We first propose a reduced-form model in discrete time for S&P 500 volatility showing that
the forecasting performance can be significantly improved by introducing a persistent …

Univariate dependencies in market volatility, both objective and risk neutral, are best
described by long-memory fractionally integrated processes. Meanwhile, the ex post …

This paper proposes a test statistic for the null hypothesis that a given time series is a
stationary long-memory process against the alternative hypothesis that it is affected by …

In this study, we construct an investor sentiment indicator (S s PCA) to predict stock volatility
in the Chinese stock market by applying the scaled principal component analysis (sPCA). As …

This study aims to investigate which types of commodity price information are more useful for
predicting US stock market realized volatility (RV) in a data-rich word. The standard …

This paper examines how COVID-19 pandemic has affected volatility persistence in the G7's
stock markets. Based on daily data we divided the whole sample into two sub-samples …

This study explores the effect of investor sentiment on the volatility forecasting power of
option-implied information. We find that the risk-neutral skewness has the explanatory power …

Based on the characteristics of investors' risk preference, which is considered to change with
the two opposite outcomes (gain/loss), we build a DGARCH-M model by dividing investors' …

Provides a simple exposition of the basic time series material, and insights into underlying
technical aspects and methods of proof Long memory time series are characterized by a …

Tse (1998) proposes a model which combines the fractionally integrated GARCH
formulation of Baillie, Bollerslev and Mikkelsen (1996) with the asymmetric power ARCH …