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Amado Cristina (University of Minho, Braga; CREATES, Aarhus University), Silvennoinen Annastiina (School of Economics and Finance, Queensland University of Technology, Brisbane), Teräsvirta Timo (CREATES, Aarhus University; C.A.S.E., Humboldt-Universität zu Berlin)
Modelling and Forecasting WIG20 Daily Returns
Central European Journal of Economic Modelling and Econometrics (CEJEME), 2017, vol. 9, nr 3, s. 173-200, rys., tab., appendix, bibliogr. 35 poz.
Słowa kluczowe
Model GARCH, Indeks giełdowy, Procesy zmienności stochastycznej
GARCH model, Stock market indexes, Stochastic Volatility Processes
summ.; Klasyfikacja JEL: C32, C52, C58
The purpose of this paper is to model daily returns of the WIG20 index. The idea is to consider a model that explicitly takes changes in the amplitude of the clusters of volatility into account. This variation is modelled by a positive-valued deterministic component. A novelty in specification of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity. (original abstract)
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Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Pełny tekst
  1. Akaike H. (1974), A new look at statistical model identification, IEEE Transactions on Automatic Control AC-19, 716-723.
  2. Amado C., Teräsvirta T. (2008), Modelling conditional and unconditional heteroskedasticity with smoothly time-varying structure, SSE/EFI Working Paper Series in Economics and Finance 691, Stockholm School of Economics.
  3. Amado C., Teräsvirta T. (2013), Modelling volatility by variance decomposition, Journal of Econometrics 175, 153-165.
  4. Amado C., Teräsvirta T. (2014), Modelling changes in the unconditional variance of long stock return series, Journal of Empirical Finance 25, 15-35.
  5. Amado C., Teräsvirta T. (2017), Specification and testing of multiplicative time-varying GARCH models with applications, Econometric Reviews 36, 421- 446.
  6. Baillie R. T., Bollerslev T., Mikkelsen H. O. (1996), Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 74, 3-30.
  7. Bollerslev T. (1986), Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, 307-327.
  8. Brdyś M. A., Borowa A., Idźkowiak P., Brdyś M. T.: 2009, Adaptive prediction of stock exchange indices by state space wavelet networks, International Journal of Applied Mathematics and Computer Science 19, 337- 348.
  9. Brownlees C. T., Gallo G. M. (2010), Comparison of volatility measures: A risk management perspective, Journal of Financial Econometrics 8, 29-56.
  10. Chan F., Theoharakis B. (2011), Estimating m-regimes STAR-GARCH model using QMLE with parameter transformation, Mathematics and Computers in Simulation 81, 1385-1396.
  11. Czapkiewicz A., Basiura B. (2014), The position of the WIG index in comparison with selected market indices in boom and bust periods, Statistics in Transition 15, 427-436.
  12. Davidson J. (2004), Moment and memory properties of linear conditional heteroscedasticity models, and a new model, Journal of Business and Economic Statistics 22, 16-29.
  13. Diebold F. X. (1986), Modeling the persistence of conditional variances: A comment, Econometric Reviews 5, 51-56.
  14. Ekner L. E., Nejstgaard E. (2013), Parameter identification in the logistic STAR model, Discussion Paper 13-07, Department of Economics, University of Copenhagen.
  15. Engle R. F., Rangel J. G. (2008), The spline-GARCH model for low-frequency volatility and its global macroeconomic causes, Review of Financial Studies 21, 1187-1222.
  16. Feng Y. (2004), Simultaneously modeling conditional heteroskedasticity and scale change, Econometric Theory 20, 563-596.
  17. Glosten L. W., Jagannathan R., Runkle D. E. (1993), On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance 48, 1779-1801.
  18. Goodwin B. K., Holt M. T., Prestemon J. P. (2011), North American oriented strand board markets, arbitrage activity, and market price dynamics: A smooth transition approach, American Journal of Agricultural Economics 93, 993-1014.
  19. Hagerud G. E. (1997), A new non-linear GARCH model, EFI Economic Research Institute, Stockholm.
  20. Hall A. D., Silvennoinen A., Teräsvirta T. (2017), Building multiplicative time-varying smooth transition conditional correlation GARCH models, work in progress, School of Economics and Finance, Queensland University of Technology.
  21. Hansen P. R., Lunde A., Nason J. M. (2011), The model confidence set, Econometrica 79, 453-497.
  22. He C., Teräsvirta T. (1999), Properties of moments of a family of GARCH processes, Journal of Econometrics 92, 173-192.
  23. Lamoureux C. G., Lastrapes W. G. (1990), Persistence in variance, structural change and the GARCH model, Journal of Business and Economic Statistics 8, 225-234.
  24. Lundbergh S., Teräsvirta T. (2002), Evaluating GARCH models, Journal of Econometrics 110, 417-435.
  25. Makiel K. (2012), ARIMA-GARCH models in estimating market risk using value at risk for WIG20 index, Financial Internet Quarterly "e-Finance" 8, 25-33.
  26. Malecka M. (2013), GARCH process application in risk valuation for WIG20 index, Acta Universitatis Lodziensis Folia Oeconomica 285, 209-220.
  27. Mazur B., Pipień M. (2012), On the empirical importance of periodicity in the volatility of financial returns - time varying GARCH as a second order APC(2) process, Central European Journal of Economic Modelling and Econometrics 4, 95-116.
  28. Patton A. J. (2011), Volatility forecast comparison using imperfect volatility proxies, Journal of Econometrics 160, 246-256.
  29. Rissanen J. (1978), Modeling by shortest data description, Automatica 14, 465- 471.
  30. Schwarz G. (1978), Estimating the dimension of a model, Annals of Statistics 6, 461-464.
  31. Silvennoinen A., Teräsvirta T. (2016), Testing constancy of unconditional variance in volatility models by misspecification and specification tests, Studies in Nonlinear Dynamics and Econometrics 20, 347-364.
  32. Song P. X., Fan Y., Kalbfleisch J. D. (2005), Maximization by parts in likelihood inference, Journal of the American Statistical Association 100, 1145- 1158.
  33. Teräsvirta, T. (1994), Specification, estimation, and evaluation of smooth transition autoregressive models, Journal of the American Statistical Association 89, 208-218.
  34. van Bellegem S., von Sachs R. (2004), Forecasting economic time series with unconditional time-varying variance, International Journal of Forecasting 20, 611-627.
  35. Wooldridge J. M. (1991), On the application of robust, regression-based diagnostics to models of conditional mean and conditional variances, Journal of Econometrics 47, 5-46.
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