Two relevant forecasting problems for practitioners in finance: equity risk premium and non-performing loans
- Autores: Julián David Cortés Sánchez
- Directores de la Tesis: Pilar Soriano Felipe (dir. tes.)
- Lectura: En la Universitat de València ( España ) en 2021
- Idioma: español
- Tribunal Calificador de la Tesis: Francisco José Climent Diranzo (presid.), Francisco Jareño Cebrián (secret.), Helena Chuliá (voc.)
- Programa de doctorado: Programa de Doctorado en Finanzas y Economía Cuantitativas/Quantitative Finance and Economics por la Universidad Complutense de Madrid; la Universidad de Castilla-La Mancha; la Universidad del País Vasco/Euskal Herriko Unibertsitatea y la Universitat de València (Estudi General)
- Materias:
- Enlaces
- Tesis en acceso abierto en: webges.uv.es (pdf)
- Resumen
The thesis aims to substantiate whether macroeconomic factors indicators are relevant to predict both in-sample and out-of-sample assets' future performance focusing on two well-studied themes in financial economics and banking: First, the ability to predict the equity risk premium, and second, the macroeconomic determinants of non-performing loans (NPL) rates. The dissertation is divided in three chapters. Chapter 1, entitled "Forecasting the equity risk premium in the European Monetary Union", investigates the capacity of multiple economic and technical variables to predict the Euro area equity risk premium. The chapter examines the performance of several variables that could be good predictors of the equity risk premium in the European Monetary Union for a period that spans from 2000 to 2020. Chapter 2, entitled "Forecasting the EMU equity risk premium with regression trees", expands on the previous chapter and investigates whether popular machine learning algorithms, such as classification and regression trees (CART), can help to improve equity risk premium forecasts. Finally, chapter 3, entitled "Macro determinants of non-performing loans: A comparative analysis between consumer and mortgage loans", examines the influence of several macroeconomic factors on delinquency rates using dynamic panel data techniques.
