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The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis.

Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets).

First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis.

These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises.

This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.

This study proposes a framework based on salience theory and shows that focusing on one type of risk (idiosyncratic or systemic) can explain overpricing of securities ex ante, and resales at low prices during crisis periods.

The author consider an overlapping generations (OLG) model where each generation lives for two periods and there is no population growth. Agents (investors) start their lives with an endowment W > 0 and have mean-variance utility. They invest their endowment when young and consume when old. Each period, the young investors optimally choose their portfolio from different risky assets acquired from the old generation, all assumed to be in fixed supply.

The author show that investor salience bias can explain excess volatility of asset prices and the resulting fire-sales in periods of financial turmoil. A change in salience – from one component (idiosyncratic) to the other (systemic) – will generate excess volatility. Interestingly, higher risk aversion generally exacerbates the excess volatility of prices. Moreover, the model predicts that if a big systemic shock hits the financial system, due to salience bias the price of systemic assets falls sharply. This relates to the observed fire-sales of assets during the global financial crisis.

The proposed model and results suggest that there may be a scope for intervention in financial markets during turbulences. In terms of ex ante policies the study suggests that investors and regulator should use better risk assessment technologies.

This is the first study constructing a tractable model based on the argument that investor salience may exacerbate the excess volatility of prices during financial downturns. The author relate salience to two types of risk; idiosyncratic and systemic and assume that investors' risk perception is biased towards the type of risk that is currently salient based on prior beliefs or past data. The author show that the diversification fallacy of the precrisis period, where seemingly safe assets were overpriced, can be explained by agents overweighing idiosyncratic risk and ignoring systemic risk.

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.

We analyze the interaction among the common and country-specific components for the inflation rates in 12 euro area countries through a factor model with time-varying parameters. The variation of the model parameters is driven by the score of the predictive likelihood, so that, conditionally on past data, the model is Gaussian and the likelihood function can be evaluated using the Kalman filter. The empirical analysis uncovers significant variation over time in the model parameters. We find that, over an extended time period, inflation persistence has fallen and the importance of common shocks has increased relatively to that of idiosyncratic disturbances. According to the model, the fall in inflation observed since the sovereign debt crisis is broadly a common phenomenon since no significant cross-country inflation differentials have emerged. Stressed countries, however, have been hit by unusually large shocks.

The purpose of this paper is to offer a “how to” guide for applying Merton’s (1987) valuation adjustment for incomplete information, which depends on market capitalization, idiosyncratic risk and extent of investor ownership.

The paper illustrates Bodnaruk and Ostberg’s (2009) formula for Merton’s adjustment, and presents some example empirical estimates of the adjustment for some US stocks.

The adjustment estimates are material for many example stocks, particularly volatile stocks with a low percentage of shares held by institutional funds. However, the adjustment estimates are modest for many other stocks, including some smaller cap. stocks.

Measuring the model’s inputs requires using some judgment, particularly regarding the investor ownership variable. The paper will hopefully help stimulate useful empirical research on adjustment estimates and on best practices for applying the model.

The paper may encourage more use of the incomplete-information adjustment in practice, which should lead to improved discount rate estimates in valuation analyses.

No other “bridge the gap” coverage of the incomplete-information adjustment is available in textbooks or the applied literature.

Purpose – This chapter investigates vulnerability to poverty in the Republic of Haiti.

Methodology – We use a hierarchical modeling technique to allow the assessment and decomposition of vulnerability to poverty by exploiting the short-panel structure of nested data in a cross section.

Originality – Specifically, a three-level hierarchical model with a partially Bayesian restricted maximum likelihood is used in the estimation procedure. This is novel in this literature.

Findings – The decomposition method adopted in this chapter reveals that vulnerability in the Republic of Haiti is largely a rural phenomenon and is correlated negatively with schooling. The results also disclose the lack of equality in various aspects of circumstances or opportunities, including education, as the salient factor determining the status and level of vulnerability of households. Most importantly, among the different shocks affecting household's income, it is found that meso-level shocks are in general far more important than covariate shocks. This finding points to some interesting policy implications in terms of decentralizing policies and delegating more powers and providing better means to local governments to enhance household resilience to shocks and to alleviate their vulnerability to poverty.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.

The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.

This paper relates to the literature on central bank (CB) transparency and inflation uncertainty. Considering that opacity is a possible source for inflation uncertainty the purpose of this paper is to test the hypothesis that increase in the dispersion of the degree of CB opacity generates higher levels of inflation uncertainty.

In a first step, the authors present a theoretical model that shows how increase in the dispersion of the degree of CB opacity creates higher levels of inflation uncertainty. In a second step, the authors test the assumption that increase in the dispersion of the degree of CB opacity generates higher levels of inflation uncertainty in the Brazilian economy.

The findings denote that CB transparency is an important tool for guiding public expectations and thus contributes to avoiding the uncertainty caused by CB preferences.

This paper extends the theoretical model presented by de Mendonça and Simão Filho (2007) by the theoretical link between the forecast error and opacity. Furthermore, because the theoretical underpinning relies on the CB guiding inflation expectations, the authors construct an uncertainty measure based on survey of forecasts where such expectations can be inferred through the variability in the forecast error.

The purpose of this paper is to take advantage of a natural experiment in Taiwan to test the effect of short‐sales constraints on price dynamics.

Since September 1998, short‐selling is banned at a price below the close price of the previous trading day. The new rule creates unique daily dynamics of short‐sales constraints. The paper employs a difference‐in‐difference method to evaluate whether the short‐sales constraint rule plays an important role in the price dynamics.

The results show that stock prices react to information in a way similar to if short‐selling was not banned. This is in line with the implication of a rational expectation framework like Diamond and Verrecchia.

The paper has implications on the short selling bans in the 2008/2009 credit crisis and the European debt crisis because the bans are public information as those in this setting. The rational agents in the market could incorporate the bans into price beliefs which could lead to the ineffectiveness of the policy. The short‐sales constraints may be widely imposed in the crisis but they are not the effective tools to alleviate downward price pressures.

The results suggest that the effort of the government to boost stock price by imposing short sales constraints will not be effective if rational investors take the constraints into account while forming their beliefs.

Unlike existing short‐sales constraint proxies like short interest or lending fees, the dynamic constraints do not suffer from endogeneity. Moreover, the constraints are public information and thus ideal for testing the rational expectation models, in which investors have to be aware of the level of the constraints.