cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6088058
title: Testing Nowcast Monotonicity with Estimated Factors

description: This article proposes a test to determine whether “big data” nowcasting methods, which have become an important tool to many public and private institutions, are monotonically improving as new information becomes available. The test is the first to formalize existing evaluation procedures from the nowcasting literature. We place particular emphasis on models involving estimated factors, since factor-based methods are a leading case in the high-dimensional empirical nowcasting literature, although our test is still applicable to small-dimensional set-ups like bridge equations and MIDAS models. Our approach extends a recent methodology for testing many moment inequalities to the case of nowcast monotonicity testing, which allows the number of inequalities to grow with the sample size. We provide results showing the conditions under which both parameter estimation error and factor estimation error can be accommodated in this high-dimensional setting when using the pseudo out-of-sample approach. The finite sample performance of our test is illustrated using a wide range of Monte Carlo simulations, and we conclude with an empirical application of nowcasting U.S. real gross domestic product (GDP) growth and five GDP sub-components. Our test results confirm monotonicity for all but one sub-component (government spending), suggesting that the factor-augmented model may be misspecified for this GDP constituent. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6088073.v2
title: Identifying Demand Shocks From Production Data

description: Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case, the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method’s performance and sensitivity. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6239006
title: Mixed Marginal Copula Modeling

description: This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We do so by carefully defining the likelihood as the density of the observations with respect to a mixed measure. The treatment is quite general, although we focus on mixtures of Gaussian and Archimedean copulas. The inference is Bayesian with the estimation carried out by Markov chain Monte Carlo. We illustrate the methodology and algorithms by applying them to estimate a multivariate income dynamics model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6295001
title: Conditional Extremes in Asymmetric Financial Markets

description: The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.
DOI: 10.6084/m9.figshare.6295001.v3
title: Conditional Extremes in Asymmetric Financial Markets

description: The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.
DOI: 10.6084/m9.figshare.6088058.v3
title: Testing Nowcast Monotonicity with Estimated Factors

description: This article proposes a test to determine whether “big data” nowcasting methods, which have become an important tool to many public and private institutions, are monotonically improving as new information becomes available. The test is the first to formalize existing evaluation procedures from the nowcasting literature. We place particular emphasis on models involving estimated factors, since factor-based methods are a leading case in the high-dimensional empirical nowcasting literature, although our test is still applicable to small-dimensional set-ups like bridge equations and MIDAS models. Our approach extends a recent methodology for testing many moment inequalities to the case of nowcast monotonicity testing, which allows the number of inequalities to grow with the sample size. We provide results showing the conditions under which both parameter estimation error and factor estimation error can be accommodated in this high-dimensional setting when using the pseudo out-of-sample approach. The finite sample performance of our test is illustrated using a wide range of Monte Carlo simulations, and we conclude with an empirical application of nowcasting U.S. real gross domestic product (GDP) growth and five GDP sub-components. Our test results confirm monotonicity for all but one sub-component (government spending), suggesting that the factor-augmented model may be misspecified for this GDP constituent. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6239006.v1
title: Mixed Marginal Copula Modeling

description: This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We do so by carefully defining the likelihood as the density of the observations with respect to a mixed measure. The treatment is quite general, although we focus on mixtures of Gaussian and Archimedean copulas. The inference is Bayesian with the estimation carried out by Markov chain Monte Carlo. We illustrate the methodology and algorithms by applying them to estimate a multivariate income dynamics model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6163556.v2
title: Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

description: Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can <i>drastically</i> improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6088058.v2
title: Testing Nowcast Monotonicity with Estimated Factors

description: This article proposes a test to determine whether “big data” nowcasting methods, which have become an important tool to many public and private institutions, are monotonically improving as new information becomes available. The test is the first to formalize existing evaluation procedures from the nowcasting literature. We place particular emphasis on models involving estimated factors, since factor-based methods are a leading case in the high-dimensional empirical nowcasting literature, although our test is still applicable to small-dimensional set-ups like bridge equations and MIDAS models. Our approach extends a recent methodology for testing many moment inequalities to the case of nowcast monotonicity testing, which allows the number of inequalities to grow with the sample size. We provide results showing the conditions under which both parameter estimation error and factor estimation error can be accommodated in this high-dimensional setting when using the pseudo out-of-sample approach. The finite sample performance of our test is illustrated using a wide range of Monte Carlo simulations, and we conclude with an empirical application of nowcasting U.S. real gross domestic product (GDP) growth and five GDP sub-components. Our test results confirm monotonicity for all but one sub-component (government spending), suggesting that the factor-augmented model may be misspecified for this GDP constituent. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6295001.v2
title: Conditional Extremes in Asymmetric Financial Markets

description: The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.
DOI: 10.6084/m9.figshare.6453074
title: Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets

description: For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model is better than CAPM in explaining the mean-variance efficiency of both the Chinese and U.S. stock markets. Furthermore, our results suggest that the U.S. stock market is more efficient in terms of mean-variance efficiency than the Chinese stock market. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6453074.v3
title: Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets

description: For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model is better than CAPM in explaining the mean-variance efficiency of both the Chinese and U.S. stock markets. Furthermore, our results suggest that the U.S. stock market is more efficient in terms of mean-variance efficiency than the Chinese stock market. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6453074.v2
title: Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets

