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Working Paper
International Stock Comovements with Endogenous Clusters
We examine international stock return comovements of country-industry portfolios. Our model allows comovements to be driven by a global and a cluster component, with the cluster membership endogenously determined. Results indicate that country-industry portfolios tend to cluster mainly within geographical areas that can include one or more countries. The cluster compositions substantially changed over time, with the emergence of clusters among European countries from the early 2000s. The cluster component was the main driver of country-industry portfolio returns for most of the sample, except ...
Working Paper
Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models
We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead-lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time - nowcasting - since inference can be conducted in presence of mixed frequency ...
Working Paper
Common Factors, Trends, and Cycles in Large Datasets
This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross ...
Working Paper
Surprise and uncertainty indexes: real-time aggregation of real-activity macro surprises
I construct two daily, real-time, real activity indexes for the United States, Euro area, the United Kingdom, Canada, and Japan: (i) a surprise index that summarizes recent economic data surprises and measures optimism/pessimism about the state of the economy, and (ii) an uncertainty index that measures uncertainty related to the state of the economy. The surprise index preserves the properties of the underlying series in affecting asset prices, with the advantage of being a parsimonious summary measure of real-activity surprises. For the United States, the real-activity uncertainty index is ...
Working Paper
Tests of Equal Accuracy for Nested Models with Estimated Factors
In this paper we develop asymptotics for tests of equal predictive ability between nested models when factor-augmented regression models are used to forecast. We provide conditions under which the estimation of the factors does not affect the asymptotic distributions developed in Clark and McCracken (2001) and McCracken (2007). This enables researchers to use the existing tabulated critical values when conducting inference. As an intermediate result, we derive the asymptotic properties of the principal components estimator over recursive windows. We provide simulation evidence on the finite ...
Working Paper
Linear Factor Models and the Estimation of Expected Returns
This paper analyzes the properties of expected return estimators on individual assets implied by the linear factor models of asset pricing, i.e., the product of β and λ. We provide the asymptotic properties of factor--model--based expected return estimators, which yield the standard errors for risk premium estimators for individual assets. We show that using factor-model-based risk premium estimates leads to sizable precision gains compared to using historical averages. Finally, inference about expected returns does not suffer from a small--beta bias when factors are traded. The more ...
Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade
We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, mobility, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel ...
Working Paper
Dynamic Factor Copula Models with Estimated Cluster Assignments
This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model ...
Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade
We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau’s Monthly Retail Trade Survey (MRTS). The index’s weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate implications of policies implemented during the Covid-19 pandemic. To construct the index, we extract the co-movement in several weekly data series capturing credit & debit card transactions and revenues, mobility, and consumer sentiment as well as monthly retail and food services sales excluding automotive spending (ex. autos) from the ...
Working Paper
Factor Models with Local Factors—Determining the Number of Relevant Factors
We extend the theory on factor models by incorporating “local” factors into the model. Local factors affect only an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. We de-rive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. We then introduce a new class of estimators to determine the number of those relevant factors. Un-like existing estimators, our estimators use not only ...