Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

Kaewsompong, Nachatchapong; Maneejuk, Paravee; Yamaka, Woraphon

doi:10.1155/2020/6746303

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…According to this experiment result, we can conclude that our proposed model is the robust model, and the incorrectly specified copula function will lead to the low accuracy of the model. Our finding is consistent with Kaewsompong et al (2020).…”

Section: Simulation Studysupporting

confidence: 94%

Structural and predictive analyses with a mixed copula‐based vector autoregression model

Yamaka

Gupta

Thongkairat

et al. 2022

Journal of Forecasting

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In this study, we introduce a mixed copula-based vector autoregressive (VAR) model for investigating the relationship between random variables. The one-step maximum likelihood estimation is used to obtain point estimates of the autoregressive parameters and mixed copula parameters. More specifically, we combine the likelihoods of the marginal and mixed Copula to construct the full likelihood function. The simulation study is used to confirm the accuracy of the estimation as well as the reliability of the proposed model. Various mixed copula forms from a combination of Gaussian, Student-t, Clayton, Frank, Gumbel, and Joe copulas are introduced. The proposed model is compared to the traditional VAR model and single copula-based VAR models to assess its performance. Furthermore, the real data study is also conducted to validate our proposed method. As a result, it is found that the one-step maximum likelihood provides accurate and reliable results. Also, we show that if we ignore the complex and nonlinear correlation between the errors, it causes significant efficiency loss in the parameter estimation, in terms of Bias and MSE. In the application study, the mixed copula-based VAR is the best fitting Copula for our application study.

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Section: Simulation Studysupporting

confidence: 94%

Structural and predictive analyses with a mixed copula‐based vector autoregression model

Yamaka

Gupta

Thongkairat

et al. 2022

Journal of Forecasting

Self Cite

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show abstract

“…Among them, the quantile causality test is a new methodology used to test the nonlinear causality between variables. Inspired by the idea of the rolling-window causality test developed by Kaewsompong et al [51], we propose a novel methodology that incorporates the rolling-window estimation and quantile causality test developed by Nishiyama et al [52], Jeong et al [53], and Balcilar et al [54,55].…”

Section: Methodsmentioning

confidence: 99%

Time‐ and Quantile‐Varying Causality between Investor Attention and Bitcoin Returns: A Rolling‐Window Causality‐in‐Quantiles Approach

Hang

Zhang

2021

Complexity

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This study proposes a novel approach that incorporates rolling-window estimation and a quantile causality test. Using this approach, Google Trends and Bitcoin price data are used to empirically investigate the time-varying quantile causality between investor attention and Bitcoin returns. The results show that the parameters of the causality tests are unstable during the sample period. The results also show strong evidence of quantile- and time-varying causality between investor attention and Bitcoin returns. Specifically, our results show that causality appears only in high volatility periods within the time domain, and causality presents various patterns across quantiles within the quantile domain.

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“…for a function φ(• | θ) with a single parameter, known as the generator function of the Archimedean copula Nelsen (2007), where φ −1 is its inverse function. Archimedean copulas have been used for both frequentist and Bayesian analyses in the literature (McNeil and Nešlehová, 2009;Genest et al, 2011;Kaewsompong et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Grid-Uniform Copulas and Rectangle Exchanges: Bayesian Model and Inference for a Rich Class of Copula Functions

Kuschinski¹,

Jara²

2021

Preprint

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Copula-based models provide a great deal of flexibility in modelling multivariate distributions, allowing for the specifications of models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. Choosing a class of copula models is not a trivial task and its misspecification can lead to wrong conclusions.We introduce a novel class of grid-uniform copula functions, which is dense in the space of all continuous copula functions in a Hellinger sense. We propose a Bayesian model based on this class and develop an automatic Markov chain Monte Carlo algorithm for exploring the corresponding posterior distribution. The methodology is illustrated by means of simulated data and compared to the main existing approach.

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Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

Cited by 6 publications

References 28 publications

Structural and predictive analyses with a mixed copula‐based vector autoregression model

Structural and predictive analyses with a mixed copula‐based vector autoregression model

Time‐ and Quantile‐Varying Causality between Investor Attention and Bitcoin Returns: A Rolling‐Window Causality‐in‐Quantiles Approach

Grid-Uniform Copulas and Rectangle Exchanges: Bayesian Model and Inference for a Rich Class of Copula Functions

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