Modeling and forecasting the oil volatility index

Forecasting Crude Oil Price Volatility

We use high-frequency intra-day realized volatility to evaluate the relative forecasting performance of several models for the volatility of crude oil daily spot returns. Our objective is to evaluate the predictive ability of time-invariant and Markov switching GARCH models over different horizons. Using Carasco, Hu and Ploberger (2014) test for regime switching in the mean and variance of the ...

modeling and forecasting the volatility of tehran exchange dividend price index (tedpix)

modeling and forecasting the volatility of tehran exchange dividend price index (tedpix) the present research, analyses the forecasting performance of a variety of conditional and non-conditional models of tedpix volatility at the daily frequencies performance criterion namely the root mean square error (rmse). under rmse, results show ma250 and cgarch models had better performance between non ...

Forecasting the volatility of crude oil futures using intraday data

We use the information in intraday data to forecast the volatility of crude oil at a horizon of 1 to 66 days using a variety of models relying on the decomposition of realized variance in its positive or negative (semivariances) part and its continuous or discontinuous part (jumps). We show the importance of these decompositions in predictive regressions using a number of specifications. Nevert...

Har Modeling for Realized Volatility Forecasting

Forecasting Crude Oil prices Volatility and Value at Risk: Single and Switching Regime GARCH Models

Forecasting crude oil price volatility is an important issues in risk management. The historical course of oil price volatility indicates the existence of a cluster pattern. Therefore, GARCH models are used to model and more accurately predict oil price fluctuations. The purpose of this study is to identify the best GARCH model with the best performance in different time horizons. To achieve th...