The quality of statistical risk models is much lower than often assumed. Such models are useful for measuring the risk of frequent small events, such as in internal risk management, but not for systemically important events. Unfortunately, it is common to see unrealistic demands placed on risk models. Having a number representing risk seems to be more important than having a number which is correct. Here, it is demonstrated that even in what may be the easiest and most reliable modeling exercise, value-at-risk forecasts from the most commonly used risk models provide very inconsistent results.

Many financial applications, such as risk analysis, and derivatives pricing, depend on time scaling of risk. A common method for this purpose is the square-root-of-time rule where an estimated quantile of a return distribution is scaled to a lower frequency by the square root of the time horizon. This paper examines time scaling of quantiles when returns follow a jump diffusion process. We demonstrate that when jumps represent losses, the square-root-of-time rule leads to a systematic underestimation of risk, whereby the degree of underestimation worsens with the time horizon, the jump intensity and the confidence level.

The rapid adoption of artificial intelligence (AI) poses new and poorly understood threats to financial stability. We use a game-theoretic model to analyse the stability impact of AI, finding that it amplifies existing financial system vulnerabilities — leverage, liquidity stress and opacity — through superior information processing, common data, speed and strategic complementarities. The consequence is crises become faster and more severe, where the likelihood of a crisis is directly affected by how effectively the authorities engage with AI. In response, we propose that the financial authorities develop their own AI systems and expertise, establish direct AI-to-AI communication, implement automated crisis facilities and monitor AI use.

There are increasing calls for the regulation of hedge funds, both for consumer protection and systemic reasons. We argue that the consumer protection arguments for direct regulation are not convincing, but find that the systemic concerns are sufficiently serious to warrant some forms of regulation. Existing regulatory methods, disclosure and activity restrictions, are unsuitable for hedge funds. Any future regulation must reduce the likelihood and potential costs of the failure of systemically important hedge funds while at the same time preserving the wider market benefits of hedge funds’ ongoing activities.

Artificial intelligence (AI) is rapidly changing how the financial system is operated, taking over core functions for both cost savings and operational efficiency reasons. AI will assist both risk managers and the financial authorities. However, it can destabilize the financial system, creating new tail risks and amplifying existing ones due to procyclicality, unknown-unknowns, the need for trust, and optimization against the system.

We investigate the effects of financial risk cycles on business cycles, using a panel spanning 73 countries since 1900. Agents use a Bayesian learning model to form their beliefs about risk. We construct a proxy of these beliefs and show that perceived low risk encourages risk-taking, augmenting growth at the cost of accumulating financial vulnerabilities, and, therefore, a reversal in growth follows. The reversal is particularly pronounced when the low-risk environment persists and credit growth is excessive. Global risk cycles have a stronger effect on growth than local risk cycles via their impact on capital flows, investment, and debt-issuer quality.

We study the effects of stock market volatility on risk-taking and financial crises by constructing a cross-country database spanning up to 211 years and across 60 countries. Prolonged periods of low volatility have strong in-sample and out-of-sample predictive power over the incidence of banking crises and can be used as a reliable crisis indicator, whereas volatility itself does not predict crises. Low volatility leads to excessive credit buildups and balance sheet leverage in the financial system, indicating that agents take more risk in periods of low risk, supporting the dictum that “stability is destabilizing.” Received October 28, 2016; editorial decision February 7, 2017 by Editor Andrew Karolyi.

This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings; hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, further frustrating the reliability of risk readings. Finally, particular conclusions on the underlying reasons for the high model risk and the implications for practitioners and policy makers are discussed.

The experience from the global financial crisis has raised serious concerns about the accuracy of standard risk measures as tools for the quantification of extreme downward risks. A key reason for this is that risk measures are subject to a model risk due, e.g. to specification and estimation uncertainty. While regulators have proposed that financial institutions assess the model risk, there is no accepted approach for computing such a risk. We propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting the imperfect risk forecasts by outcomes from backtesting frameworks, considering the desirable quality of VaR models such as the frequency, independence and magnitude of violations. We also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections.