- Volume 24 Issue 6
DOI QR Code
Empirical Analyses of Asymmetric Conditional Heteroscedasticities for the KOSPI and Korean Won-US Dollar Exchange Rate
KOSPI지수와 원-달러 환율의 변동성의 비대칭성에 대한 실증연구
Maeng, Hye-Young;Shin, Dong-Wan
- Received : 20110900
- Accepted : 20111000
- Published : 2011.12.31
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.
Asymmetric Volatility;Garch Models;Kernel density plot;Bootstrap
- 김세완 (2009). 경기변동을 고려한 주식수익률과 변동성 관계의 변화: 비대칭 GARCH 모형을 이용하여, 금융연구, 23, 1-28.
- 박주연, 여인권 (2009). 변화된 GARCH모형에서의 예측값 추정, 응용통계연구, 22, 971-979.
- 성범용, 김기석 (2000). 뉴스충격이 원/달러환율의 변동성에 미치는 효과분석, 국제경제연구, 6, 161-180.
- 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
- 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.
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
- Box, G. E. P. and Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.
- 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
- 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
- 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
- Efron, B. (1979). Bootstrap methods: Another look at the jackknife, Annals of Statistics, 7, 1-26. https://doi.org/10.1214/aos/1176344552
- Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom, Econometrica, 46, 1287-1294.
- 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
- 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
- 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
- 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
- Hwang, E. J. and Shin, D. W. (2010). Asymptotics and optimal bandwidth selection for kernel estimators of mode under psi-weak dependence.
- 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
- 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
- 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.
- Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370. https://doi.org/10.2307/2938260
- Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization, Wiley.
- 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.
- Silverman, B. W. (1986). Density Estimation, Chapman and Hall, London.
- Wu, G. (2001). The determinants of asymmetric volatility, Review of Financial Studies, 14, 837-859. https://doi.org/10.1093/rfs/14.3.837
- 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
Supported by : 연구재단