presenting a model for multiple-step-ahead-forecasting of volatility and conditional value at risk in fossil energy markets

Fossil energy markets have always been known as strategic and important markets. they have a significant impact on the macro economy and financial markets of the world. the nature of these markets is accompanied by sudden shocks and volatility in the prices. therefore, they must be controlled and forecasted using appropriate tools. this paper adopts the generalized auto regressive conditional heteroskedasticity (garch)-type models, exponential smoothing (es)-type models, and classic model in order to multiple-step-ahead forecast volatility, value at risk, and conditional value at risk of brent oil and natural gas in two different estimation window lengths, respectively. to evaluate the accuracy of the aforementioned models, eight different loss functions are utilized. there are a lot of financial terms in this the noted part. so, it’s comprehensible for financial person and etc. therefore, the hwes model is proposed to multiple-step-ahead forecast functions as a verb.