value robust volatility estimator with respect to the standard robust volatility estimator as proposed in the paper by … Muneer & Maheswaran (2018b). We show that the robust volatility ratio is unbiased both in the population as well as in finite … samples. We empirically test the robust volatility ratio on 9 global stock indices from America, Asia Pacific and EMEA markets …

This paper introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility … feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi …

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …

volatility and realized R-Squared. Because the residual process is latent in the high frequency regression, the estimation of … idiosyncratic volatility is notoriously difficult and complex, especially in the presence of jumps, microstructure noise and … features of the idiosyncratic volatility estimate and the realized R-Squared estimate …

through a reverse inference approach, based on a distribution-free testing theory [Coudin and Dufour (2009, The Econometrics …