This paper identifies the best models for forecasting the volatility of daily exchange returns of developing countries. An emerging consensus in the recent literature focusing on industrialised counties has noted the superior performance of the FIGARCH model in the case of industrialised countries, a result that is reaffirmed here. However, we show that when dealing with developing countries’ d...

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...

Bu çalışmanın amacı, yapısal kırılmalar altında asimetrik bilginin hisse senedi getiri oynaklığı üzerindeki etkisini ARFIMA-FIGARCH ikili uzun hafıza ve Markov Switching Regresyon modelleriyle ortaya koymaktır. doğrultuda, çalışmada BİST 100 Endeksi’nin 04.01.2010-31.12.2018 dönemine ilişkin günlük dolar cinsinden kapanış fiyatları, alım satım fiyat marjı ile toplam işlem hacmi verileri dikkate...

This paper analyses moment and near-epoch dependence properties for the general class of models in which the conditional variance is a linear function of squared lags of the process. It is shown how the properties of these processes depend independently on the sum and rate of convergence of the lag coefficients, the former controlling the existence of moments, and the latter the memory of the v...

In this work we focus on the effects of aggregation on parameters estimation and Value-at-Risk computation if the data generator follow a FIGARCH model. We present a Montecarlo experiment which shows that the memory structure is affected. In a second simulation study we compare Value-at-Risk estimates obtained by high frequency and aggregated data. We verify that aggregated data have a better p...