A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the
last 15 years, with varying degrees of success. This review article aims to explain the …

This article reviews the exciting and rapidly expanding literature on realized volatility. After
presenting a general univariate framework for estimating realized volatilities, a simple …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

Macroeconomic practitioners frequently work with multivariate time series models such as
VARs, factor augmented VARs as well as time-varying parameter versions of these models …

This book contains a up-to-date overview of nonlinear time series models and their
application to modelling economic relationships. It considers nonlinear models in stationary …

Financial markets respond to information virtually instantaneously. Each new piece of
information influences the prices of assets and their correlations with each other, and as the …

This paper proposes a methodology for dynamic modelling and forecasting of realized
covariance matrices based on fractionally integrated processes. The approach allows for …

This paper introduces a new class of multivariate volatility models that utilizes high‐
frequency data. We discuss the models' dynamics and highlight their differences from …

This study compares the dynamic characteristic of Chinese and American energy prices
from the perspectives of learning expectation, volatility, persistence, and so on. First, the …

Modelling is fundamental to many fields of science and engineering. A model can be
thought of as a representation of possible data one could predict from a system. The …