Dissecting the idiosyncratic volatility anomaly

The idiosyncratic volatility (IVOL) anomaly, documented in Ang, et al. (2006), has garnered a great deal of attention in the literature. Yet questions remain regarding the robustness and pervasiveness of the IVOL anomaly, with a particular concern that the IVOL anomaly might simply be the manifestation of market microstructure effect. In this paper, we show that the IVOL anomaly is strong and pervasive after we exclude stocks most susceptible to market microstructure noise — such as microcap stocks, penny stocks, and stocks with strong short-term return reversal. These results are robust to equal-weighting or value-weighting stocks in the IVOL portfolios. Our findings suggest that rather than being the cause of the anomaly, market microstructure noise actually weakens the IVOL anomaly.     

‘Expected utility / subjective probability’ analysis without the sure-thing principle or probabilistic sophistication

Summary. The basic analytical concepts, tools and results of the classical expected utility/subjective probability model of risk preferences and beliefs under subjective uncertainty can be extended to general event-smooth preferences over subjective acts that do not necessarily satisfy either of the key behavioral assumptions of the classical model, namely the Sure-Thing Principle or the Hypothesis of Probabilistic Sophistication. This is accomplished by a technique analogous to that used by Machina (1982) and others to generalize expected utility analysis under objective uncertainty, combined with an event-theoretic approach to the classical model and the use of a special class of subjective events, acts and mixtures that exhibit almost-objective like properties. The classical expected utility/subjective probability characterizations of outcome monotonicity, outcome derivatives, probabilistic sophistication, comparative and relative subjective likelihood, and comparative risk aversion are all globally robustified to general event-smooth preferences over subjective acts.Received: 4 May 2004, Revised: 4 October 2004, JEL Classification Numbers: D81.This paper presents a considerably improved version of the concept of event-differentiability from Machina (1992). An alternative definition has been independently developed by Epstein (1999) in his analysis of the concept of uncertainty aversion. I am grateful to Kenneth Arrow, Mark Durst, Jürgen Eichberger, Daniel Ellsberg, Clive Granger, Simon Grant, Edi Karni, Peter Klibanoff, David Kreps, Duncan Luce, Robert Nau, Uzi Segal, Peter Wakker, Joel Watson and especially Larry Epstein, Ted Groves and Joel Sobel for helpful discussions and comments. This material is based upon work supported by the National Science Foundation under Grants No. 9209012 and 9870894.     

Understanding the drivers of Urban Development Agreements with the rough set approach and robust decision rules

The rise of neoliberalism in the context of urban development has encouraged cooperation between public and private parties. This cooperation is structured by contracts, generally called Urban Development Agreements (UDAs). Being part of the urban regeneration strategies, UDAs aim at achieving durable improvements of abandoned areas, namely brownfields, according to the sustainability principles. Thus, within the negotiation between private developers and public administrations, multiple and conflicting instances have to be faced case by case. Despite the uniqueness of each UDA, it is possible to define a set of pertinent characteristics that play a crucial role in determining the fairness and appropriateness of the public-private partnership. Given this context, we propose a novel variant of the Dominance Rough Set Approach (DRSA) for i) exploring the relationship between condition attributes or criteria and effects of urban development processes and for ii) supporting negotiations according to the detection of a set of relevant features. Specifically, DRSA is applied on a sample of UDAs recently concluded in the Lombardy Region (Northern Italy), and then tested on a sample of other UDAs under the negotiation phase. The analysis involves five dimensions represented by attributes and criteria related to urban, institutional, negotiation, development and economic contexts. The inferred decision rules provide useful knowledge for supporting complex decision processes such as the allocation of costs and benefits within UDAs.     

Four types of ignorance: Discussion

This is a comment on Hansen and Sargent (2015). Using a generalized one-period setup, I provide some intuition and perspective.     

Precautionary Principle and Robustness for a Stock Pollutant with Multiplicative Risk

The interest shown by policy makers and economists in the precautionary principle indicates the importance of model uncertainty in global warming policy. I show that through robust control, policy makers can implement the precautionary principle to regulate a stock pollutant, and I analyze its effect on expected steady state pollution taxes, stocks and welfare. The paper is broadly comprised of a theoretical part and an application to global warming policy. I find that: (1) an increase in either uncertainty about the model or risk about abatement cost increases expected steady state pollution taxes; (2) a robust policy is preferred for any level of model uncertainty and this preference increases for either higher model uncertainty or higher multiplicative risk and (3) the effect on expected steady state pollution taxes and stock of introducing model uncertainty is relatively small for high levels of model uncertainty. These results advocate using robust policies for a stock pollutant in the presence of model uncertainty.      

Robustness and the general dynamic factor model with infinite-dimensional space: Identification,estimation, and forecasting

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. As second-order models, however, they are sensitive to the presence of outliers—an issue that has not been analyzed so far in the general case of dynamic factors with possibly infinite-dimensional factor spaces (Forni et al. 2000, 2015, 2017). In this paper, we consider this robustness issue and study the impact of additive outliers on the identification, estimation, and forecasting performance of general dynamic factor models. Based on our findings, we propose robust versions of identification, estimation, and forecasting procedures. The finite-sample performance of our methods is evaluated via Monte Carlo experiments and successfully applied to a classical data set of 115 US macroeconomic and financial time series.     

CHOOSING AMONG ALTERNATIVE LONG‐RUN EVENT‐STUDY TECHNIQUES

This paper reviews the long‐run event‐study debate by outlining the strengths and weakness of the most commonly used alternative techniques. The fist part of the discussion highlights that prior literature has failed to provide a single risk‐adjusted model of long‐run abnormal returns with no biases. Subsequently, the paper provides guidance on how one can choose among pertinent alternative techniques. As a conclusion, researchers ought to choose among alternative techniques after considering issues such as (i) the nature of dataset and market of interest, (ii) the event type (regulatory or corporate), (iii) returns’ time‐interval, (iv) association of the event with accounting data, (v) sample characteristics and prior evidence regarding similar events, as well as (vi) risk changes following the event. Robustness tests are essential, while the road for further research regarding the appropriate technique(s) is open.     

Robustness of normal theory statistics in structural equation models*

A condition is given by which optimal normal theory methods, such as the maximum likelihood methods, are robust against violation of the normality assumption in a general linear structural equation model. Specifically, the estimators and the goodness of fit test are robust. The estimator is efficient within some defined class, and its standard errors can be obtained by a correction formula applied to the inverse of the information matrix. Some special models, like the factor analysis model and path models, are discussed in more detail. A method for evaluating the robustness condition is given.     

Statistical efficiency in the linear combination of forecasts

In seeking an efficient combination of forecasts which minimises the forecast error variance, many methods have been suggested. Through analysis, simulation and case studies, this paper seeks to develop insights into the statistical circumstances which influence the relative accuracy of six of these methods. The six methods chosen have all been advocated in various publications and consist of ‘equal weighting’ (i.e., pooled average), ‘optimal’ (i.e., error variance minimising), ‘optimal with independence assumption’ (i.e., error variance minimising assuming zero correlation between individual forecast errors) and three variations on the formulation of a Bayesian combination based upon posterior probabilities. The statistical circumstances reflected varying conditions of relative forecast errors, error correlations and outliers.