Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

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 are accompanied by sudden shocks and volatility in the prices. Therefore, they must be controlled and forecasted by using appropriate tools. This paper adopts the Generalized Auto Regressive Condition...

Volatility Forecasting with High Frequency Data

The daily volatility is typically unobserved but can be estimated using high frequent tick-by-tick data. In this paper, we study the problem of forecasting the unobserved volatility using past values of measured volatility. Specifically, we use daily estimates of volatility based on high frequency data, called realized variance, and construct the optimal linear forecast of future volatility. Ut...

Combining high frequency data with non-linear models for forecasting energy market volatility

The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets’ price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two wa...

Forecasting Volatility using High Frequency Data

Machine Learning for Multi-step Ahead Forecasting of Volatility Proxies

In finance, volatility is defined as a measure of variation of a trading price series over time. As volatility is a latent variable, several measures, named proxies, have been proposed in the literature to represent such quantity. The purpose of our work is twofold. On one hand, we aim to perform a statistical assessment of the relationships among the most used proxies in the volatility literat...