I provide evidence on the existence of unspanned macro risk. I investigate the usefulness of unspanned macro information for forecasting bond risk premia in a macro-finance term structure model from the perspective of a bond investor. I account for model uncertainty by combining forecasts with...

A consensus has recently emerged that a number of variables in addition to the level, slope, and curvature of the term structure can help predict interest rates and excess bond returns. We demonstrate that the statistical tests that have been used to support this conclusion are subject to very...

Although the empirical evidence about the leading indicator property of the term spread (LIPTS) is powerful, this property lacks a rigorous theoretical foundation. This paper investigates whether dynamic equilibrium asset pricing models are able to provide a theoretical underpinning for the...

We consider whether government bonds, through the term structure, or corporate bonds, through the default yield, provide predictive power for output, consumption and investment growth. Such predictive power will allow policy-makers to use the information as a leading indicator for macroeconomic...

This paper applies computational intelligence methods to exchange rate forecasting. In particular, it employs neural network methodology in order to predict developments of the Euro exchange rate versus the U.S. Dollar and the Japanese Yen. Following a study of our series using traditional as...

This paper considers inflation forecasting for a vast panel of countries. We combine the information from common factors driving global inflation as well as country-specific inflation in order to build a set of different models. We also rely on new advances in the Machine Learning literature. We...

We propose a generic workflow for the use of machine learning models to inform decision making and to communicate modelling results with stakeholders. It involves three steps: (1) a comparative model evaluation, (2) a feature importance analysis and (3) statistical inference based on Shapley...

Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures. Currently, there are a plethora of methodologies and approaches for practitioners to choose from. However, there lacks a comprehensive comparison of...

Oil and Gas industry is one of the renowned industries where prediction and forecasting of necessary oil statistics is important from both the productive and economic perspective. Artificial lift methods are being used to increase the production rate. Even if the natural pressure is appropriate...

Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and...