El objetivo del presente estudio radica en construir algunos modelos estadísticos, econométricosy de inteligencia artificial que permitan realizar predicciones sobre el comportamientode mercado de la acción de SURAMINV (Suramericana de Inversiones S. A.).Se obtuvo evidencia a favor de la...

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and...

In this paper we investigate the efficiency of a support vector machine (SVM)-based forecasting model for the next-day directional change of electricity prices. We first adjust the best autoregressive SVM model and then we enhance it with various related variables. The system is tested on the...

This paper studies the nature of volatility spillovers across countries from the per-spective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international...

This paper studies the nature of volatility spillovers across countries from the perspective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international...

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation...

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy...

This draft is a summary of the paper entitled: Forecasting Fuel Prices with the Chilean Exchange Rate. In that paper we show that the Chilean exchange rate has the ability to predict the returns of oil prices and of three additional oil-related products: gasoline, propane and heating oil. The...

We propose a stochastic model for the maximal production of photovoltaic (PV) power on a daily basis based on data from three transmission system operators in Germany. We apply the sun intensity as a seasonal function and model the deseasonalized data by an autoregressive process with skewed...

The importance of solar energy has been growing in recent years. This raises the need for efficient modelling and forecasting methods. The existing methods are predominantly based on weather predictions or forecast solar radiation, which is not easy to convert into production forecast. Instead...