Dopasowanie mideli garch w próbie i jakości otrzymywanych prognoz zmienności

• Autorzy: Małgorzata Doman

Warianty tytułu

Garch Models: Goodness of In-Sample-Fit and Quality of Volatility Forecasts

• Języki publikacji: PL

Abstrakty

W artykule opisany jest eksperyment związany z poszukiwaniem odpowiedzi na pytanie czy nakład pracy włożony w dobór najlepiej dopasowanego modelu zwraca się w postaci wyraźnie lepszej jakości uzyskiwanych prognoz zmienności.(fragment tekstu)

EN

GARCH models are useful tool for modeling the volatility of financial instruments. The aim of this paper is to verify if in the case of models from this family the improvement of the goodness of in-sample-fit results in better volatility forecasts. Time series under scrutiny consists of daily returns of USD/PLN exchange rate. Our results show that models which are better in the sense of information criteria may produce worse forecasts. The difference between quality of the forecasts from GARCH(1,1) and those from more sophisticated GARCH models is rather insignificant. (original abstract)

Słowa kluczowe

PL

Praca; Instrumenty finansowe; Pomiar ryzyka; Model GARCH

EN

Labour; Financial instruments; Risk measures; GARCH model

• Czasopismo: Zeszyty Naukowe / Akademia Ekonomiczna w Poznaniu
• Rocznik: 2005
• Numer: nr 55
• Strony: 43--65

Twórcy

autor

Małgorzata Doman

Bibliografia

• Andersen T.G., Bollerslev T., Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts, International Economic Review 1998, 39, 885-905.
• Baillie R., Bollerslev T., Mikkelsen H., Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics 1996, 74, 3-30.
• Bollerslev T., Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics 1986, 31, 307-327.
• Bollerslev T., A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return, Review of Economics and Statistics 1987, 69, 542-547.
• Bollerslev T., Mikkelsen H., Modeling and Pricing Long-memory in Stock Market Volatility, Journal of Econometrics 1996, 73, 151-184 .
• Chung C.-F., Estimating the Fractionally Integrated GARCH Model, National Taiwan Uniwersity Working Paper, 1999.
• Ding Z., Granger C.W.J., Engle R., A Long Memory Property of Stock Market Returns and a New Model, Journal of Empirical Finance 1993, 1, 83-106 .
• Engle R.F., Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica 1982, 50, 987-1007 .
• Engle R.F., Bollerslev T., Modeling the Persistence of Conditional Variances, Econometric Reviews 1986, 5, 1-50 .
• Engle R.F., Lilien D.M., Robins R.P., Estimating Time Varying Risk Premia in the Term Structure: the ARCH-M Model, Econometrica 1987, 55, 525-542.
• Fernández C, Osiewalski J., Steel M.F.J., Modelling and Inference with v-spherical Distributions, Journal of the American Statistical Association 1995, 90, 1331-1340.
• Fernández C, Steel M.F.J., On Bayesian Modelling of Fat Tails and Skewness, Journal of the American Statistical Association 1998, 93, 359-371 .
• Glosten L.R., Jagannathan R., Runkle D.E., On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance 1993, 48, 1779-1801 .
• Hoi E., Koopman S.J., Stock Index Volatility Forecaststing with High Frequency Data, Tinbergen Institute Discussion Paper 2002, 068/4.
• Laurent S., Peters J.-P., G@RCH2.2: An Ox Package for Estimating and Forecasting Various ARCH Models, Journal of Economic Surveys 2002, 16, 447-484.
• Ling S., McAleer M., Stationarity and the Existence of Moments of a Family of GARCH Processes, Journal of Econometrics 2002, 106, 109-117.
• Nelson D.B., Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica 1991, 59, 347-370.
• Osiewalski J., Pipień M., GARCH-in-Mean Through Skewed t Conditional Distributions: Bayesian Inference for Exchange Rates, Macromodels' 1999, Łódź 2000 355-369.
• Welfe A., Ekonometria, PWE, Warszawa 1998.