The stability of the banking sector is of major importance for economic outcomes. Banks form the backbone of modern economies and instability in the banking sector can pose problems to the economic system as a whole. Credit losses, or more generally, asset quality problems, have repeatedly been identified as a key trigger of bank failures, e.g. Graham and Horner (1988), Caprio and Klingebiel (1996). Accordingly, much research effort has gone into developing methods for assessing credit risk both at a systemic and bank-specific level.
Two major components determine the extent of a credit loss suffered: first, the probability of a default (PD) and, second, the loss given default (LGD), which equals one minus the recovery rate in the event of default. Most credit risk literature has focussed on estimating PD; much less attention has been devoted to estimating characteristics of LGD. We address this lack of research by analyzing the determinants of LGD (or, more specifically, the recovery rate) using a comprehensive sample of Australian banks.
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In recent years, particular stress has been laid on the substitution of variance as a risk measure in the standard Markowitz [11] mean-variance problem. Since it makes no distinction between positive and negative deviations from the mean, variance is a good measure of risk only for distributions that are (approximately) symmetric around the mean, such as the normal distribution or, more generally, elliptical distributions [12]. However, in most cases, such as in portfolios containing options, one deals with wealth distributions that are highly skewed. It is thus more reasonable to consider asymmetric risk measures since individuals are typically loss averse. In this regard, Value-at-Risk (VaR), a downside risk measure, has emerged as an industry standard with regulatory authorities, such as the Basle Committee on Banking Supervision which enforces its use [9].
