0000000000863050
AUTHOR
Ignacio Olmeda
An Application of Hybrid Models in Credit Scoring
The predictive capability of parametric and non-parametric models in solving problems related to financial classification has been widely proved in empirical research carried out in the financial field, particulary in problems like bond rating, bankruptcy prediction and credit scoring. However, recently, it has been shown that a combination of different models generally reduces the prediction error, so that the best alternative to consider may not be a specific model but a combination of them. In this paper, we study hybrid systems based on the aggregation of individual (parametric and nonparametric) models. Our hybrids are built by using both parametric and non parametric models as the sys…
Risk forecasting models and optimal portfolio selection
This study analyses, from an investor's perspective, the performance of several risk forecasting models in obtaining optimal portfolios. The plausibility of the homoscedastic hypothesis implied in the classical Markowitz model is dicussed and more general models which take into account assymetry and time varying risk are analysed. Specifically, it studies whether ARCH-type based models obtain portfolios whose risk-adjusted returns exceed those of the classical Markowitz model. The same analysis is performed with models based on the Lower Partial Moment (LPM) which take into account the assymetry in the distribution of returns. The results suggest that none of the models achieve a clearly su…
Self-organizing maps could improve the classification of Spanish mutual funds
In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from…
Modelos Paramétricos y no Paramétricos en Problemas deCredit Scoring
RESUMENDada la importancia creciente que esta cobrandose la actividad crediticia en la gestion diaria de los bancos, comienza a ser imprescindible la utilizacion de modelos de clasificacion automaticos que faciliten la concesion o no del credito solicitado con alto grado de exactitud, de manera que permita reducir la morosidad.En el trabajo que presentamos se realiza un exhaustivo estudio de la capacidad predictiva de dos modelos parametricos (Analisis Discriminante y Logit) y cinco no parametricos (Arboles de regresion, Redes Neuronales Artificiales, Algoritmo C4.5, Splines de Regresion Adaptativa Multivariante y Regresion Localmente Ponderada) en un problema de concesion de tarjetas de cr…
Forecasting Exchange Rates Volatilities Using Artificial Neural Networks
This paper employs Artificial Neural Networks to forecast volatilities of the exchange rates of six currencies against the Spanish peseta. First, we propose to use ANN as an alternative to parametric volatility models, then, we employ them as an aggregation procedure to build hybrid models. Though we do not find a systematic superiority of ANN, our results suggest that they are an interesting alternative to classical parametric volatility models.