We study the impact of private information on volatility in financial markets. We develop a comprehensive framework to … private information on prices and the effects of public information on volatility. Using a high-frequency 30-year U … statistically and economically significant explanatory variables for volatility. Private information is more important than public …

univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and … result, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR … estimates improve a t the short horizon, in particular when compared to estimates based on equal weights or to the VaR estimates …

We study the impact of private information on volatility in financial markets. We develop a comprehensive framework to … private information on prices and the effects of public information on volatility. Using a high-frequency 30-year U … statistically and economically significant explanatory variables for volatility. Private information is more important than public …

We propose a new score-driven model to capture the time-varying volatility and tail behavior of realized kernels. We … and forecasts of volatility risk quantiles. …

We present a new model to decompose total daily return volatility into a filtered (high-frequency based) open …-to-close volatility and a time-varying scaling factor. We use score-driven dynamics based on fat-tailed distributions to limit the impact …-to-close volatility changes substantially through time, especially for financial stocks. …