lCARE -- localizing Conditional AutoRegressive Expectiles

TL;DR

This paper introduces a local parametric approach to model time-varying tail risk in financial portfolios, improving risk estimation and portfolio protection by adaptively selecting optimal data intervals.

Contribution

It proposes a data-driven method for dynamically estimating tail risk parameters using a sequential test, outperforming fixed-interval models and quantile-based methods.

Findings

The method selects 3-6 months of data for optimal risk estimation.

It outperforms fixed one-year interval models in empirical tests.

The approach enhances portfolio protection and asset allocation strategies.

Abstract

We account for time-varying parameters in the conditional expectile-based value at risk (EVaR) model. The EVaR downside risk is more sensitive to the magnitude of portfolio losses compared to the quantile-based value at risk (QVaR). Rather than fitting the expectile models over ad-hoc fixed data windows, this study focuses on parameter instability of tail risk dynamics by utilising a local parametric approach. Our framework yields a data-driven optimal interval length at each time point by a sequential test. Empirical evidence at three stock markets from 2005-2016 shows that the selected lengths account for approximately 3-6 months of daily observations. This method performs favorable compared to the models with one-year fixed intervals, as well as quantile based candidates while employing a time invariant portfolio protection (TIPP) strategy for the DAX, FTSE 100 and S&P 500 portfolios. The tail risk measure implied by our model finally provides valuable insights for asset allocation and portfolio insurance.