2021/04/23 by Narayan Ganesan, Ganesan, Narayan, Bernhard Hientzsch +1
Economics, Econometrics and Finance · Social Sciences · #Computational Finance (q-fin.CP) #Credit Risk and Financial Regulations #FOS: Economics and business #Insurance, Mortality, Demography, Risk Management #Mathematical Finance (q-fin.MF) #Risk Management (q-fin.RM) #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.2104.11768
openalex publication_date 2021/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Predicting future values at risk (fVaR) is an important problem in finance.\nThey arise in the modelling of future initial margin requirements for\ncounterparty credit risk and future market risk VaR. One is also interested in\nderived quantities such as: i) Dynamic Initial Margin (DIM) and Margin Value\nAdjustment (MVA) in the counterparty risk context; and ii) risk weighted assets\n(RWA) and Capital Value Adjustment (KVA) for market risk. This paper describes\nseveral methods that can be used to predict fVaRs. We begin with the Nested\nMC-empirical quantile method as benchmark, but it is too computationally\nintensive for routine use. We review several known methods and discuss their\nnovel applications to the problem at hand.\n The techniques considered include computing percentiles from distributions\n(Normal and Johnson) that were matched to parametric moments or percentile\nestimates, quantile regressions methods, and others with more specific\nassumptions or requirements.\n We also consider how limited inner simulations can be used to improve the\nperformance of these techniques. The paper also provides illustrations,\nresults, and visualizations of intermediate and final results for the various\napproaches and methods.\n