A Framework for Analysis and Prediction of Operational Risk Stress

Mathematical and Computational Applications

Peter Mitic¹ 

[1] Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK;

关键词: risk factor;    stress testing;    economic stress;    Loess;    lognormal;    regulatory capital;   

DOI  :  10.3390/mca26010019

来源: DOAJ

【 摘 要 】

A model for financial stress testing and stability analysis is presented. Given operational risk loss data within a time window, short-term projections are made using Loess fits to sequences of lognormal parameters. The projections can be scaled by a sequence of risk factors, derived from economic data in response to international regulatory requirements. Historic and projected loss data are combined using a lengthy nonlinear algorithm to calculate a capital reserve for the upcoming year. The model is embedded in a general framework, in which arrays of risk factors can be swapped in and out to assess their effect on the projected losses. Risk factor scaling is varied to assess the resilience and stability of financial institutions to economic shock. Symbolic analysis of projected losses shows that they are well-conditioned with respect to risk factors. Specific reference is made to the effect of the 2020 COVID-19 pandemic. For a 1-year projection, the framework indicates a requirement for an increase in regulatory capital of approximately 3% for mild stress, 8% for moderate stress, and 32% for extreme stress. The proposed framework is significant because it is the first formal methodology to link financial risk with economic factors in an objective way without recourse to correlations.

【 授权许可】

Unknown