A constrained robust Markov regime-switching model for long-term risk evaluation
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it