Financial risk modeling is the use of formalmathematical andeconometric techniques to measure, monitor and control themarket risk,credit risk, andoperational risk on a firm'sbalance sheet, on a bank's accounting ledger of tradeable financial assets, or of afund manager's portfolio value; seeFinancial risk management.Risk modeling is one of many subtasks within the broader area offinancial modeling.
Risk modeling uses a variety of techniques includingmarket risk,value at risk (VaR),historical simulation (HS), orextreme value theory (EVT) in order to analyze a portfolio and make forecasts of the likely losses that would be incurred for a variety of risks. As above, such risks are typically grouped intocredit risk,market risk,model risk,liquidity risk, andoperational risk categories.
Many large financial intermediary firms use risk modeling to help portfolio managers assess the amount ofcapital reserves to maintain, and to help guide their purchases and sales of various classes offinancial assets.
Formal risk modeling is required under theBasel II proposal for all the major international banking institutions by the various national depository institution regulators. In the past, risk analysis was done qualitatively but now with the advent of powerful computing software, quantitative risk analysis can be done quickly and effortlessly.
Modeling the changes by distributions with finite variance is now known to be inappropriate.Benoît Mandelbrot found in the 1960s that changes in prices in financial markets do not follow aGaussian distribution, but are rather modeled better byLévy stable distributions. The scale of change, or volatility, depends on the length of the time interval to apower a bit more than 1/2. Large changes up or down, also calledfat tails, are more likely than what one would calculate using a Gaussian distribution with an estimatedstandard deviation.[1][2]
Quantitativerisk analysis and its modeling have been under question in the light ofcorporate scandals in the past few years (most notably,Enron),Basel II, the revised FAS 123R and theSarbanes–Oxley Act, and for their failure to predict the2008 financial crisis.[1][3][4]
Rapid development of financial innovations lead to sophisticated models that are based on a set of assumptions. These models are usually prone tomodel risk. There are several approaches to deal with model uncertainty. Jokhadze and Schmidt (2018) propose practical model risk measurement framework based on Bayesian calculation.[5] They introducesuperposed risk measures that enables consistent market and model risk measurement.
Jon Danielsson argues that risk forecasts are very inaccurate, especially in typical sample sizes, and isconcerned about their use in regulations.