In this paper we present an evaluation framework for predictions of binary events in probabilistic electricity price forecasting. It employs the MSE-equivalent QPS together with the DM test and allows for further insights about deficiencies of the considered models. Additionally, techniques from...

In this paper we present an evaluation framework for predictions of binary events in probabilistic electricity price forecasting. It employs the MSE-equivalent QPS together with the DM test and allows for further insights about deficiencies of the considered models. Additionally, techniques from...

Growth at Risk (GaR) methodology developed by Adrian et al. (2019) has been of special interest by policymakers since it provides a measure of the relationship among macrofinancial variables. GaR requires estimating a set of predictive quantile regressions (QR) where future economic activity...

Growth at Risk (GaR) methodology developed by Adrian et al. (2019) has been of special interest by policymakers since it provides a measure of the relationship among macrofinancial variables. GaR requires estimating a set of predictive quantile regressions (QR) where future economic activity...

The authors evaluate the out-of-sample forecasting performance of six competing models at horizons of up to three quarters ahead in a pseudo-real time setup. All the models use information in monthly indicators released ahead of quarterly GDP. The authors estimate two models – averaged vector...

One of the most important factors to control for the achievements of investment portfolio returns is risk. If we only think that a 100% positive return is needed to recover a portfolio loss of 50%, we can understand why. With the advent of the exponential growth of technology usage in markets,...

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...