Volatility forecasting is an important aspect of finance as it dictates many decisions of market
players. A snapshot of state-of-the-art neural network–based financial volatility forecasting …

This chapter surveys the methods available for extracting information from option prices that
can be used in forecasting. We consider option-implied volatilities, skewness, kurtosis, and …

The existing contradictory findings on the contribution of trading volume to volatility
forecasting prompt us to seek new solutions to test the sequential information arrival …

We decompose the squared VIX index, derived from US S&P500 options prices, into the
conditional variance of stock returns and the equity variance premium. We evaluate a …

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In
doing so, I applied three different range-based volatility estimates along with two different …

This paper aims to improve the predictability of aggregate oil market volatility with a
substantially large macroeconomic database, including 127 macro variables. To this end …

Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the
information flow, we claim that cross-market volatility transmission effects are synonymous to …

We inspect how accurate machine learning (ML) is at forecasting realized variance of the
Dow Jones Industrial Average index constituents. We compare several ML algorithms …

Using estimators of the variation of positive and negative returns (realized semivariances)
and high-frequency data for the S&P 500 Index and 105 individual stocks, this paper sheds …

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have
a positive and mostly significant impact on future volatility. This result becomes apparent …