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 …

Most asset prices are subject to significant volatility. Arrival of new information is viewed as the main source of … volatility. As new information is continually released, financial asset prices exhibit volatility persistence, which affects … extracting information about the sources of volatility persistence in the presence of leverage effect …

presence and timing of an outlier. Next, a second test determines the type of additive outlier (volatility or level). The tests …

behaviour of the volatility process. It is assumed that both the magnitude and the timing of the breaks are unknown. We develop …

of volatility in finance for portfolio allocation, derivative pricing and risk management. The method has a two … average realized volatility processes can achieve a convergence rate close to OP(n−4/9) , which is better than the convergence … based on average realized volatility processes indeed performs better than that based on the price processes. Empirically …