Using Diverse Local Optima for Setting Kernel Parameters in Support Vector Regression: Forecasting Emerging Market Credit Spreads

30 Pages Posted: 18 Sep 2024

See all articles by Gary Anderson

Gary Anderson

CEMAR LLC

Alena Audzeyeva

Keele University - Keele Business School

Date Written: August 17, 2024

Abstract

We propose a novel approach for determining support vector regression (SVR) kernel parameters in the presence of multiple local optima. In contrast to existing approaches focusing on identifying a single "best" tuning parameter setting, an impractical goal in many financial market applications, our framework employs a global optimization algorithm to produce a collection of competitive SVR kernel parameter candidates, and applies the Model Confidence Set test to select the most accurate set from the collection of promising candidates. We use our approach to predict credit spreads for four mature emerging market sovereign borrowers. Combining our forecasts into simple forecast combinations and contrasting them against random forest, standard SVR, conventional random walk, and linear model forecasts, we find that forecast combinations from our most accurate model sets deliver substantial gains in forecast accuracy. Furthermore, the analysis of the forecasting results provides useful insights into credit risk pricing by international investors.

Keywords: Support Vector Regression, Kernel parameters, Forecasting, Model Confidence Set, Emerging market credit spreads. JEL Classifications: G17

JEL Classification: G17, G15, C53, C51

Suggested Citation

Anderson, Gary and Audzeyeva, Alena, Using Diverse Local Optima for Setting Kernel Parameters in Support Vector Regression: Forecasting Emerging Market Credit Spreads (August 17, 2024). Available at SSRN: https://ssrn.com/abstract=4929864 or http://dx.doi.org/10.2139/ssrn.4929864

Gary Anderson

CEMAR LLC ( email )

69634 Heather Way
Rancho Mirage, CA 92270
United States

Alena Audzeyeva (Contact Author)

Keele University - Keele Business School ( email )

Keele, ST5 5AA
United Kingdom

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