A Non Parametric Study of the Volatility of the Economy as a Country Risk Predictor

TL;DR

This study uses neural networks to analyze non-linear relationships between economic indicators, shocks, and country risk in Venezuela, demonstrating the effectiveness of non-parametric methods in economic risk prediction.

Contribution

It introduces a neural network-based non-parametric approach to model country risk, capturing non-linear relationships in economic indicators and shocks.

Findings

Neural networks effectively model non-linear relationships in country risk.

Non-parametric methods reveal complex dynamics in economic indicators.

Network performance validated using excess predictability measure.

Abstract

This paper intends to explain Venezuela's country spread behavior through the Neural Networks analysis of a monthly economic activity general index of economic indicators constructed by the Central Bank of Venezuela, a measure of the shocks affecting country risk of emerging markets and the U.S. short term interest rate. The use of non parametric methods allowed the finding of non linear relationship between these inputs and the country risk. The networks performance was evaluated using the method of excess predictability.