The long-term interest rates correlations: a new indicator predicting recession
Bibliographic record
Abstract
Purpose Existing research has theoretically modeled conditional correlations between the long-term interest rates as a function of macroeconomic variable. In line with it, the purpose of this paper is to explore whether conditional correlations can be a new signal to predict recessions. Furthermore, this paper also tries to investigate among the four factors – the time difference of the beginning and the end of recessions, financial integration (FI), and trade integration (TI) – which factors drive the direction of change in conditional correlations. Finally, this paper is to explain the implication for Korea trade. Design/methodology/approach This study uses a probit regression model for 33 country during the period from 1972 to 2015. To measure the time-varying interest rates conditional correlations, a VAR(1)-DBEKK-GARCH(1,1) model is adopted due to its statistical advantages. Furthermore, the authors also construct the four measures – time difference of the beginning of recessions (BEG), time difference of the end of recessions (END), FI, and TI. The authors first study the predictive power of correlations in both in and out-samples test, and study which factors determine the different behavior of interest rate co-movements using the four measures. Findings The empirical results show that the conditional correlations between the long-term interest rates of the USA and individual countries contain information about recessions a few quarters ahead which term spreads of neither individual countries nor the USA conveyed in. However, there is a heterogeneity of the significance and direction of interest rate correlations. A further research reveals that especially the heterogeneous degree of TI leads to the different overlapped recession period of individual countries with the USA, resulting in heterogeneous behavior of interest rates among countries. Research limitations/implications As a limitation of this paper, the forecasting power of interest rate correlations is not always significant in all countries. Despite this, the study has a profound implication that for those countries where the US accounts for the high proportion of trade, increase in conditional correlations can be a signal for future recessions. Especially, given a considerable portion of trade in GDP and the more sensitive trade activity of Korea to a contagious recession than a domestic recession, the conditional correlation measure is particularly useful for Korean policy makers. Originality/value Although many papers model interest rate co-movement as a function of macroeconomic condition, this paper provides the first evidence to show interest rate co-movement precede the macro shocks empirically. Furthermore, this paper determines the precise channel through which TI affects the time-varying behavior of interest rate co-movements before recessions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".