Volatility is a matter of concern for time series modeling. It provides valuable insights into the
fluctuation and stability of concerning variables over time. Volatility patterns in historical data …

This study is to discuss the techniques that will be employed by the researcher's when
conducting the study on modelling and predicting financial Time Series data. The …

Abstract Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity
(FIGARCH) models have enjoyed considerable popularity over the past decade because of …

Volatility is an important characteristic of time series. If the volatility of a series at any time
epoch is affected by its distant counterpart, then it is known as long memory in volatility. The …

In the present study exogenous variable is incorporated in the long memory model to give
better forecast of time series. Autoregressive Fractionally Integrated Moving Average …

Publisher Summary This chapter reviews various procedures for forecasting implied
volatility. Fractionally integrated processes, which are a subclass of long memory processes …

In this paper, we introduce the class of seasonal ARFIMA models with bilinear GARCH (BL-
GARCH) type innovations that are capable of capturing simultaneously four key properties of …

This paper reviews the theory and applications related to fractionally integrated generalized
autoregressive conditional heteroscedastic (FIGARCH) models, mainly for describing the …

This study is an attempt to review the theory and applications of autoregressive fractionally
integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive …

The introduction of the Autoregressive Conditional Heteroskedasticity (ARCH) model by
Engle (1982) and the Generalized ARCH (GARCH) model by Bollerslev (1986) sparked a …