We consider the problem of forecasting realized variance measures. These measures are highly persistent, but also noisy estimates of the underlying integrated variance. Recently, Bollerslev, Patton and Quaedvlieg (2016, Journal of Econometrics, 192, 1-18) exploited this fact to extend the...

We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset which includes past values of the RV and additional...

Forecasting Realized Volatility (RV) is of paramount importance for both academics andpractitioners. During recent decades, academic literature has made substantial progressboth in terms of methods and predictors under consideration. Despite the popularity oftechnical indicators, there has been...

An immediate consequence of the Efficient Market Hypothesis (EMH) is the absence of auto-correlation of the return series of the financial prices and the exclusion of excess profitability made by any (active) trading strategy. However, the precondition for the validity of EMH, which assumes that...

This paper tests whether it is possible to improve point, quantile and density forecasts of realized volatility by conditioning on macroeconomic and financial variables. We employ quantile autoregressive models augmented with a plethora of macroeconomic and financial variables. Complete subset...