There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as …, we use forecast horizons ranging from 1 day to 6 months. Third, we evaluate the precision of volatility forecast provided … fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important …

We propose the use of a risk measure built on flight-to-safety (FTS) episodes into a volatility forecasting model. We … bond and gold markets. By allowing each FTS day to be an indicator of higher future volatility, we document statistically …. Superior model performance is found over some of the most common volatility forecasting models proposed in the literature that …

This paper elaborates sequential procedures for monitoring the validity of a volatility model. A state space … representation describes dynamics of daily integrated volatility. The observation equation relates the integrated volatility to its … measures such as the realized volatility or bipower variation. On-line control procedures, based on volatility forecasting …

frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information … content of realized volatility measures extracted from high-frequency data. For this purpose, we introduce asymptotically … exact volatility measurement equations in state space form and propose a Bayesian estimation approach. Our highly efficient …

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily … volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator … of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm …

to predict future S&P realized volatility. We evaluate the aggregate volatility predictions of regularization methods … (Ridge, Lasso, and Elastic Net), tree-based methods (Random forest and Gradient boosting), and forecast combination methods … main drivers of aggregate volatility are several financial and macroeconomic uncertainty proxies …

price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the … jump component is persistent when forecasting the oil futures market volatility. Specifically, we propose a strategy that … according to their recent past forecasting performance. The volatility data are based on the intraday prices of West Texas …

of asset-return volatility, in the context of financial risk management using high frequency data. In our evaluation we … use both statistical criteria (i.e., accuracy of directional volatility predictions) and economic criteria (i … contemporaneous return-volatility relationship and leads to new insights related to linkages between economic and statistical methods …

realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the … realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR … alternative, especially during periods of high market volatility …

time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out … empirical findings, based on the S&P 500 stock index, indicate that almost all realized and implied volatility measures can … produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being …