The complex relationship between inflation and equity returns
Bibliographic record
Abstract
Purpose To investigate the complex relationship between inflation, inflation uncertainty and equity returns. Design/methodology/approach This paper uses a bivariate VARMA, GARCH-in-mean, asymmetric BEKK model to investigate the relationship between inflation, inflation uncertainty and equity returns. Findings Using monthly inflation and equity returns data for the G7 and EM7 economies, we find that the effects of inflation and inflation uncertainty on equity returns vary across countries. Research limitations/implications The mixed evidence we find potentially reflects the changing dynamics, policy regimes, economic shocks and country-specific factors (such as differences in the financing patterns of enterprises and the legal and financial environments) across the G7 and EM7 countries. Practical implications We contribute to the empirical literature in the following ways. First, we rely on a wide sample of countries, including both developed and emerging economies. Second, we extend previous research by estimating a GARCH-in-mean model of monthly equity returns in which both realized returns and their conditional volatility are allowed to vary with inflation. Previous articles that studied the relationship between inflation and stock market returns generally sought time-invariant effects of inflation on stock returns. Social implications The paper helps to reconcile the divergent results of previous empirical studies and distinguish between alternative explanations of the relationship between inflation and equity returns. Originality/value Our study provides an improved comprehension of the ambiguous relationship between inflation, inflation uncertainty and equity returns under various central bank mandates and different levels of central bank independence. The mixed empirical evidence across countries we present provides insights for the macroeconomic models that consider the relationship between uncertainty and macroeconomic performance as a fundamental building block. Therefore, our empirical study calls for further work on the relationship between inflation, inflation uncertainty and equity returns.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".