description: For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model is better than CAPM in explaining the mean-variance efficiency of both the Chinese and U.S. stock markets. Furthermore, our results suggest that the U.S. stock market is more efficient in terms of mean-variance efficiency than the Chinese stock market. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6453080
title: Testing for an Omitted Multiplicative Long-Term Component in GARCH Models

description: We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte Carlo simulation. Our test also covers the mixed-frequency case in which the returns are observed at a higher frequency than the explanatory variable. The usefulness of our procedure is illustrated by empirical applications to S&P 500 return data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6453080.v2
title: Testing for an Omitted Multiplicative Long-Term Component in GARCH Models

description: We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte Carlo simulation. Our test also covers the mixed-frequency case in which the returns are observed at a higher frequency than the explanatory variable. The usefulness of our procedure is illustrated by empirical applications to S&P 500 return data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6453080.v1
title: Testing for an Omitted Multiplicative Long-Term Component in GARCH Models

description: We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte-Carlo simulation. Our test also covers the mixed-frequency case in which the returns are observed at a higher frequency than the explanatory variable. The usefulness of our procedure is illustrated by empirical applications to S&P 500 return data.
DOI: 10.6084/m9.figshare.6938489
title: Learning and Index Option Returns

description: Little is known about the economic sources that may generate the abnormal returns observed in put index options. We show that the learning process followed by investors may be one such source. We develop an equilibrium model under partial information in which a rational Bayesian learner prices put option contracts. Our model generates put option returns similar to the empirical returns of S&P 500 put index options. This result is not obtained when we analyze alternative setups of the model in which no learning process exists.
DOI: 10.6084/m9.figshare.7268627
title: Flexible Mixture-Amount Models Using Multivariate Gaussian Processes

description: Many products and services can be described as mixtures of components whose proportions sum to one. Specialized models have been developed for relating the mixture component proportions to response variables, such as the preference, quality, and liking of products. If only the mixture component proportions affect the response variable, mixture models suffice to analyze the data. In case the total amount of the mixture also affects the response variable, mixture<i>-amount</i> models are needed. The current strategy for mixture-amount models is to express the response in terms of the mixture component proportions and subsequently specify the corresponding parameters as parametric functions of the amount. Specifying the functional form for these parameters may not be straightforward, and using a flexible functional form usually comes at the cost of a large number of parameters. In this article, we present a new modeling approach that is flexible, but parsimonious in the number of parameters. This new approach uses multivariate Gaussian processes and avoids the necessity to a priori specify the nature of the dependence of the mixture model parameters on the amount of the mixture. We show that this model encompasses two commonly used model specifications as extreme cases. We consider two applications and demonstrate that the new model outperforms standard models for mixture-amount data.
DOI: 10.6084/m9.figshare.6938492.v1
title: Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models

description: We propose semi-parametric CUSUM tests to detect a change point in the correlation structures of non–linear multivariate models with dynamically evolving volatilities. The asymptotic distributions of the proposed statistics are derived under mild conditions. We discuss the applicability of our method to the most often used models, including constant conditional correlation (CCC), dynamic conditional correlation (DCC), BEKK, corrected DCC and factor models. Our simulations show that, our tests have good size and power properties. Also, even though the near–unit root property distorts the size and power of tests, de–volatizing the data by means of appropriate multivariate volatility models can correct such distortions. We apply the semi–parametric CUSUM tests in the attempt to date the occurrence of financial contagion from the U.S. to emerging markets worldwide during the great recession.
DOI: 10.6084/m9.figshare.6938489.v2
title: Learning and Index Option Returns

description: Little is known about the economic sources that may generate the abnormal returns observed in put index options. We show that the learning process followed by investors may be one such source. We develop an equilibrium model under partial information in which a rational Bayesian learner prices put option contracts. Our model generates put option returns similar to the empirical returns of S&P 500 put index options. This result is not obtained when we analyze alternative setups of the model in which no learning process exists.
DOI: 10.6084/m9.figshare.6938489.v1
title: Learning and Index Option Returns

description: Little is known about the economic sources that may generate the abnormal returns observed in put index options. We show that the learning process followed by investors may be one such source. We develop an equilibrium model under partial information in which a rational Bayesian learner prices put option contracts. Our model generates put option returns similar to the empirical returns of S&P 500 put index options. This result is not obtained when we analyze alternative setups of the model in which no learning process exists.
DOI: 10.6084/m9.figshare.7268627.v2
title: Flexible Mixture-Amount Models Using Multivariate Gaussian Processes

description: Many products and services can be described as mixtures of components whose proportions sum to one. Specialized models have been developed for relating the mixture component proportions to response variables, such as the preference, quality, and liking of products. If only the mixture component proportions affect the response variable, mixture models suffice to analyze the data. In case the total amount of the mixture also affects the response variable, mixture<i>-amount</i> models are needed. The current strategy for mixture-amount models is to express the response in terms of the mixture component proportions and subsequently specify the corresponding parameters as parametric functions of the amount. Specifying the functional form for these parameters may not be straightforward, and using a flexible functional form usually comes at the cost of a large number of parameters. In this article, we present a new modeling approach that is flexible, but parsimonious in the number of parameters. This new approach uses multivariate Gaussian processes and avoids the necessity to a priori specify the nature of the dependence of the mixture model parameters on the amount of the mixture. We show that this model encompasses two commonly used model specifications as extreme cases. We consider two applications and demonstrate that the new model outperforms standard models for mixture-amount data.