A Smooth Shadow-Rate Dynamic Nelson-Siegel Model for Yields at the Zero Lower Bound
In: Tinbergen Institute Discussion Paper 2022-011/III
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In: Tinbergen Institute Discussion Paper 2022-011/III
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In: Overes , B H L & van der Wel , M 2022 , ' Modelling Sovereign Credit Ratings : Evaluating the Accuracy and Driving Factors using Machine Learning Techniques ' , Computational Economics . https://doi.org/10.1007/s10614-022-10245-7
Sovereign credit ratings summarize the creditworthiness of countries. These ratings have a large influence on the economy and the yields at which governments can issue new debt. This paper investigates the use of a multilayer perceptron (MLP), classification and regression trees (CART), support vector machines (SVM), Naïve Bayes (NB), and an ordered logit (OL) model for the prediction of sovereign credit ratings. We show that MLP is best suited for predicting sovereign credit ratings, with a random cross-validated accuracy of 68%, followed by CART (59%), SVM (41%), NB (38%), and OL (33%). Investigation of the determining factors shows that there is some heterogeneity in the important variables across the models. However, the two models with the highest out-of-sample predictive accuracy, MLP and CART, show a lot of similarities in the influential variables, with regulatory quality, and GDP per capita as common important variables. Consistent with economic theory, a higher regulatory quality and/or GDP per capita are associated with a higher credit rating.
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In: International journal of forecasting, Band 29, Heft 4, S. 676-694
ISSN: 0169-2070
In: International journal of forecasting, Band 34, Heft 2, S. 288-311
ISSN: 0169-2070
In: CESifo Working Paper Series No. 5030
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In: Tinbergen Institute Discussion Paper 2021-109/III
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Central banks resorted to asset purchase programs to replace conventional policy measures, which became ineffective after interest rates approached the zero lower bound. We investigate their effects on financial markets and focus on heterogeneous transmission using a Bayesian structural vector autoregression analysis. Since financial markets react directly to policy announcements, we base our identification scheme on market surprises at the announcement time. We find evidence of a stimulating effect on the economy, declining government bond yields, increasing stock prices, increasing value-growth spread and a reduction in stress in corporate and sovereign debt markets after an asset purchase shock. We disentangle the effect among industry sectors and EMU countries and find that the effect is heterogeneous, with financial stocks and the economy of Southern European countries being the most positively affected.
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In: The economic journal: the journal of the Royal Economic Society, Band 126, Heft 592, S. 618-653
ISSN: 1468-0297
In: Economic Journal, 126, 2016
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In: NBER Working Paper No. w19814
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Working paper
In: Journal of Finance, Volume 79, Issue 3, June 2024, Pages 2339-2390.
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