Forecasting oil prices: High-frequency financial data are indeed useful

High Frequency Jump Characteristics of Financial Asset Prices

There is evidence that the conventional, continuous underlying function model of financial asset pricing can be improved by adding a discontinuous jump component. Barndorff-Nielsen and Shephard (2004) propose a method of detecting these jumps that compares different estimators of quadratic variation. This paper applies that detection method to the common stock of Altria Group, Inc. and attempts...

Compumetric Forecasting of Crude Oil Prices

This paper contains short term monthly forecasts of crude oil prices using compumetric methods. Compumetric forecasting methods are ones that use computers to identify the underlying model that produces the forecast. Typically, forecasting models are designed or specified by humans rather than machines. Compumetric methods are applied to determine whether models they provide produce reliable fo...

Machine Learning Models for Housing Prices Forecasting using Registration Data

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

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...

Forecasting Volatility using High Frequency Data