South Africa is a cornucopia of the platinum group metals particularly platinum and palladium. These metals have many unique physical and chemical characteristics which render them indispensable to technology and industry, the markets and the medical field. In this paper we carry out a holistic investigation on long memory (LM), structural breaks and stylized facts in platinum and palladium ret...

In this paper, we study the dual long memory property of the Korean stock market. For this purpose, the ARFIMA–FIGARCH model is applied to two daily Korean stock price indices (KOSPI and KOSDAQ). Our empirical results indicate that long memory dynamics in the returns and volatility can be adequately estimated by the joint ARFIMA–FIGARCH model. We also found that the assumption of a skewed Stude...

This paper reviews the theory and applications related to fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) models, mainly for describing the observed persistence in the volatility of a time series. The long memory nature of FIGARCHmodels allows to be a better candidate than other conditional heteroscedastic models for modeling volatility in exchange rates...

Son yıllarda rüzgâr enerjisinin yenilenebilir bir enerji kaynağı olarak yaygınlaşması ile birlikte hızının üretimindeki ekonomik etkilerinin değerlendirilmesi de önem kazanmış ve planlamalarında doğru hızı tahmini modellemesine olan ilgi artmıştır. Çalışmada klasik yaklaşımlardan farklı hızlarındaki uzun hafıza özelliği incelenmiştir. Bu amaçla, Türkiye’ Bartın ili Amasra bölgesi hızları için e...

This study estimates the effects of dual long memory property and structural breaks on persistence level six major cryptocurrency markets. We apply Bai Perron break test, Inclán Tiao’s iterated cumulative sum squares (ICSS) algorithm, fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that characteriz...

We present a generalisation of the double long memory ARFIMA-FIGARCH model introducing time-varying memory coefficients both in the mean and in the variance. The model satisfies the empirical evidence of changing memory observed in average temperature series and can provide useful improvements in the forecasting, simulation and pricing issues related to weather derivatives. We provide an applic...

Purpose The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns volatility. Design/methodology/approach competing models are autoregressive moving average (ARMA) model fractional integrated (ARFIMA) for returns. For volatility, exponential generalized conditional heteroscedasticity (EGARCH) with GARCH (FIGARCH) component...

In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we div...

The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather derivative contracts. The pricing of such contracts require the development of appropriate models for the prediction of the underlying weather variables. Within this framework, we present a modification of the double long memory ARFIMA-FIGARCH model introducing time-varying memory coef...

In this paper, we use wavelet analysis to localize in Paris, France, a mean-reverting Ornstein-Uhlenbeck process with seasonality in the level and volatility. Wavelet analysis is an extension of the Fourier transform, which is very well suited to the analysis of non-stationary signals. We use wavelet analysis to identify the seasonality component in the temperature process as well as in the vol...