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 once volatility is separated into its continuous and discontinuous component using estimators which are not...

Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk management; they have become one of the major topics in financial econometrics and they are principally and continuously used in the pricing of financial assets and the Value at Risk,...

The volatility accuracy of several volatility forecast models is examined for the case of daily spot returns for the Mexican peso - US Dollar exchange rate. The models applied are univariate GARCH, a multi-variate GARCH (the BEKK model), option implied volatilities, and a composite forecast...

We propose using Realized GARCH-type models to estimate the daily price volatility in the EPEX power markets. The model specifications extract the volatility-related information from realized measures, which improves the in-sample fit of the data. More importantly, evidence on the out-of-sample...

This paper investigates the most appropriate model for generating scenarios for daily foreign exchange rates for a long history of a large number of daily exchange rates and finds: returns are not normal; a mean reversion model is rarely appropriate; sampling from historical returns (natural log...

Volatility forecasting is an important process required to measure variability in equity prices, risk management, and several other financial activities. Generalized autoregressive conditional heteroscedastic methods <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$(\textit{GARCH})$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mo stretchy="false">(</mo> <mi mathvariant="italic">GARCH</mi> <mo stretchy="false">)</mo> </mrow> </math> </EquationSource> </InlineEquation> have been used to forecast volatility...</equationsource></equationsource></inlineequation>

This study explores the effect of investor sentiment on the volatility forecasting power of option-implied information. We find that the risk-neutral skewness has the explanatory power regarding future volatility only during high sentiment periods. Furthermore, the implied volatility has varying...

In this paper, we study the role of the volatility risk premium for the forecasting performance of implied volatility. We introduce a non-parametric and parsimonious approach to adjust the model-free implied volatility for the volatility risk premium and implement this methodology using more...

The main goal of this paper is to investigate whether the long memory behavior observed in many volatility energy futures markets series is a spurious behavior or not. For this purpose, we employ a wide variety of advanced volatility models that allow for long memory and/or structural changes:...