The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen, Bollerslev, Diebold and Labys (2003), and by Andersen, Bollerslev and Meddahi (2004, 2005), who address the issue of pointwise prediction of volatility via ARMA models, based on the use of realized volatility. Our...

Diurnal fluctuations in volatility are a well-documented stylized fact of intraday price data. This warrants an investigation how this periodicity (IP) affects both finite sample as well asymptotic properties several popular realized estimators daily integrated which based on functionals number returns. It turns out that most the considered study exhibit finite-sample bias due to IP, can howeve...

In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic ‘heterogeneous autoregressive’ (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the r...

The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets’ price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two wa...

The paper develops a novel realized stochastic volatility model of asset returns and realized volatility that incorporates general asymmetry and long memory (hereafter the RSV-GALMmodel). The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of...

We compare the forecasts of Quadratic Variation given by the Realized Volatility (RV) and the Two Scales Realized Volatility (TSRV) computed from high frequency data in the presence of market microstructure noise, under several different dynamics for the volatility process and assumptions on the noise. We show that TSRV largely outperforms RV, whether looking at bias, variance, RMSE or out-of-s...