Abstract
The modelling and forecasting of volatility in Time Series has been receiving great attention from researchers over the past years. In this topic, GARCH models are one of the most popular models. In this work, the effects of choosing different distribution families for the innovation process on asymmetric GARCH models are investigated. In particular, we compare A-PARCH models for the IBM stock data with Normal, Student’s t, Generalized Error, skew Student’s t and Pearson type-IV distributions. The main findings indicate that distributions with skewness have better performance than non-skewed distributions and that the Pearson IV distribution arises as a great candidate for the innovation process on asymmetric models.
Funded by CONICYT-PCHA/Magíster Nacional/2014 - 22141595
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Alberg, D., Shalit, H., Yosef, R.: Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics 18(15), 1201–1208 (2008)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307–327 (1986)
Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. Journal of Business & Economic Statistics 20(1), 134–144 (2002)
Ding, Z., Granger, C.W.J., Engle, R.F.: A long memory property of stock market returns and a new model. Journal of Empirical Finance 1(1), 83–106 (1993)
Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society 50(4), 987–1007 (1982)
Engle, R.F., Patton, A.J.: What good is a volatility model. Quantitative Finance 1(2), 237–245 (2001)
Fama, E.F.: The Behavior of Stock-Market Prices 38(1), 34–105 (1965)
Fernandez, C., Steel, M.F.J.: On Bayesian Modeling of Fat Tails and Skewness. Journal of the American Statistical Association 93(441), 359–371 (1998)
Hansen, P.R., Lunde, A.: A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics 20(7), 873–889 (2005)
Jondeau, E., Poon, S.H., Rockinger, M.: Financial modeling under non-Gaussian distributions. Springer Science & Business Media (2007)
Klass, M.J., Mark, B.G.: On the Estimation of Security Price Volatility from Historical Data. The Journal of Business 53, 67–78 (1980)
Magdalinos, M.A., Mitsopoulos, G.P.: Conditional heteroskedasticity models with pearson family disturbances. The Refinement of Econometric Estimation and Test Procedures, Finite Sample and Asymptotic Analysis, 1–33 (2007)
Mandelbrot, B.: The Variation of Certain Speculative Prices (1963)
Nagahara, Y.: The PDF and CF of Pearson type IV distributions and the ML estimation of the parameters. Statistics & Probability Letters 43(3), 251–264 (1999)
Nelson, D.B.: Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society 59, 347–370 (1991)
Peters, J.P.: Estimating and forecasting volatility of stock indices using asymmetric GARCH models and (Skewed) Student-t densities. Preprint, University of Liege, Belgium (2001)
Poon, S.H.: A practical guide to forecasting financial market volatility. Wiley Finance (2005)
Stavroyiannis, S., Makris, I., Nikolaidis, V., Zarangas, L.: Econometric modeling and value-at-risk using the Pearson type-IV distribution. International Review of Financial Analysis 22, 10–17 (2012)
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Acuña, D., Allende-Cid, H., Allende, H. (2015). The Effect of Innovation Assumptions on Asymmetric GARCH Models for Volatility Forecasting. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_63
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