  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.
