility, conditional variance and the Taylor property in absolute-value-GARCH processes, Statistics & Probability Letters , 79, 1674-1683. https://doi.org/10.1016/j.spl.2009.04.017
  8. Rabemananjara, R. and Zakoian, J. M. (1993). Threshold ARCH models and asymmetries in volatility, Journal of Applied Econometrics, 8, 31-49. https://doi.org/10.1002/jae.3950080104
  9. Sentana, E. (1995). Quadratic ARCH models, The Review of Economic Studies, 62, 639-661. https://doi.org/10.2307/2298081
  10. Storti, G. and Vitale, C. (2003). BL-GARCH models and asymmetries in volatility, Statistical Methods and Applications, 12, 19-40. https://doi.org/10.1007/BF02511581

Cited by

  1. Asymmetric CCC Modelling in Multivariate-GARCH with Illustrations of Multivariate Financial Data vol.24, pp.5, 2011, https://doi.org/10.5351/KJAS.2011.24.5.821