We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and...

We define risk spillover as the dependence of a given asset variance on the past covariances and variances of other assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an international equity portfolio. According to the risk...

We investigate price duration variance estimators that have long been neglected in the literature. We show i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and ii) how they are affected by a)...

In this paper I examine the properties of four realized correlation estimators and model their jumps. The correlations are between the French, German and Greek equity markets. Using intraday data I first construct four state-of-the-art realized correlation estimators which I then use to testing...

We provide new empirical evidence on volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Leverage and volatility feedback effects of the S&P 500 price and volatility dynamics are examined using recently developed methodologies to...

The approximate long memory Heterogeneous Autoregressive (HAR) model proposed by Corsi is extended in order to account for leverage effects in the realized volatility process and the long memory of the conditional variance of the HAR residuals. The proposed model is estimated using ten years of...

In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several difficulties in modelling and forecasting the conditional covariance matrix in large dimensions arising from the curse of dimensionality. We show that outliers have a...

This paper proposes a double tree structured AR-GARCH model for the analysis of stock index return series, which extends previous approaches to incorporate (i) an arbitrary number of multivariate thresholds in conditional means and volatilities of stock index returns and (ii) a richer...

We establish a framework to study the factor structure in stock variance under a high-frequency and high-dimensional setup. We prove the consistency of conducting principal component analysis on realized variances in estimating the factor structure. Moreover, based on strong empirical evidence,...

This paper introduces a new approach to modelling the conditional variance in a multivariate setting. It is essentially a combination of the popular GARCH model class with a spatial component, inspired by generalized space-time models. The resulting spatial GARCH model takes into account both...