Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio

Abstract

In this paper we perform a quantitative analysis, under the IFRS 9 framework, on the tradeoff of classifying a financial asset at amortized cost versus at fair value. We define and implement a banking impairment model in order to quantify the forward-looking expected credit loss. Based on the suggested impairment model we conduct a backtest on the 10-year Portuguese Government bonds, for the time period from January 2003 to December 2019. The Portuguese bonds' history constitutes a very rich data set for our experiment, as these bonds have experienced significant downgrades during the 2011-2014 financial crisis. We suggest a quantitative and systematic approach in order to find efficient allocations, in an income/downside comprehensive income bi-dimensional space. Resorting to stochastic simulation, we show a possible approach to mitigate the estimation error ingrained in the proposed bi-objective stochastic model. Finally, we assess the out-of-sample performance of some of the suggested efficient allocations.

Bank strategic asset allocation under a unified risk measure

“…First, the optimizer gives a clear prevalence to book value asset classes (namely mortgages), which, unlike fair value classes, do not generate volatility in the balance sheet from changes in market prices. This finding is consistent with [8], who conduct an asset optimization methodology for securities at fair value and amortized cost. We also observe that leverage is correlated to risk (not surprisingly).…”

“…The interest rate risk for assets at book value is different: it comes from a long-term potential loss in net interest margin on these assets in case there is an increase in funding costs. As shown by [8] using a simulation model, the interest rate risk for assets at fair-value is typically much higher than the interest rate risk for assets at book-value.…”

Audit Data and Management Platform Based on Intelligent Algorithm

“…(3) Distance-based outlier detection algorithm The distance-based outlier detection algorithm is mainly used to calculate the distance between each object and other objects based on the number of outliers that users want to find before this, and then calculate the distance between each object and all other objects, so that the M objects with the largest distance sum are outliers [19].…”

A penalty decomposition algorithm with greedy improvement for mean‐reverting portfolios with sparsity and volatility constraints

“…Another favorable property for a portfolio is sparsity, which helps to minimize trading costs. Consequently, sparse mean‐reverting portfolios have been studied (Brodie et al., 2009; d'Aspremont, 2011; Fogarasi and Levendovszky, 2012, 2013; Sipos and Levendovszky, 2013; Sipos and Levendovszky, 2015; Cesarone et al., 2018; Long et al., 2018; Corsaro and De Simone, 2019; Zhang et al., 2020; Brito and Júdice, 2021; Naccarato et al., 2021; Sehgal and Mehra, 2021; van Staden et al., 2021). Sparsity has also shown to be advantageous in numerous applications (Shen and Mousavi, 2018; Mousavi et al., 2020, 2019; Mousavi and Shen, 2019; Shen and Mousavi, 2019; Mohammadi et al., 2020, 2021a, 2021b).…”