The benefits of using flight-to-safety (FTS) in volatility forecasting are assessed within a multivariate GARCH … volatility. Moreover, when comparing with a benchmark and controlling for data snooping, we find that the proposed model yields …

Forecasts of stock market volatility is an important input for market participants in measuring and managing investment … Machine Learning methods, and specifically Artificial Neural Network (ANN) models to forecast volatility. The ANN models are …

strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state …-series volatility models, in this paper we comprehensively examine how volatility forecastability varies across bull and bear states of … the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state …

This paper investigates the predictive ability of international volatility risk for the daily aggregate Chinese stock … market returns. We employ the innovations in implied volatility indices of seven major international markets as our … international volatility risk proxies. We find that international volatility risks are negatively associated with contemporaneous …

to predict future S&P realized volatility. We evaluate the aggregate volatility predictions of regularization methods … main drivers of aggregate volatility are several financial and macroeconomic uncertainty proxies …

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … the stochastic volatility model with asymmetry and long memory, using high frequency data for the underlying asset …

forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that …, 60, and 300 seconds), forecast horizons (1, 5, 22, and 66 days) and the use of standard and robust-to-noise volatility … real-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility …

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities …. Still, little is known about the accuracy of volatility forecasts and the horizon of volatility predictability. This paper … volatilities and propose to describe the term structure of volatility predictability by the spot and forward forecast accuracy …

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