A1383
Title: Predicting emerging market credit spreads with support vector regression: Exploiting ubiquitous local optima
Authors: Alena Audzeyeva - Keele University (United Kingdom)
Gary Anderson - CEMAR LLC (United States) [presenting]
Abstract: A coherent framework is proposed using support vector regression (SVR), 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 SVR kernel functions. In contrast to previous approaches identifying a single best tuning parameter setting, a task that is practically unattainable in many financial market 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 superior out-of-sample forecasting performance. Benchmarking our SVR forecasts against the random walk and conventional linear model forecasts provides evidence for the notably superior forecasting accuracy of SVR-based models. 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.
