Garch forecasting performance under different distribution assumptions

Journal of Forecasting

Volumn 25, Issue 8, 2006, Pages 561-578

  Wilhelmsson, Anders ^a  

^a HANKEN SCHOOL OF ECONOMICS   (Finland)

Author keywords

GARCH; Intra day data; Realized volatility; Volatility forecasts

Indexed keywords

COMPUTATIONAL COMPLEXITY; ERROR ANALYSIS; MATHEMATICAL MODELS; STATISTICAL METHODS; THEOREM PROVING;

GARCH; INTRA-DAY DATA; REALIZED VOLATILITY; VOLATILITY FORECASTS;

PROBABILITY DISTRIBUTIONS;

EID: 33845968035     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.1009     Document Type: Article

References (31)

1. Andersen TG, Bollerslev T. 1997. Heterogeneous information arrivals and return variance dynamics: uncovering the long-run in high frequency returns. Journal of Finance 52: 975-1005.
2. Andersen TG, Bollerslev T. 1998. Answering the skeptics: yes standard variance models do provide accurate forecasts. International Economic Review 39: 885-905.
3. Andersen TG, Bollerslev T, Lange S. 1999. Forecasting financial market variance: sample frequency vis-à-vis forecast horizon. Journal of Empirical Finance 6: 457-477.
4. Andersen TG, Bollerslev T, Diebold FX, Ebens H. 2001. The distribution of stock return variance. Journal of Financial Economics 61: 43-76.
5. Andersen TG, Bollerslev T, Meddahi N. 2004. Analytic evaluation of variance forecasts. International Economic Review 45: 1079-1110.
6. Andersen TG, Bollerslev T, Diebold FX. 2005. Parametric and nonparametric volatility measurement. In Handbook of Financial Econometrics, Hansen LP, Aït-Sahalia Y (eds). North-Holland: Amsterdam (forthcoming).
7. Bollerslev T. 1986. Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 31: 307-327.
8. Bollerslev T. 1987. A conditionally heteroskedastic time series model for speculative prices and rates of return. Review of Economics and Statistics 69: 542-547.
9. Brännäs K, Nordman N. 2003. An alternative conditional asymmetry specification for stock returns. Applied Financial Economics 13: 537-541.
10. Diebold FX, Mariano R. 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13: 253-263.
11. Engle R. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50: 987-1008.
12. Engle R, Lilien DM, Robins RP. 1987. Estimating time varying risk premia in the term structure: the ARCH-M model. Econometrica 55: 391-407.
13. Figlewski S. 1997. Forecasting variance. Financial Markets, Institutions and Instruments 6: 1-88.
14. Franses P, Ghijsels H. 1999. Additive outliers, Garch and forecasting variance. International Journal of Forecasting 15: 1-9.
15. Glosten L, Jagannathan R, Runkle D. 1993. On the relation between the expected value and the variance of the nominal excess returns on stocks. Journal of Finance 48: 1779-1801.
16. Hamilton J, Susmel R. 1994. Autoregressive conditional heteroscedasticity and changes in regime. Journal of Econometrics 64: 307-333.
17. Hansen B. 1994. Autoregressive conditional density estimation. International Economic Review 35: 705-730.
18. Hansen P, Lunde A. 2004. An unbiased measure of realized variance. Working paper, Brown University and Aarhus School of Business, http://www.stanford.edu/~prhansen/ [May 2006].
19. Hansen P, Lunde A. 2005a. A forecast comparison of variance models: does anything beat a GARCH (1, 1)? Journal of Applied Econometrics 20: 873-889.
20. Hansen P, Lunde A. 2005b. A realized variance for the whole day based on intermittent high-frequency data. Journal of Financial Econometrics 3: 525-554.
21. Hansen P, Lunde A. 2006. Consistent ranking of variance models. Journal of Econometrics 131: 97-121.
22. Harvey CR, Siddique A. 1999. Autoregressive conditional skewness. Journal of Financial and Quantitative Analysis 34: 465-488.
23. Jarque CM, Bera AK. 1987. A test for normality of observations and regression residuals. International Statistical Review 55: 163-172.
24. Jondeau E, Rockinger M. 2003. Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements. Journal of Economic Dynamics and Control 27: 1699-1737.
25. Kim T, White H. 2004. On more robust estimation of skewness and kurtosis: simulation and application to the S&P500 index. Finance Research Letters 1: 56-73.
26. Lopez J. 2001. Evaluating the predictive accuracy of variance models. Journal of Forecasting 20: 87-109.
27. Malmsten H, Teräsvirta T. 2004. Stylized facts of financial time series and three popular models of variance. SSE/EFI Working Paper Series in Economics and Finance no 563.
28. McMillan D, Speight A. 2004. Daily volatility forecasts: reassessing the performance of Garch models. Journal of Forecasting 23: 449-460.
29. Nelson DB. 1991. Conditional heteroskedasticity in asset pricing: a new approach. Econometrica 59: 347-370.
30. Poon S, Granger C. 2003. Forecasting variance in financial markets: a review. Journal of Economic Literature 41: 478-540.
31. Taylor S. 1986. Modelling Financial Time Series. Wiley: Chichester.