A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression

We propose a coherent framework using support vector regression (SRV) for generating and ranking a set of high quality models for predicting emerging market sovereign credit spreads. Our framework adapts a global optimization algorithm employing an hv-block cross-validation metric, pertinent for models with serially correlated economic variables, to produce robust sets of tuning parameters for SRV kernel functions. In contrast to previous approaches identifying a single "best" tuning parameter setting, a task that is pragmatically improbable to achieve in many applications, we proceed with a collection of tuning parameter candidates, employing the Model Confidence Set test to select the most accurate models from the collection of promising candidates. Using bond credit spread data for three large emerging market economies and an array of input variables motivated by economic theory, we apply our framework to identify relatively small sets of SVR models with su perior out-of-sample forecasting performance. Benchmarking our SRV forecasts against random walk and conventional linear model forecasts provides evidence for the notably superior forecasting accuracy of SRV-based models. In contrast to routinely used linear model benchmarks, the SRV-based models can generate accurate forecasts using only a small set of input variables limited to the country-specific credit-spread-curve factors, lending some support to the rational expectation theory of the term structure in the context of emerging market credit spreads. Consequently, our evidence indicates a better ability of highly flexible SVR to capture investor expectations about future spreads reflected in today's credit spread curve.