A constrained robust Markov regime-switching model for long-term risk evaluation
Bibliographic record
Abstract
Markov Regime-Switching (MRS) models are widely used for modeling equity return time series. Yet standard MRS models may inadequately capture the mean reversion behavior of long-term equity returns and exhibit unstable parameter estimation due to their reliance on normality assumptions within each regime. These limitations in model adequacy can compromise the accuracy of risk exposure measurements for invested assets. To address these issues, we propose a constrained robust MRS (CRMRS) model, which integrates an order restriction and sparse constraints on regime means and transition probabilities to better capture mean reversion while employing a general ρ-based least favorable distribution to improve distributional flexibility across regimes. We assess the method's performance through finite-sample simulations under various scenarios in the presence or absence of atypical values. Furthermore, we empirically validate the improvements in model adequacy and risk exposure measurement using monthly returns from the S&P/TSX Composite Index, the benchmark for Canadian equity performance, where S&P and TSX stand for Standard & Poor's and the Toronto Stock Exchange, respectively. Our findings demonstrate that the proposed CRMRS-Huber produces stable parameter estimates and superior approximations of higher-order moments, such as skewness and kurtosis, and provides balanced intermediate risk evaluation across all cases.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".