Forecasting Financial Stress Indices in Korea: A Factor Model Approach

Abstract

We propose factor-based out-of-sample forecast models for the financial stress index and its 4 sub-indices developed by the Bank of Korea. We employ the method of the principal components for 198 monthly frequency macroeconomic data to extract multiple latent factors that summarize the common components of the entire data set. We evaluate the out-of-sample predictability of our models via the ratio of the root mean squared prediction errors and the Diebold-Mariano-West statistics. Our factor models overall outperform the random walk model in forecasting the financial stress indices for up to 1-year forecast horizon. Our models also perform fairly well relative to a stationary autoregressive model especially when the forecast horizon is short, which is practically useful because financial crises often occur abruptly with no systemic warning signals. Parsimonious models with small number of factors perform as well as bigger models. Overall, our findings imply that not only financial data but also real activity variables can help out-of-sample forecast the vulnerability in the financial markets.

Keywords: Financial stress index, Principal component analysis, PANIC, In-sample fit, Out-of-sample forecast, Diebold-Mariano-West Statistic

JEL Classification: C53, G17, C38