Forecasting Financial Stress Indices in Korea: A Factor Model Approach

Abstract

Contents 1. Introduction ··········································································· 1 2. The Econometric Model ····················································· 3 3. Data Descriptions and Factor Estimations ··················· 7 3.1. Data descriptions ························································································ 7 3.2. Latent factors and their characteristics ·············································· 9 4. Forecasting Exercises ························································· 16 4.1. In-Sample fit analysis ·············································································· 16 4.2. Out-of-Sample forecast exercises and model evaluations ·········· 18 5. Concluding Remarks ·························································· 25 References ·················································································· 26 Forecasting Financial Stress Indices in Korea: A Factor Model ApproachWe 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.

Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments

“…ere are many related studies on macroeconomic forecasting based on factor models and machine learning techniques. In the last two decades, forecasting studies using large-scale datasets and pseudo-out-of-sample forecast incorporate those by Artis et al [8]; Boivin and Ng [9,10]; Forni et al [11]; Armah and Swanson [12,13]; Stock and Watson [14][15][16][17][18]; Varian [5]; Kim and Swanson [19,20]; Castle et al [21,22]; Luciani [23]; Kristensen [24]; Swanson and Xiong [6,25]; Tu and Lee [26]; Swanson et al [27]; Maehashi and Shintani [28]; Kim and Ko [29]; Kim et al [30]; Abdić et al [31]; and and Kim and Shi [32].…”

Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach

“…3) Bai and Ng (2004) propose a similar method for their panel unit root test procedure that uses PC to estimate latent factors. 4) Kim, Shi, and Kim (2016) implemented similar forecasting exercises using factor estimates from the PC method, which utilizes 198 predictor variables but not the target variable.…”