We investigate the effects of competition and signaling in a pure order driven market and examine the trading patterns of agents when walking through the book is not allowed. Our results suggest that the variables capturing the cost of a large market order are not informative for an impatient trader under this market mechanism. We also document that the competition effect is not present only at the top of the book but persistent beyond the best quotes. Moreover, it dominates the signaling effect for both a limit order and a market order trader. Finally, we show that institutional investors’ order submission strategies are characterized by only a few pieces of the limit order book information. This is consistent with informed traders placing orders based on their own private valuations rather than the state of the book.

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.

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 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 inclusion of several alternative measures.

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.

This paper provides a comprehensive survey of existing measures of uncertainty, risk, and volatility, noting their conceptual distinctions. It summarizes how they are constructed, their relative advantages in usage, and their effects on financial market and economic outcomes. The measures are divided into four categories based on the construction methodology: news-based, survey-based, econometric-based, and market-based measures. While heightened uncertainty is typically associated with negative real and financial outcomes, the magnitude of these effects and the interpretation of transmission channels crucially depend on identification considerations.