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.

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

“…Therefore, selecting and weighting indicator variables that capture financial risk from various perspectives is essential when constructing a FSI with indicators from multiple markets. Kim et al [31] and Chen et al [32] used different methods, such as factor analysis and principal component analysis, to extract common factors and construct FSI. Ding et al [33] 30employed a dynamic correlation coefficient method and credit weighting method to build FSI, which is more sensitive and accurate for financial stress identification.…”

“…The core idea is to nonlinearly map the input space to a higher-dimensional feature space, allowing the model to address problems that are not linearly separable. In the hidden layer, the output of the j-th neuron is as follows: (31) where…”

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

“…Therefore, it is imperative to carefully select and weigh indicator variables that encompass financial risk from diverse perspectives when constructing an FSI with indicators from multiple markets. Kim et al [33] and Chen et al [34] used different methods, including factor analysis and principal component analysis, to extract latent factors and construct an FSI. Ding et al [35] employed a dynamic correlation coefficient method in conjunction with a credit weighting approach to construct an FSI, demonstrating enhanced sensitivity and accuracy in identifying financial stress.…”

What charge-off rates are predictable by macroeconomic latent factors?

“…Some recent studies investigate the out-of-sample predictability of FSIs as a proxy for …nancial market vulnerability. See among others, Christensen and Li (2014), Kim, Shi, and Kim (2020), Kim and Ko (2020), and Kim and Shi (2021).…”