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Empirical Analyses of Asymmetric Conditional Heteroscedasticities for the KOSPI and Korean Won-US Dollar Exchange Rate

KOSPI지수와 원-달러 환율의 변동성의 비대칭성에 대한 실증연구

  • 맹혜영 (이화여자대학교 통계학과) ;
  • 신동완 (이화여자대학교 통계학과)
  • Received : 20110900
  • Accepted : 20111000
  • Published : 2011.12.31

Abstract

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

본 논문에서는 KOSPI지수와 원-달러 환율의 로그수익률을 사용하여 비대칭 이분산성에 대해 연구한다. 커널 density plot과 상승기와 하강기의 평균, 분산을 검토하여 이들 시계열의 변동의 비대칭성에 대한 윤곽을 파악하고 GARCH군의 여러 비대칭 모형을 적합하여 비대칭성을 실증적으로 파악한다. 또한 최종선택 모형인 EGARCH 모형을 바탕으로 부트스트래핑을 사용하여 미래 시점의 변동성인 조건부 분산의 기대치를 예측하고 예측표준오차를 구해본다.

Keywords

References

  1. 김세완 (2009). 경기변동을 고려한 주식수익률과 변동성 관계의 변화: 비대칭 GARCH 모형을 이용하여, 금융연구, 23, 1-28.
  2. 박주연, 여인권 (2009). 변화된 GARCH모형에서의 예측값 추정, 응용통계연구, 22, 971-979.
  3. 성범용, 김기석 (2000). 뉴스충격이 원/달러환율의 변동성에 미치는 효과분석, 국제경제연구, 6, 161-180.
  4. Bekaert, G. and Wu, G. (2000). Asymmetric volatility and risks in equity markets, The Review of Financial Studies, 13, 1-42. https://doi.org/10.1093/rfs/13.1.1
  5. Black, F. (1976). Studies in stock price volatility changes, Proceedings of the 1976 business meeting of the business and economic statistics section, American Statistical Association, 177-181.
  6. Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  7. Box, G. E. P. and Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.
  8. Chen, B., Gel, Y. R., Balakrishna, N. and Abraham, B. (2011). Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes, Journal of Forecasting, 30, 51-71. https://doi.org/10.1002/for.1197
  9. Christie, A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects, Journal of Financial Economics, 10, 407-432. https://doi.org/10.1016/0304-405X(82)90018-6
  10. Ding, Z., Granger, C. W. J. and Engle, R. F. (1993). A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1, 83-106. https://doi.org/10.1016/0927-5398(93)90006-D
  11. Efron, B. (1979). Bootstrap methods: Another look at the jackknife, Annals of Statistics, 7, 1-26. https://doi.org/10.1214/aos/1176344552
  12. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom, Econometrica, 46, 1287-1294.
  13. Engle, R. F. and Ng, V. (1993). Measuring and testing the impact of news on volatility, Journal of Finance, 48, 1749-1778. https://doi.org/10.2307/2329066
  14. Glosten, L., Jagannatha, R. and Runkle, D. (1993). On the relation between expected excess return on stocks, Journal of Finance, 48, 1779-1801. https://doi.org/10.2307/2329067
  15. Henry, O., Olekalns, N. and Shields, K. (2010). Sign and phase asymmetry: News, economic activity and the stock market, Journal of Macroeconomics, 32, 1083-1100. https://doi.org/10.1016/j.jmacro.2010.06.006
  16. Higgins, M. L. and Bera, A. K. (1992). A class of nonlinear arch models, International Economic Review, 33, 137-158. https://doi.org/10.2307/2526988
  17. Hwang, E. J. and Shin, D. W. (2010). Asymptotics and optimal bandwidth selection for kernel estimators of mode under psi-weak dependence.
  18. Kumar, R. and Dhankar, R. S. (2010). Empirical analysis of conditional heteroskedasticity in time series of stock returns and asymmetric effect on volatility, Global Business Review, 11, 21-33. https://doi.org/10.1177/097215090901100102
  19. Kunsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations, Annals of Statistics, 17, 1217-1241. https://doi.org/10.1214/aos/1176347265
  20. Liu, R. Y. and Singh, K. (1992). Moving Blocks Jackknife and Bootstrap Capture Weak Dependence, In Exploring the Limits of Bootstrap (R. LePage and L. Billard, eds.), 225-248, Wiley, New York.
  21. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370. https://doi.org/10.2307/2938260
  22. Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization, Wiley.
  23. Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation, Journal of the Royal Statistical Society, Series B, 53, 683-690.
  24. Silverman, B. W. (1986). Density Estimation, Chapman and Hall, London.
  25. Wu, G. (2001). The determinants of asymmetric volatility, Review of Financial Studies, 14, 837-859. https://doi.org/10.1093/rfs/14.3.837
  26. Zakoian, J. M. (1994). Threshold heteroskedastic models, Journal of Economic Dynamics and Control, 18, 931-955. https://doi.org/10.1016/0165-1889(94)90039-6