Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR

TL;DR

This paper introduces a Bayesian approach that incorporates volatility clustering to improve the estimation of portfolio VaR and CVaR, allowing for rapid adaptation to market volatility changes.

Contribution

It develops a volatility-sensitive Bayesian method with conjugate priors based on rolling windows, enhancing responsiveness to volatility shifts compared to existing methods.

Findings

Performs well on simulated data

Effective during turbulent market periods

Adapts quickly to changing volatility

Abstract

In this paper, a new way to integrate volatility information for estimating value at risk (VaR) and conditional value at risk (CVaR) of a portfolio is suggested. The new method is developed from the perspective of Bayesian statistics and it is based on the idea of volatility clustering. By specifying the hyperparameters in a conjugate prior based on two different rolling window sizes, it is possible to quickly adapt to changes in volatility and automatically specify the degree of certainty in the prior. This constitutes an advantage in comparison to existing Bayesian methods that are less sensitive to such changes in volatilities and also usually lack standardized ways of expressing the degree of belief. We illustrate our new approach using both simulated and empirical data. Compared to some other well known homoscedastic and heteroscedastic models, the new method provides a good alternative for risk estimation, especially during turbulent periods where it can quickly adapt to changing market conditions.