Modeling and Forecasting Tail Risk Spillovers: A Component-Based CAViaR Approach

Battle Count: 52

Highly relevant for quantitative trading and risk management. The CAViaR-SE model provides improved tail risk forecasts (VaR and ES) that are directly applicable to position sizing, stop-loss calibration, portfolio risk limits, and regulatory capital calculations. The spillover decomposition allows traders to understand whether tail risk is driven by asset-specific factors or cross-market contagion, informing hedging strategies. The out-of-sample MCS results demonstrate practical forecasting superiority. However, the model is primarily a risk measurement tool rather than a direct trading signal generator.

Moderate complexity. The CAViaR framework itself is well-established, but the component-based decomposition requires careful implementation of the joint VaR-ES estimation via Fissler-Ziegel loss. The recursive partial correlation selection algorithm involves iterative regression steps and PCA for weight construction. The main challenges include: (1) correctly implementing the asymmetric Laplace pseudo-likelihood optimization, (2) the recursive asset selection algorithm with proper stopping criteria, (3) ensuring identifiability constraints (β₁ > φ₁, |φ₁| < 1), and (4) the rolling-window out-of-sample evaluation framework. No code is provided, requiring independent implementation.

The empirical application uses DJIA stocks (publicly available data) with a clearly defined sample period (March 19, 2008 – November 14, 2025). The recursive selection algorithm is described step-by-step. However, no code or repository is provided. The specific implementation of the Fissler-Ziegel loss minimization and the recursive algorithm details (e.g., exact stopping criteria, handling of ties) could benefit from supplementary material. The selection of 11 representative assets from 29 DJIA tickers is not fully justified in the extract.