Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Har Modeling for Realized Volatility Forecasting

Modelling Realized Covariances and Returns∗

This paper proposes new dynamic component models of returns and realized covariance (RCOV) matrices based on time-varying Wishart distributions. Bayesian estimation and model comparison is conducted with a range of multivariate GARCH models and existing RCOV models from the literature. The main method of model comparison consists of a term-structure of density forecasts of returns for multiple ...

Essays on Financial Risk Modeling and Forecasting

Essays on Financial Risk Modeling and Forecasting

Analysis of High Frequency Financial Data: Models, Methods and Software. Part II: Modeling and Forecasting Realized Variance Measures

A key problem in financial econometrics is the modeling, estimation and forecasting of conditional return volatility and correlation. Having accurate forecasting models for conditional volatility and correlation is important for accurate derivatives pricing, risk management and asset allocation decisions. It is well known that conditional volatility and correlation are highly predictable. An in...

Forecasting realized volatility: a review

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...