Toward More Accurate Portfolio Modeling, with Equity and Credit Risk Applications
22 Pages Posted: 8 Nov 2011 Last revised: 8 Mar 2012
Date Written: November 7, 2011
Abstract
We describe a numerical method to estimate joint physical probability distributions on large portfolios. This method allows for conditioning on available firm-specific side information and can accommodate portfolios with "new'' instruments (for example, from IPO's) that lack historical data. We show that for the data that we examine, our models are more accurate than certain benchmark models, in the sense that our models, on average, better place probability mass where it belongs, as measured by out-of-sample likelihood.
Keywords: Large portfolio models, copula models, factor models, correlation, dependence, equity, credit, fat tails
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