How to Increase Accuracy of Volatility Forecasts Based on GARCH Models

• Autorzy: Piotr Fiszeder
• Języki publikacji: EN

Abstrakty

EN

There is a large literature on volatility forecasting (see Poon and Granger, 2003), but nevertheless it is difficult to extract a coherent set of prescriptions concerning the most appropriate empirical procedure for tackling this issue. The results of empirical analyses are unclear and often even contradictory. Various conditional variance specifications within the parametric GARCH class of models were proposed in the literature, but there is no consensus on the relative quality of out-of-sample forecasts of those formulations. Analyses with GARCH models for the Polish stock market were performed among others by Piontek (2003), Doman and Doman (2004), Fiszeder (2004a, 2004b, 2005), Osiewalski, Pajor and Pipień (2004) and Pipień (2006). Only in the investigations of Doman and Doman and Fiszeder were intraday data used for evaluation of forecasts quality. In this paper a significantly wider class of GARCH models, especially models extended with additional information, was used and the research period was wider, which could significantly influence results. The main purpose of this study is to compare a performance of the different specifications of GARCH models for predicting volatility. Additional information used in construction of the GARCH model or in estimation of its parameters does not always lead to an increase in accuracy of volatility forecasts. The plan for the rest of the paper is as follows. Section 2 outlines the competing methods used in the analysis and the measures used to assess the performance of the candidate models. In section 3 accuracy of volatility forecasts for WIG20 index was analysed and section 4 concludes. (fragment of text)

Słowa kluczowe

EN

Forecasting; Forecasting models; GARCH model; Stock exchange prediction

PL

Prognozowanie; Modele prognostyczne; Model GARCH; Prognozowanie notowań giełdowych

• Czasopismo: Dynamic Econometric Models
• Rocznik: 2008
• Tom: 8
• Strony: 111--118

Twórcy

autor

Piotr Fiszeder

• Nicolaus Copernicus University in Toruń, Poland

Bibliografia

• Doman, M., Doman, R. (2004), Ekonometryczne modelowanie dynamiki polskiego rynku finansowego, AE w Poznaniu, Poznań.
• Fiszeder, P. (2004a), Forecasting Volatility with GARCH Models, materiały konferencji MACROMODELS'2003, Wydawnictwo Uniwersytetu Łódzkiego, Łódź.
• Fiszeder, P. (2004b), Prognozowanie zmienności na podstawie modeli GARCH, Rynek Terminowy, 25, 121-128.
• Fiszeder, P. (2005), Forecasting the Volatility of the Polish Stock Index - WIG20, Forecasting Financial Markets. Theory and Applications, Wydawnictwo Uniwersytetu Łódzkiego, Łódź.
• Fiszeder, P., Romański, J., (2002), Looking for the Pattern of GARCH Type Models in Polish Stock Returns. Comparison with Indices of the EU and the East European Stock Markets, East European Transition and EU Enlargement, A Quantitative Approach, Physica-Verlag, A Springer-Verlag Company, Heidelberg.
• Osiewalski, J., Pajor, A., Pipień, M., (2004), Bayesowskie modelowanie i prognozowanie indeksu WIG z wykorzystaniem procesów GARCH i SV, XX Seminarium Ekonometryczne im. Profesora Zbigniewa Pawłowskiego, AE w Krakowie, Kraków.
• Piontek, K. (2003), Weryfikacja wybranych technik prognozowania zmienności - Analiza szeregów czasowych, Inwestycje finansowe i ubezpieczenia - tendencje światowe a polski rynek, Prace naukowe AE we Wrocławiu, nr 991.
• Pipień, M. (2006), Wnioskowanie bayesowskie w ekonometrii finansowej, Wydawnictwo Akademii Ekonomicznej w Krakowie, Kraków.
• Poon, S-H., Granger, C. (2003), Forecasting Volatility in Financial Markets: A Review, Journal of Economic Literature, 41, 478-539.
• West, K. D., Cho, D. (1995), The Predictive Ability of Several Models of Exchange Rate Volatility, Journal of Econometrics, 69, 367-391.