We consider Bayesian estimation of Value-at-Risk (VaR) using parametric Product Partition Models (PPM). VaR is a standard tool to measure and control the market risk of an asset or a portfolio, and it is also required for regulatory purposes. We use PPM to provide robustly Bayesian estimators of VaR, remaining in a Normal setting, even in presence of outlying points. We consider two different scenarios: a product partition structure on the vector of means and a product partition structure on the vector of variances. In both frameworks we obtain a closed-form expression for VaR. The results are illustrated with an application to a set of shares from the Italian stock market. The methodology and the obtained results are described in details in Bormetti et al. (2009).
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
|Titolo:||Bayesian Analysis of Value-at-Risk with Product Partition Models|
|Data di pubblicazione:||2009|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|