Showing results 1 to 7 of approximately 7.(refine search)
The impact of risk cycles on business cycles: a historical view
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 on 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 ...
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 and caused by 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, with a no obvious way to identify which method is the best. Finally, we discuss the main problems in risk ...
Relative Liquidity and Future Volatility
The main contribution of this paper is to identify the strong predictive power of the relative concentration of depth provision, rather than volume of orders, over volatility. To this end, we propose a new measure, relative liquidity (RLIQ), which extracts information from a limit order book distribution and captures the level of consensus on a security's 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 ...
Effects of Information Overload on Financial Markets: How Much Is Too Much?
Motivated by cognitive theories verifying that investors have limited capacity to process information, we study the effects of information overload on stock market dynamics. We construct an information overload index using textual analysis tools on daily data from The New York Times since 1885. We structure our empirical analysis around a discrete-time learning model, which links information overload with asset prices and trading volume when investors are attention constrained. We find that our index is associated with lower trading volume and predicts higher market returns for up to 18 ...
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 ...
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.
How global risk perceptions affect economic growth
The global crisis in 2008 reminded us of the importance of the financial sector for the macroeconomy, a lesson many had forgotten in the decades after the previous global crisis, the Great Depression. Financial risk matters. It is necessary for investment and growth, while also driving uncertainty, inefficiency, recessions, and crises.