Model risk and the implications for risk management, macroprudential policy, and financial regulations
Jon Danielsson, Kevin James, Marcela Valenzuela, Ilknur Zer 08 June 2014
Risk forecasting is central to financial regulations, risk management, and macroprudential policy. This column raises concerns about the reliance on risk forecasting, since risk forecast models have high levels of model risk – especially when the models are needed the most, during crises. Policymakers should be wary of relying solely on such models. Formal model-risk analysis should be a part of the regulatory design process.
Risk forecasting is central to macroprudential policy, financial regulations, and the operations of financial institutions. Therefore, the accuracy of risk forecast models – model risk analysis – should be a key concern for the users of such models. Surprisingly, this does not appear to be the case. Both industry practice and regulatory guidance currently neglect the risk that the models themselves can pose, even though this problem has long been noted in the literature (see for example Hendricks 1996 and Berkowitz and O’Brien 2002).
financial crises, financial regulation, forecasting, risk management, Macroprudential policy
Toward a world of larger disasters? Ideas for risk-management policies
Stéphane Hallegatte 14 April 2012
Earlier this week, much of Southeast Asia was stunned by an earthquake that for a moment brought back memories of the devastating tsunami of 2004. The cost of such natural disasters has been on the rise in recent years due to an increase in the number of people living and working in high-risk areas. This column explores some of the reasons behind this increase.
It is widely recognised that economic losses due to natural disasters have been increasing exponentially in the last decades. The main drivers of this trend are the increase in population and the growth in wealth per capita. With more and richer people, it is not surprising to find an increase in disaster losses. More surprising is the fact that, in spite of growing investments in risk reduction, the growth in losses has been as fast as economic growth (eg Miller et al 2008), or even faster than economic growth (eg Bouwer et al 2007).
risk management, natural disasters
The appropriate use of risk models: Part II
Jon Danielsson, Robert Macrae 17 June 2011
Financial risk models have been widely criticised for both theoretical and practical failures, especially during the recent financial crisis. In the second of two columns, the authors outline why the shortcomings of risk models matter before making suggestions for how the financial industry and supervisors should use models in practice.
In our last column (Danielsson and Macrae 2011), we consider the three key problems arising from data snooping, error maximisation, and extreme forecasts. All of these will arise to some extent in most practical situations; this fact must be taken into account when we consider the proper use of these models.
Risk models are used in four different (though overlapping) situations:
Financial markets International finance
financial regulation, risk management
Too much capital, not enough safety?
Avinash Persaud 13 June 2009
There is a strong consensus that banks had insufficient reserves set aside for a rainy day and that they should be required to hold more capital. This column says we should differentiate institutions less by what they are called and more by how they are funded. Encouraging individual risks to flow to those who can absorb them would make the system safer and introduce new players with risk capacities.
There is a strong consensus that banks had insufficient reserves set aside for a rainy day and that they should be required to hold more capital – more capital for credit risks, more capital for the economic cycle, more capital for liquidity risks, more capital for operational risks, more capital for risks as a result of compensation practices, in short, more capital for anything that moves (see Gersbach 2009; Perotti and
risk, capital requirements, risk management