Multifractality is ubiquitously observed in complex natural and socioeconomic systems.
Multifractal analysis provides powerful tools to understand the complex nonlinear nature of …

Volatility has been one of the most active and successful areas of research in time series
econometrics and economic forecasting in recent decades. This chapter provides a selective …

This article serves as an introduction and survey for economists to the field of sequential
Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo …

This paper provides an optimal filtering methodology in discretely observed continuous-time
jump-diffusion models. Although the filtering problem has received little attention, it is useful …

Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on
insights from the use of multifractals in the natural sciences and mathematics and provides a …

We use a Markov switching multifractal (MSM) volatility model to forecast crude oil return
volatility. Not only can the model capture stylized facts of multiscaling, long memory, and …

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting
commonality in intraday volatility via pooling stock data together, and by incorporating a …

This paper adopts the Markov-switching multifractal (MSM) model and a battery of
generalized autoregressive conditional heteroscedasticity (GARCH)-type models to model …

We provide a detailed summary of the large and vibrant emerging literature that deals with
the multivariate modeling of conditional volatility of financial time series within the framework …

Volatility has been one of the most active and successful areas of research in time series
econometrics and economic forecasting in recent decades. This chapter provides a selective …