This paper employs the symmetric GARCH and asymmetric GJR-GARCH(1,1) E-GARCH(1,1) models to explain dynamics of PLN/EUR PLN/USD exchange rates in Poland for periods January 2015 July 2022. The result our study shows that USD rate is more susceptible market fluctuations events than EUR rate. Additionally, both rates' volatility persists after a crisis while, with taking longer until subsides. Us...

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...

We propose a multiplicative factor multi frequency component GARCH model which exploits the empirical fact that daily standardized forecast errors of one-component models behave counter-cyclical when averaged at lower frequency. For new model, we derive unconditional variance returns, news impact function and multi-step-ahead volatility forecasts. When applied to S&P 500, significantly outperfo...

Financial time series forecast has been classified as standard problem in forecasting due to its high non-linearity and high volatility in data. Statistical methods such as GARCH, GJR, EGARCH and Artificial Neural Networks (ANNs) based on standard learning algorithms such as backpropagation have been widely used for forecasting time series volatility of various fields. In this paper, we propose...

Modelling and forecasting volatility of a financial time series has been significant area research in recent years, owing to the fact that is regarded as an essential notion many economic applications. Because not directly observable, analysts are especially eager obtain accurate estimation this conditional variance process. As result, number models have developed specifically suited estimate i...