Tingkat inflasi nasional merupakan salah satu indikator yang penting dalam menganalisis pertumubuhan perekonomian suatu negara. tidak dikelola dengan baik dapat menyebabkan negara mengalami kemunduran. Pada data tingkat digunakan model ARIMA (Autoregressive Integrated Moving Average) dan terdeteksi terdapat adanya heteroskedastisitas, sehingga time series ARCH-GARCH Conditional Heteroskedastici...

The study is pioneer to investigate the volatility of CO2 emissions in Uzbekistan. To this end, ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive models are used spanning period 1925-2021 for annual data emissions. results indicate that model more adequate assessment. Furthermore, it found Uzbekistan very high. policymakers have consider high environment...

The autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models take the dependency of the conditional second moments. The idea behind ARCH/GARCH model is quite intuitive. For ARCH models, past squared innovations describes the present squared volatility. For GARCH models, both squared innovations and the past squared volatil...

Asymmetry and fat-tail are both stylized facts of financial return data. Many asymmetric and fat-tailed distributions have been used to model the innovation in autoregressive conditional heteroskedasticity (ARCH) models. This article introduces two more distributions from systems of frequency curves into the ARCH context: Pearson’s Type IV and Johnson’s SU. Both distributions have two shape par...

A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an e...

Finally, it should be noted that White's approach to standard errors which are robust to heteroskedasticity succeeds because it does not assume that the analyst knows the nature of the heteroskedasticity. Such ignorance is clearly the most common situation. But there are times, such as with time-series{cross-section data, that the analyst may have some better insight about the nature of . Such ...

The purpose of this study is to investigate the factors that affect real exchange rate volatility for Pakistan through the co-integration and error correction model over a 30-year time period, i.e. between 1980 and 2010. The study employed the autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) and Vector Error Correction model...