How the Early-Warning System Works
Korea · Can today’s market information warn of broad stress within the next 10 trading days?
01 Market Data
KOSPI 200, VKOSPI and the AA− corporate 3Y − government 3Y spread describe equity, volatility and credit conditions.
View data details
The Korean credit measure is AA− corporate 3Y yield minus government 3Y yield.
02 Market Stress Index
MSI equally weights equity, volatility and credit stress using past-only standardization.
Technical definition
Equity stress is the negative 21-trading-day KOSPI 200 return; volatility stress is the VKOSPI level; credit stress is the AA− corporate 3Y − government 3Y spread.
Each component is standardized using a 252-trading-day rolling window shifted by one observation. At least 126 prior observations are required. Standardized components receive equal weights.
A Stress Event occurs when Korea MSI reaches or exceeds its past-only expanding 90th-percentile threshold. Estimation uses observations through t−1 and requires at least 252 prior observations. Stress describes broad market conditions, rather than a simple future KOSPI 200 decline.
03 17 Features
7 equity, 5 volatility and 5 credit features capture returns, volatility, momentum and relative stress.
Feature details
Equity · 7
KOSPI 200 1D Return · KOSPI 200 5D Return · KOSPI 200 20D Return · KOSPI 200 20D Volatility · KOSPI 200 60D Volatility · KOSPI 200 Drawdown · KOSPI 200 20D Momentum
Volatility · 5
VKOSPI Level · VKOSPI 1D Change · VKOSPI 5D Change · VKOSPI 20D Momentum · VKOSPI 252D Z-Score
Credit · 5
AA− Corporate − Government 3Y Spread Level · Credit 1D Change · Credit 5D Change · Credit 20D Momentum · Credit 252D Z-Score
04 10-Day Forward Target
Target₁₀D(t) = 1 if at least one defined Stress Event occurs during t+1 through t+10; otherwise 0.
Target construction
The subsequent 10 trading days provide the training label. Features and event thresholds retain their point-in-time, past-only design.
Observations without a complete forward horizon remain unknown and are not converted to artificial negative labels. Initial observations without a valid event threshold also remain unknown.
05 Logistic Regression / XGBoost
Logistic Regression is the baseline; XGBoost models nonlinear relationships and produces the deployed score.
Model details
Historical scores are generated from out-of-sample predictions. The fitted XGBoost deployment model produces the latest score and SHAP drivers; Logistic Regression remains a benchmark.
Review actual model performance →06 Purged Walk-Forward Validation
Expanding training windows and a 10-day purge separate past training labels from unseen OOS tests.
Validation design
Random train/test splitting is not used. A 10-trading-observation gap removes training labels whose forward target windows overlap the beginning of the test period.
Evaluation uses ROC-AUC, PR-AUC, Brier Score, Precision, Recall, F1, False Alarm Rate and Missed Event Rate. Classification uses the fixed 0.50 raw-score reference (50 / 100 display scale), rather than a threshold selected post-hoc from OOS results.
07 Risk Intelligence
Model scores and SHAP support early warning without implying calibrated event probabilities or investment advice.
Limitations
SHAP identifies features that push the fitted XGBoost output higher or lower. Contributions are model-output effects, not probability-point changes, and do not establish causality.
Future performance can differ from historical OOS results. The system does not guarantee Stress Events or predict future KOSPI 200 price direction.
Korea is independently modeled with its own warning logic; U.S. bands do not apply. Credit spreads are proxies for credit conditions.
Model outputs are decision-support signals, not investment recommendations. Scores are not necessarily calibrated event probabilities.