This paper proposes a model-free approach to extracting option-implied equity premia, and empirically examines the information content of these risk premia for forecasting the stock market return. Our approach does not require specifying the functional form of the pricing kernel, and only assumes the existence of put and call options which complete the market. We show that the equity premium can be inferred from expected excess returns on a portfolio of options. An empirical investigation of S&P 500 index options yields the following conclusions: (i) the implied equity premium is consistently positive; (ii) the implied equity premium predicts stock market returns; (iii) the implied equity premium consistently outperforms variables commonly used in the forecasting literature both in- and out-of-sample; (iv) at the cross-sectional level, stocks that are more sensitive to the implied equity premium have higher returns on average.

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A financial crisis originating in one country can travel within and beyond its original neighbourhood spreading among countries like a contagious disease. This paper proposes a framework for modelling financial contagion that is based on SIR (Susceptible-Infected-Recovered) transmission models from epidemic theory. It addresses two important features of contagion modelling which are a common shortcoming of most existing empirical approaches, namely the direct modelling of the inherent dependencies involved in the transmission mechanism, and an associated canonical measure of crisis severity. The proposed methodology naturally implies a control mechanism, which is required when evaluating prospective immunisation policies that intend to mitigate the impact of a crisis. It can be implemented not only ex-post, as a way of learning from past experiences, but also at the onset of a contagious financial crisis. The approach is illustrated using both historical final outcome and temporal data on a number of currency crisis episodes. The latter requires the introduction of a novel hierarchical model, termed the Hidden Epidemic Model (HEM), which embeds the stochastic financial epidemic as a latent process. The empirical results suggest an increasing trend for global transmission of currency crises over time.

This paper explores the various shapes the recoveries may exhibit within a Markov-Switching model. It relies on the bounce-back effects first analyzed by Kim, Morley and Piger (2005) and extends the methodology by proposing i) a more flexible bounce-back model, ii) explicit tests to select the appropriate bounce-back function, if any, and iii) a suitable measure of the permanent impact of recessions. This approach is then applied to post-WWII quarterly growth rates of US and UK real GDPs.

In several application fields, from social to behavioural sciences, from environmental sciences to marketing, informations are gathered and coded in several categorical attributes. A well-known exploratory method to describe and visualize this type of data is multiple correspondence analysis (MCA) (Greenacre, 2007). MCA is the generalization of correspondence analysis (CA) to more than two categorical variables. MCA aims to identify a reduced set of synthetic dimensions maximizing the explained variability of the categorical data set in question. The advantages in using MCA to study associations of categorical data are then to obtain a simplified representation of the multiple associations characterizing attributes as well as to remove noise and redundancies in data. The MCA implementation consists of a eigenvalue decomposition (EVD) or the related singular value decomposition (SVD) of properly transformed data. The applicability of MCA on very large data sets or on categorical data streams is limited due to the required EVD (or SVD). The application of EVD and SVD to large and high-dimensional data is unfeasible because of the high computational costs and because of the fact that the whole data structures being decomposed have to be kept in memory. The latter aspect makes impossible to apply MCA in case of categorical data flows. The aim of the present contribution is to propose an MCA-like procedure that can be modified incrementally as new data batches are processed. In particular, the procedure is obtained by integrating a properly modified MCA with an EVD-based approach (Hall et al., 2002) in order to obtain updates and down-dates of the MCA-like solution. Updates will take into account the new data batches analyzed, down-dates will discard older data batches in order to refresh the solution. The low-dimensional quantification and visualization of categorical attributes via this MCA-like procedure is a promising approach to investigate the association structures and for fast clustering purposes. 

Canonical Analysis of Distance (AoD) extends Canonical Variate Analysis (CVA) to cope with a wide class of distance functions. In this presentation attention  is restricted to Euclidean embeddable distances that are also  additive e.g. Pythagorean distance, Clark’s distance, Chi-squared distance and the extended matching coefficient (EMC). It will be shown how canonical AoD deals with both continuous variables and categorical variables.  Extending the procedures for dealing with mixed variables is straight forward.
A key feature of canonical AoD is the analysis of the grouping structure of the data in a space of dimension not larger than K–1 where K denotes the number of groups. Biplot visualisations (predictive and interpolative) can be made showing: points representing the group centroids; points and/or regions representing within group variation; linear or nonlinear trajectories for quantitative variables and Category Level Points (CLPs) for categorical variables as well as prediction regions for different categories. Generalised biplots give nonlinear biplot trajectories for continuous variables and CLPs for categorical variables.  These CLPs have interesting properties, analogous to those associated with Multiple Correspondence Analysis (MCA), which is a special case. Canonical AoD extends these properties to the analysis of grouped data leading to a technique that may be termed categorical CVA. 

This paper reconsiders sequential Monte Carlo approaches to Bayesian inference in the light of massively parallel desktop computing capabilities now well within the reach of individual academics. It first develops an algorithm that is well suited to parallel computing in general and for which convergence results have been established in the sequential Monte Carlo literature but that tends to require manual tuning in practical application. It then introduces endogenous adaptations in the algorithm that obviate the need for tuning, using a new approach based on the structure of parallel computing to show that convergence properties are preserved and to provide reliable assessment of simulation error in the approximation of posterior moments. The algorithm is generic, requiring only code for simulation from the prior distribution and evaluation of the prior and data densities, thereby shortening development cycles for new models. Through its use of data point tempering it is robust to irregular posteriors, including multimodal distributions. The sequential structure of the algorithm leads to reliable and generic computation of marginal likelihood as a by-product. The paper includes three detailed examples taken from state-of-the-art substantive research applications. These examples illustrate the many desirable properties of the algorithm.

In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman fi…lter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.

Factor models have become useful tools for studying international business cycles. Block factor models [e.g., Kose, Otrok, and Whiteman (2003)] can be especially useful as the zero restrictions on the loadings of some factors may provide some economic interpretation of the factors. These models, however, require the econometrician to predefine the blocks, leading to potential misspecification. In Monte Carlo experiments, we show that even small misspecification can lead to substantial declines in fit. We propose an alternative model in which the blocks are chosen endogenously. The model is estimated in a Bayesian framework using a hierarchical prior, which allows us to incorporate series-level covariates that may influence and explain how the series are grouped. Using similar international business cycle data as Kose, Otrok, and Whiteman, we find our country clusters differ in important ways from those identified by geography alone. In particular, we find that similarities in institutions (e.g., legal systems, language diversity) may be just as important as physical proximity for analyzing business cycle comovements.

This paper gives a new jackknife estimator for instrumental variable inference with unknown heteroskedasticity. The estimator is derived by using a method of moments approach similar to the one that produces LIML in case of homoskedasticity. The estimator is symmetric in the endogenous variables including the dependent variable. Many instruments and many weak instruments asymptotic distributions are derived using high-level assumptions that allow for the simultaneous presence of weak and strong instruments for different explanatory variables. Standard errors are formulated compactly. We review briefly known estimators and show in particular that the symmetric jackknife estimator performs well when compared to the HLIM and HFUL estimators of Hausman et al. (2011) in Monte Carlo experiments.

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