We give the theoretical basis of a possible explanation for two stylized facts observed in long
log-return series: the long-range dependence (LRD) in volatility and the integrated GARCH …

In his seminal 1982 paper, Robert F. Engle described a time series model with a time-
varying volatility. Engle showed that this model, which he called ARCH (autoregressive …

There exists a wide literature on parametrically or semi-parametrically modelling strongly
dependent time series using a long-memory parameter d, including more recent work on …

This chapter considers the multiple change-point problem for time series, including strongly
dependent processes, with an unknown number of change-points. We propose an adaptive …

The question of the measurement of strategic long-term financial risks is of considerable
importance. Existing modelling instruments allow for a good measurement of market risks of …

We consider the estimation of a random level shift model for which the series of interest is
the sum of a short-memory process and a jump or level shift component. For the latter …

Constructed from high-frequency data, realized volatility (RV) provides an accurate estimate
of the unobserved volatility of financial markets. This paper uses a Bayesian approach to …

LSTM (Long Short-Term Memory) is a neural network model that can effectively predict time
series. This paper studies the problem of LSTM multi-step time series prediction. By studying …

Recent research argues that if the cause of confusion between long memory and regime
switching were properly controlled for, they could be effectively distinguished. Motivated by …

With the recent availability of high-frequency financial data the long-range dependence of
volatility regained researchers' interest and has led to the consideration of long-memory …