Asymmetric GARCH and the financial crisis: a preliminary study

Výrost, Tomáš and Baumöhl, Eduard (2009): Asymmetric GARCH and the financial crisis: a preliminary study.

Abstract

The paper deals with estimation of both general GARCH as well as asymmetric EGARCH and TGARCH models, used to model the leverage effect of good news and bad news on market volatility. We estimate the models using daily returns of S&P 500 stock index and describe the news impact curves (NICs) for these models. When estimating the crisis series, we show the possibility of using a news impact surface to describe the results from models of higher orders.

• Item Type: MPRA Paper
• Language: English
• Keywords: volatility modeling, financial crisis, asymmetric GARCH class models, news impact curve
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; G - Financial Economics > G0 - General > G01 - Financial Crises

References

[1] ALBERG, D. – SHALIT, H. – YOSEF, R. 2008. Estimating Stock Market Volatility Using Asymmetric GARCH Models. In: Applied Financial Economics, 2008, vol. 18, no. 15, p. 1201 – 1208. ISSN 1466–4305

[2] BOLLERSLEV, T. 1986. Generalized Autoregressive Conditional Heteroskedasticity. In: Journal of Econometrics, 1986, vol. 31, no. 3, p. 307-327. ISSN 0304-4076

[3] DING, Z. – GRANGER, C. – ENGLE, R. 1993. A Long Memory Property of Stock Returns and a New Model. In: Journal of Emirical Finance, 1993, vol. 1, no. 1, p. 83 – 106. ISSN 0927-5398

[4] ENGLE, R. – NG, V. 1993. Measuring and Testing the Impact of News on Volatility. In: Journal of Finance, 1993, vol. 48, no. 5, p. 1749 – 1778. ISSN 0022-1082

[5] ENGLE, R. F. 1982. Autoregressive Conditional Heteroskedasticity with Estimates of Variance of United Kingdom Inflation. In: Econometrica, 1982, vol. 50, no. 4, p. 987-1008. ISSN 0012-9682

[6] GLOSTEN, L. – JAGANNATHAN, R. – RUNKLE, D. 1993. On the Relationship between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. In: Journal of Finance, 1993, vol. 48, no. 5, p. 1779 – 1801. ISSN 0022-1082

[7] KOČENDA, E. – ČERNÝ, A. 2007. Elements of Time Series Econometrics: An Applied Approach. Praha : Karolinum Press, 2007. ISBN 978-80-246-1370-3

[8] LAURENT, S. 2004. Analytical Derivates of the APARCH Model. In: Computational Economics, 2004, vol. 24, no. 1, p. 51 – 57. ISSN 0927-7099

[9] MANDELBROT, B. 1963. The Variation of Certain Speculatives Bubbles. In: Journal of Business, 1963, vol. 36, p. 394 – 419. ISSN 0021-9398

[10] NELSON, D. B. 1991. Conditional Heteroskedasticity in Asset Returns: A New Approach. In: Econometrica, 1991, vol. 59, no. 2, p. 347 – 370. ISSN 0012-9682

[11] RABEMANANJARA, R. – ZAKOIAN, J. M. 1993. Threshold Arch Models and Asymmetries in Volatility. In: Journal of Applied Econometrics, 1993, vol. 8, no. 1, p. 31 – 49. ISSN 0883-7252

[12] ZAKOIAN, J. M. 1994. Threshold Heteroskedastic Models. In: Journal of Economic Dynamics and Control, 1994, vol. 18, no. 5, p. 931 – 955. ISSN 0165-1889