Copula-Based Factor Models for Multivariate Asset Returns

Иванов, Е. В.; Min, Aleksey; Ramsauer, Franz

doi:10.3390/econometrics5020020

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…ey mentioned that their model has the ability to capture the lower tail dependence of the SUN model. Ivanov et al [11] extended a copula approach to model the dependence of unobserved multivariate factors in the dynamic factor models. ey confirmed that the copula-based approach is more general and applicable to several factor models.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

2020

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We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure, thus, we apply the multivariate exchangeable copula function to relax this assumption. As there are many parameters to be estimated, we consider the Bayesian Markov chain Monte Carlo approach to estimate the parameter interests in the model. Four simulation studies are conducted to assess the performance of our proposed model and Bayesian estimation. Satisfactory results from simulation studies are obtained suggesting the good performance and reliability of the Bayesian method used in our proposed model. The real data analysis is also provided, and the empirical comparison indicates our proposed model outperforms the conventional models in all considered quantile levels.

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Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

2020

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“…In simpler terms, by having knowledge of only the marginal distributions of two random variables, it is theoretically possible to construct their joint distribution. Copulas have found widespread application in various fields, notably in finance [13][14][15][16][17] and hydrology [18][19][20][21][22][4][5].…”

Section: Copula For Hydroclimatic Applicationsmentioning

confidence: 99%

Copulas and hydro-economic models for assessing the impacts of climate change in irrigated agriculture

El ouadi,

Badaoui

2024

E3S Web Conf.

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Climate change significantly affects water resources through alterations in rainfall patterns, reduced rainfall quantities, rising temperatures, and prolonged drought cycles. Consequently, the escalating demand for water coupled with diminishing water availability is anticipated to lead to a critical water scarcity issue in the future unless proactive and innovative strategies are implemented.The impact of climate change on water resources is globally recognized as an increasingly vital concern, given its intricate interconnections with various sectors, notably agriculture, energy, and the provision of drinking water. Consequently, the scientific community has dedicated substantial efforts to devise optimal water resource management strategies in response to the challenges posed by climate change. Over the past four decades, hydro-economic models (HEM) have been instrumental in proposing solutions to adapt to evolving climatic conditions.This paper presents a newhydro-economic optimization model accounting for climatic uncertainties. The problem is formulated as a chance-constrained program in which the dependence structure between hydrologic and meteorologic variables is modeled using copula theory. The novelty of the approach lies in the capacity of the model to optimize the water resources, taking into account the dependence between agronomic, socio-economic, and hydrologic systems and climatic uncertainties.

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“…In a next step, an extension to heteroscedasticity or the incorporation of regime-switching concepts would be worthwhile. Finally, several ADFMs could be coupled by copulas to capture nonlinear inter-market dependencies similarly to Ivanov et al [49]. Acknowledgments: The authors want to thank the editor and two anonymous reviewers for their very helpful suggestions, which essentially contributed to an improved manuscript.…”

Section: Conclusion and Final Remarksmentioning

confidence: 99%

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Defend¹,

Min

Portelli³

et al. 2021

Forecasting

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This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

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Copula-Based Factor Models for Multivariate Asset Returns

Cited by 7 publications

References 45 publications

Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

Copulas and hydro-economic models for assessing the impacts of climate change in irrigated agriculture

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

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