Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. I...

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 ...

Behavioral Finance Models and Behavioral Biases in Stock Price Forecasting

Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was ...

Forecasting in Hierarchical Models

Crude Oil Price Forecasting with Anfis

Crude oil pricing is commonly expressed as a formula referenced to Brent or WTI crude oil. The final price of these two qualities and the spread between WTI and Brent can drive the decision when the purchase of a crude oil cargo is evaluated. A crude oil price-forecasting model is presented. It is based on past data, inventory level and volatility index and it is derived with a neuro fuzzy infe...