Marcela Valenzuela

Model Risk of Risk Models

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.

Model risk of risk models

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.

Can We Prove a Bank Guilty of Creating Systemic Risk? A Minority Report

Because increasing a banks capital requirement to improve the stability of the financial system imposes costs upon the bank, a regulator should ideally be able to prove beyond a reasonable doubt that banks classified as systemically risky really do create systemic risk before subjecting them to this capital punishment. Evaluating the performance of two leading systemic risk models, we show that estimation error alone prevents the reliable identification of the most systemically risky banks. We conclude that it will be a considerable challenge to develop a riskometer that is sound and reliable enough to provide an adequate foundation for macroprudential policy.

Relative Liquidity and Future Volatility

The main contribution of this paper is to identify the strong predictive power of the relative, rather than the absolute, volume of orders over volatility. To this end, we propose a new measure, relative liquidity, which accounts for how quoted depth is distributed in a limit order book and captures the level of consensus on a securitys trading price. Higher liquidity provision farther away from the best quotes, relative to the rest of the book, is associated with a disagreement on the current price and followed by high volatility. The relationship is robust to the inclusion of several alternative measures.

Low risk as a predictor of financial crises

Reliable indicators of future financial crises are important for policymakers and practitioners. While most indicators consider an observation of high volatility as a warning signal, this column argues that such an alarm comes too late, arriving only once a crisis is already under way. A better warning is provided by low volatility, which is a reliable indication of an increased likelihood of a future crisis.

Learning from History : Volatility and Financial Crises

We study the effects of volatility on financial crises by constructing a cross-country database spanning over 200 years. Volatility is not a significant predictor of crises whereas unusually high and low volatilities are. Low volatility is followed by credit build-ups, indicating that agents take more risk in periods of low financial risk consistent with Minsky hypothesis, and increasing the likelihood of a banking crisis. The impact is stronger when financial markets are more prominent and less regulated. Finally, both high and low volatilities make stock market crises more likely, while volatility in any form has no impact on currency crises.