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Econometrics, Volume 5, Issue 2 (June 2017)

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Research

Open AccessArticle Copula-Based Factor Models for Multivariate Asset Returns
Econometrics 2017, 5(2), 20; doi:10.3390/econometrics5020020
Received: 29 September 2016 / Revised: 3 May 2017 / Accepted: 3 May 2017 / Published: 17 May 2017
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Abstract
Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to
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Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to model the dependence of unobserved multivariate factors resulting from two dynamic factor models. However, the proposed methodology is general and applicable to several factor models as well as to other copula models for stationary multivariate time series. An empirical study illustrates the forecasting superiority of our approach for constructing an optimal portfolio of U.S. industrial stocks in the mean-variance framework. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting
Econometrics 2017, 5(2), 21; doi:10.3390/econometrics5020021
Received: 25 November 2016 / Revised: 26 April 2017 / Accepted: 8 May 2017 / Published: 23 May 2017
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Abstract
Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent
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Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use adaptive rejection Metropolis sampling (ARMS) within Gibbs sampling for posterior simulation: Gibbs sampling enables application to Bayesian problems, while ARMS is an adaptive strategy that replaces traditional Metropolis-Hastings updates, which typically require careful tuning. Our simulation study shows favorable performance of our proposed approach both in terms of sampling efficiency and accuracy. We provide an extensive application example using historical data on European financial stocks that forecasts portfolio Value at Risk (VaR) and Expected Shortfall (ES). Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity
Econometrics 2017, 5(2), 16; doi:10.3390/econometrics5020016
Received: 4 March 2016 / Revised: 3 March 2017 / Accepted: 5 April 2017 / Published: 12 April 2017
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Abstract
We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past values and conditional
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We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past values and conditional expectations of the variables can be simultaneously estimated. Results with realized volatility, volumes and number of trades of the JNJ stock show that significantly superior realized volatility forecasts are delivered with a fully interdependent vMEM relative to a single equation. Alternatives involving log–Normal or semiparametric formulations produce substantially equivalent results. Full article
(This article belongs to the Special Issue Financial High-Frequency Data)
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Open AccessArticle Selecting the Lag Length for the MGLS Unit Root Tests with Structural Change: A Warning Note for Practitioners Based on Simulations
Econometrics 2017, 5(2), 17; doi:10.3390/econometrics5020017
Received: 31 August 2016 / Revised: 31 March 2017 / Accepted: 4 April 2017 / Published: 16 April 2017
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Abstract
This is a simulation-based warning note for practitioners who use the MGLS unit root tests in the context of structural change using different selection lag length criteria. With T=100, we find severe oversize problems when using some
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This is a simulation-based warning note for practitioners who use the M G L S unit root tests in the context of structural change using different selection lag length criteria. With T = 100 , we find severe oversize problems when using some criteria, while other criteria produce an undersizing behavior. In view of this dilemma, we do not recommend using these tests. While such behavior tends to disappear when T = 250 , it is important to note that most empirical applications use smaller sample sizes such as T = 100 or T = 150 . The A D F G L S test does not present an oversizing or undersizing problem. The only disadvantage of the A D F G L S test arises in the presence of M A ( 1 ) negative correlation, in which case the M G L S tests are preferable, but in all other cases they are very undersized. When there is a break in the series, selecting the breakpoint using the Supremum method greatly improves the results relative to the Infimum method. Full article
(This article belongs to the Special Issue Unit Roots and Structural Breaks)
Open AccessArticle The Univariate Collapsing Method for Portfolio Optimization
Econometrics 2017, 5(2), 18; doi:10.3390/econometrics5020018
Received: 9 October 2016 / Revised: 4 February 2017 / Accepted: 17 February 2017 / Published: 5 May 2017
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Abstract
The univariate collapsing method (UCM) for portfolio optimization is based on obtaining the predictive mean and a risk measure such as variance or expected shortfall of the univariate pseudo-return series generated from a given set of portfolio weights and multivariate set of assets
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The univariate collapsing method (UCM) for portfolio optimization is based on obtaining the predictive mean and a risk measure such as variance or expected shortfall of the univariate pseudo-return series generated from a given set of portfolio weights and multivariate set of assets under interest and, via simulation or optimization, repeating this process until the desired portfolio weight vector is obtained. The UCM is well-known conceptually, straightforward to implement, and possesses several advantages over use of multivariate models, but, among other things, has been criticized for being too slow. As such, it does not play prominently in asset allocation and receives little attention in the academic literature. This paper proposes use of fast model estimation methods combined with new heuristics for sampling, based on easily-determined characteristics of the data, to accelerate and optimize the simulation search. An extensive empirical analysis confirms the viability of the method. Full article
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Open AccessArticle Maximum Likelihood Estimation of the I(2) Model under Linear Restrictions
Econometrics 2017, 5(2), 19; doi:10.3390/econometrics5020019
Received: 27 February 2017 / Revised: 2 May 2017 / Accepted: 8 May 2017 / Published: 15 May 2017
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Abstract
Estimation of the I(2) cointegrated vector autoregressive (CVAR) model is considered. Without further restrictions, estimation of the I(1) model is by reduced-rank regression (Anderson (1951)). Maximum likelihood estimation of I(2) models, on the other hand, always requires iteration. This paper presents a new
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Estimation of the I(2) cointegrated vector autoregressive (CVAR) model is considered. Without further restrictions, estimation of the I(1) model is by reduced-rank regression (Anderson (1951)). Maximum likelihood estimation of I(2) models, on the other hand, always requires iteration. This paper presents a new triangular representation of the I(2) model. This is the basis for a new estimation procedure of the unrestricted I(2) model, as well as the I(2) model with linear restrictions imposed. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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