Credit ratings are fundamental in assessing the credit risk of a security or debtor. The failure of the Collateralized Debt Obligation (CDO) ratings during the financial crisis of 2007-2008 and the massive undervaluation of corporate risk leading up to the crisis resulted in a review of rating approaches. Yet the fundamental metric that guides the construction of credit ratings has not changed. We study the inadequacies of the old metric in simple models of investment and in structured finance portfolio optimization tasks, and we propose a new methodology based on a buffered probability of exceedance. The new approach offers a conservative risk assessment, with substantial conceptual and computational benefits. We illustrate the new approach using several examples and report the results of a structuring step-up CDO case study, with details available in an online Supplement.

We design an adaptive framework for the detection of illegal trading behavior. Its key component is an extension of a pattern recognition tool, originating from the field of signal processing and adapted to modern electronic systems of securities trading. The new method combines the flexibility of dynamic time warping with contemporary approaches from extreme value theory to explore large-scale transaction data and accurately identify illegal trading patterns. Importantly, our method does not need access to any confirmed illegal transactions for training. We use a high-frequency order book dataset provided by an international investment firm to show that the method achieves remarkable improvements over alternative approaches in the identification of suspected illegal insider trading cases.

We study the impact of exchange rate volatility on cost efficiency and market structure in a cross-section of banks that have non-trivial exposures to foreign currency (FX) operations. We use unique data on quarterly revaluations of FX assets and liabilities (Revals) that Russian banks were reporting between 2004 Q1 and 2020 Q2. First, we document that Revals constitute the largest part of the banks’ total costs, 26.5% on average, with considerable variation across banks. Second, we find that stochastic estimates of cost efficiency are both severely downward biased – by 30% on average – and generally not rank preserving when Revals are ignored, except for the tails, as our nonparametric copulas reveal. To ensure generalizability to other emerging market economies, we suggest a two-stage approach that does not rely on Revals but is able to shrink the downward bias in cost efficiency estimates by two-thirds. Third, we show that Revals are triggered by the mismatch in the banks’ FX operations, which, in turn, is driven by household FX deposits and the instability of Ruble’s exchange rate. Fourth, we find that the failure to account for Revals leads to the erroneous conclusion that the credit market is inefficient, which is driven by the upper quartile of the banks’ distribution by total assets. Revals have considerable negative implications for financial stability which can be attenuated by the cross-border diversification of bank assets.