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Ex Ante Regime-Aware Evaluation of VaR-ES Tail Risk Models: A Multidimensional Assessment of Statistical Adequacy, Predictive Performance, and Stability
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Orientador(es)
Resumo(s)
The regulatory transition in market risk oversight from Value at Risk to Expected Shortfall has transformed how tail-risk forecasts are evaluated. Model validity is now assessed in a complementary evaluation that combines backtesting procedures with joint VaR-ES scoring, not only by violation frequency but also by tail-loss severity. Despite this regulatory adjustment, methodological challenges arise in maintaining statistical and practical consistency in tail risk estimates due to volatility clustering and state-dependent shifts in conditional variance dynamics. While regime dependent volatility structures have become more widely recognized in empirical finance, it is not yet evident whether prospective regime adjustments drive sustained improvements across adequacy dimensions within this scoring-based context. Without incorporating scoring-based evaluation, comparative assessments of VaR-ES forecasts could lead to variability in evaluation results, fundamentally altering how regime-specific findings are interpreted. This analysis compares baseline and regime-aware specifications to assess performance trade offs across statistical, scoring-based, and stability dimensions. The modeling approach evaluates conventional volatility models against macro financial regime dependent refinements activated by observable threshold indicators. This evaluation incorporates joint VaR-ES scoring, rank-stability diagnostics, and conditional coverage backtests within a multidimensional evaluation approach at the 95% and 99% tail quantiles. The empirical findings indicate that incorporating regime awareness produces state-contingent performance gains rather than uniform improvements in statistical adequacy and scoring-based evaluation across evaluation dimensions. Enduring volatility persistence exerts greater influence than regime responsiveness in evaluating performance under extreme market conditions. The results indicate that regime-aware adjustments yield contextdependent marginal improvements but do not consistently enhance overall model adequacy.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
Palavras-chave
Value at Risk (VaR) Expected Shortfall (ES) Regime-Aware Risk Modeling Joint VaR-ES Backtesting Cross-Quantile Stability