This paper aims to evaluate an inference of bank internal PDs (Default Probabilities) on subsequent prepayments of variable rate institutional loans. Since variable rate loans hardly present an economic motivation for early prepayments in that they would not offer a cheaper refinancing alternative, we test the conjecture of a correlation between improvements in obligors’ creditworthiness (as reflected by negative changes in Bank Internal PDs) and subsequent loan prepayments, as obligors might be tempted to renegotiate more advantageous terms (lower credit spreads) with their lenders. The analysis is purported to serve as an early warning mechanism for banks pursuing the Basel III internal rating-based (IRB) approach for unexpected inflows of liquidity in the near future. We use Machine Learning (ML) ensemble methods to forecast potential prepayments and perform a conditional prepayment analysis to make an inference on the prepayment amounts and the prepayment timing distributions while controlling for macroeconomic and corporate idiosyncratic characteristics.
This paper aims to develop a methodology for the estimation of the idiosyncratic confidence level inherent within the process of determining the threshold of separation between volatile and stable deposit volumes. The idiosyncratic confidence level must be reflective of the institution’s specific risk preferences and liquidity risk management policies as anchored into the Principle 9 of the European Banking Authority and Basel Committee for Banking Supervision recommendations. We illustrate the proposed methodology by including liquidity constraints from the Basel III regulatory recommendations introduced in 2013. Furthermore, we point to other ancillary applications of such procedures in the financial risk management practice.
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