augmenting a large class of volatility models with implied volatility and Google Trends data improves the quality of the … during periods of high volatility, when parameters estimates became very unstable. Moreover, several models augmented with … markets, T-GARCH models with implied volatility and student's t errors are better choices if robust market risk measures are …

. Still, little is known about how far ahead one can forecast volatility. First, in this paper we introduce the notions of the … and forward forecast accuracy curves. Then, by employing a few popular time-series volatility models, we perform a …Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities …

Forecasting Realized Volatility (RV) is of paramount importance for both academics andpractitioners. During recent … relative to macroeconomic predictors. We contendthat these sets of predictors impact volatility at different frequencies. We …

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new …

assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an …

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

The internal models amendment to the Basel Accord allows banks to use internal models to forecast Value-at-Risk (VaR … penalised through higher capital charges. This paper investigates the performance of five popular volatility models that can be … used to forecast VaR thresholds under a variety of distributional assumptions. The results suggest that, within the current …

-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast is based on the median of the point … VaR forecasts of a set of conditional volatility models. This risk management strategy is GFC-robust in the sense that …

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in … of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much …