Realized Volatility Versus GARCH and Stochastic Volatility Models. The Evidence from the WIG20 Index and the EUR/PLN Foreign Exchange Market

• Authors: Barbara Będowska-Sójka , Agata Kliber

Title variants

PL

Zmienność zrealizowana wobec modeli GARCH i modeli zmienności stochastycznej na polskim rynku kapitałowym

Abstracts

PL

Celem artykułu jest porównanie oszacowań zmienności uzyskanych z modeli parametrycznych: GARCH i SV z oszacowaniem uzyskanym na podstawie zmienności zrealizowanej szacowanej w oparciu o dane różnej częstotliwości. W badaniu wzięto pod uwagę zwroty z wybranych instrumentów polskiego rynku finansowego: indeks WIG 20 oraz kurs walutowy EUR/PLN. Ujęta w badaniu próba objęła okres kryzysu finansowego, co stanowi istotne uzupełnienie wyników prezentowanych do tej pory w literaturze.

EN

The aim of the article is to compare the estimates of the volatility obtained from the parametric models: the GARCH and the SV with the estimates based upon the Realized Volatility approach, whereas the estimates from the RV are obtained from the data of different frequencies. The data sample consists of the WIG20 index and the EUR/PLN exchange rate and covers the hectic crisis period. Hence, the presented results can be viewed as an extension of the results of the studies presented up to date.

Keywords

EN

realizded volatility; SV; GARCH; volatility forecasting

PL

zmienność zrealizowana; SV; GARCH; prognozowanie zmienności

• Publisher: Główny Urząd Statystyczny
• Journal: Przegląd Statystyczny
• Year: 2010
• Volume: 57
• Issue: 4
• Pages: 105-127

Dates

published

2010

Contributors

author

Barbara Będowska-Sójka

• Poznań University of Economics

author

Agata Kliber

• Poznań University of Economics

References

• Akaike H., [1973], Information theory and an extension of the maximum likelihood principle, [in:] B. Petrov, F. Csaki, (eds.), Proceedings of 2nd International Symposium on Information Theory, Academia Kiado, Budapest, 267-281.
• Andersen T., Bollerslev T., Diebold F., [2003], Parametric and nonparametric volatility measurement, [in:] L.P. Hansen, Y. Ait-Sahalia (eds.), Handbook of Financial Econometrics, Amsterdam, North-Holland.
• Black F., [1976], Studies of Stock Price Volatility Changes, Proceedings of the 1976 Meeting of the American Statistical Association, 177-181.
• Bollerslev T., [1986], Generalized autoregressive conditional heteroscedasticity, „Journal of Econometrics” 31, 307-327.
• Bollerslev T., Chou R.Y., Kroner K.F., [1992], ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence, „Journal of Econometrics” 52, 5-59.
• Broto C., Ruiz E., [2004], Estimation Methods for Stochastic Volatility Models: Survey, „Journal of Economic Surveys” 18, 613-649.
• Clark P.K., [1973], A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices, „Econometrica” 41, 135-155.
• Doman M., Doman R., [2004], Stochastic Volatility Models of Exchange Rates in Central European Countries: effects of Exchange Rate Regimes, „Przegląd Statystyczny” 51 (4), 37-55.
• Doman M., Doman R., [2009], Modelowanie zmienności i ryzyka (Volatility and Risk Modeling), Oficyna Wydawnicza Wolters Kluwer Polska, Kraków.
• Doman R., [2003], Prognozowanie zmienności zrealizowanej indeksu WIG20 za pomocą modelu GARCH (Foreseeing the Realized Volatility of the WIG20 index Using the GARCH model), „Przegl;d Statystyczny” 50 (4), 147-163.
• Engle R.F., [1982], Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, „Econometrica” 50, 987-1007.
• Glosten L.R., Jagannathan R., Runkle D., [1993], On the Relation Between Expected Value and the Volatility of the Nominal Excess Return on Stocks, „Journal of Finance” 48, 1779-1801.
• Harvey A.C., Shephard N., [1996], The Estimation of an Assymeric Stochastic Volatility Models for Asset Reurns, „Journal of Business and Economic Statistics” 14, 429-434.
• Jacquier E., Polson N., Rossi P., [2004], Bayesian analysis of stochastic volatility models with fat-tails and correlated errors, „Journal of Econometrics”, 122, 185-212.
• Kim S., Shephard N., Chib S., [1998], Stochastic volatility: likelihood inference and comparison with ARCH models, „Review of Economic Studies” 65, 361-393.
• Laurent S., Peters J.P., [2002], G@RCH 2.2: An Ox Package for Estimating and Forecasting Various ARCH Models, „Journal of Economic Surveys”, 16, 447-485.
• Mandelbrot B., [1963], The Variation of Certain Speculative Prices, „Journal of Business”, 36, 394-419.
• Mele A., Formari F., [2000], Stochastic Volatility in Financial Markets: Crossing the Bridge to Continuous Time, Kluwer Academic Publishers.
• Mincer J., Zarnowitz V., [1969], The Evaluation of Economic Forecasts and Expectations, National Bureau of Economic Research.
• Ntzoufras I., [2009], Bayesian Modeling using WinBUGS, John Wiley and Sons.
• Pajor A., [2003], Procesy zmienności stochastycznej SV w bayesowskiej analizie finansowych szeregów czasowych (Stochastic Volatility Processes In Bayesian Analysis of Financial Time Series), doctoral dissertation (In Polish), published by Cracow University of Economics, Kraków.
• Osiewalski J., Pajor A., Pipień M., [2006], Bayesian Analysis of Main Bivariate GARCH and SV Models for PLN/USD and PLN/DEM (1966-2001), [in:] Zieliński Z. (ed.), Dynamic Econometric Models 7, ISSN: 12343862.
• Shepard N., [1996], Statistical Aspects of ARCH and Stochastic Volatility, [in:] Time Series Models in Econometrics, Finance and Other Fields, ed. Cox D.R., Hinkley D.V., Barndorff-Nielsen O.E., Champan and Hall, London.
• Speigehalter D., Best N., Carlin B., van der Linde A., [2002], Bayesian measures of model complexity and fit (with discussion), Journal of the Royal Society B 64, 583-639.
• Taylor S., [1986], Modeling Financial Time Series, John Wiley and Sons.
• Yu J., [2005], On Leverage in a Stochastic Volatility Model, „Journal of Econometrics”, 127, 165-178.
• Yu J., Meyer R., [2000], BUGS for a Bayesian analysis of Stochastic Volatility models, „Econometric Journal” 3, 198-215.
• http://www.mysmu.edu/faculty/yujun/research.html

Identifiers

Biblioteka Nauki

1828610