This work aims to investigate the (inter)relations of information arrival, news sentiment, volatility and jump dynamics of intraday returns. Two parametric GARCHtype jump models which explicitly incorporate both news arrival and news sentiment variables are proposed, among which one assumes news...

We apply a multivariate multiplicative error model (MMEM) and investigate effects in the simultaneous processes of high-frequency return volatilities, trading volume, and trading intensities on the Italien Electronic Interbank Credit Market (e-MID). Analysing five minutes data from the Italian...

This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...

A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...

This note discusses some aspects of the paper by Hu and Tsay (2014), "Principal Volatility Component Analysis". The key issues are considered, and are also related to existing conditional covariance and correlation models. Some caveats are given about multivariate models of time-varying...

The problems related to the application of multivariate GARCH models to a market with a large number of stocks are solved by restricting the form of the conditional covariance matrix. It contains one component describing the market and a second simple component to account for the remaining...

Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to ensure robustness and correct the downward bias of RS measure with an additive term. Moreover scaling factors are provided for different interquantile ranges to ensure unbiasedness....

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk,...

This paper studies the nature of volatility spillovers across countries from the per-spective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international...

We examine the effectiveness of GARCH models and the SV model in foreign exchange rates and the stock indices of the BRICS. The empirical analysis is conducted using the daily log returns. The data cover the period 13/05/1999-22/11/2018. The theoretical results are confirmed by running the...