This paper investigates whether structural breaks and long memory are relevant features in modeling and forecasting the conditional volatility of oil spot and futures prices using three GARCH-type models, i.e., linear GARCH, GARCH with structural breaks and FIGARCH. By relying on a modified...

Forecasts of crude oil prices' volatility are important inputs to many decision making processes in application areas such as macroeconomic policy making, risk management, options pricing, and portfolio management. Despite the fact that a large number of forecasting models have been designed to...

This article examines the volatility forecasting abilities of three approaches: GARCH-type model that uses carbon futures prices, an implied volatility from carbon options prices, and the k-nearest neighbor model. Based on the results, we document that GARCH-type models perform better than an...

Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of highfrequency financial market data modeling realized...

In this paper we are interested in the term structure of futures contracts on oil. The objective is to specify a relatively parsimonious model which explains data well and performs well in a real time out of sample forecasting. The dynamic Nelson-Siegel model is normally used to analyze and...

This paper analyses the application of several volatility models to forecast daily Value-at-Risk (VaR) both for single assets and portfolios. We calculate the VaR number for 4 Greek stocks, 2 portfolios based on these securities and for the Athens Stock Exchange General Index. We model VaR for...

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine...

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and,...