CAESar: Conditional Autoregressive Expected Shortfall

TL;DR

This paper introduces CAESar, a novel dynamic model for jointly estimating Value at Risk and Expected Shortfall without distributional assumptions, improving tail risk forecasting in financial portfolios.

Contribution

The paper proposes CAESar, a flexible autoregressive model for ES and VaR, incorporating heteroskedastic effects and monotonicity constraints, advancing risk measurement methods.

Findings

CAESar outperforms existing models in forecasting accuracy.

The model effectively captures dynamic tail risks.

Empirical tests confirm robustness across financial data.

Abstract

In financial risk management, Value at Risk (VaR) is widely used to estimate potential portfolio losses. VaR's limitation is its inability to account for the magnitude of losses beyond a certain threshold. Expected Shortfall (ES) addresses this by providing the conditional expectation of such exceedances, offering a more comprehensive measure of tail risk. Despite its benefits, ES is not elicitable on its own, complicating its direct estimation. However, joint elicitability with VaR allows for their combined estimation. Building on this, we propose a new methodology named Conditional Autoregressive Expected Shortfall (CAESar), inspired by the CAViaR model. CAESar handles dynamic patterns flexibly and includes heteroskedastic effects for both VaR and ES, with no distributional assumption on price returns. CAESar involves a three-step process: estimating VaR via CAViaR regression, formulating ES in an autoregressive manner, and jointly estimating VaR and ES while ensuring a monotonicity constraint to avoid crossing quantiles. By employing various backtesting procedures, we show the effectiveness of CAESar through extensive simulations and empirical testing on daily financial data. Our results demonstrate that CAESar outperforms existing regression methods in terms of forecasting performance, making it a robust tool for financial risk management.