Estimating standard errors in regular vine copula models

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

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Truncation Mixture R-Vine Copulas

10.20944/preprints202102.0458.v1 ◽

2021 ◽

Author(s):

Fadhah Alanazi

Keyword(s):

Simulation Study ◽

Real Data ◽

Computational Time ◽

Model Parameters ◽

Copula Models ◽

Truncation Level ◽

Truncation Method ◽

Tree Estimation ◽

Vine Copula ◽

Vine Copulas

Uncovering hidden mixture correlation among variables have been investigating in the literature using mixture R-vine copula models. These models are hierarchical in nature. They provides a huge flexibility for modelling multivariate data. As the dimensions increases, the number of the model parameters that need to be estimated is increased dramatically, which becomes along with huge computational times and efforts. This situation becomes even much more harder and complicated in the mixture Regular vine models. Incorporating truncation method with mixture Regular vine models will reduce the computation difficulty for the mixture based models. In this paper, tree-by-tree estimation mixture model is joined with the truncation method, in order to reduce the computational time and the number of the parameters that need to be estimated in the mixture vine copula models. A simulation study and a real data applications illustrated the performance of the method. In addition, the real data applications show the affect of the mixture components on the truncation level.

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Gold and Government Bonds as Safe-Haven Assets Against Stock Market Turbulence in China

SAGE Open ◽

10.1177/2158244021990655 ◽

2021 ◽

Vol 11 (1) ◽

pp. 215824402199065

Author(s):

Wei Huang ◽

Meng-Shiuh Chang

Keyword(s):

Stock Market ◽

Stock Exchange ◽

Composite Index ◽

Investment Strategy ◽

Chinese Stock Market ◽

Safe Haven ◽

Copula Models ◽

Government Bonds ◽

Market Turbulence ◽

Vine Copula

We examine whether gold and China’s government bonds are safe-haven assets against the turbulence of the Shanghai Stock Exchange Composite Index by employing vine copula models during the 2003 to 2015 period. We find that either bonds or gold can be a weak safe haven but only gold can be a strong safe haven. Our simultaneous analysis advises against a joint safe-haven strategy of gold and bonds, given the high- to low-tail correlation. This result highlights an investment strategy of using a single safe-haven asset against the Chinese stock market turbulences.

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Aggregation of underwriting risks in insurance industry of Iran using vine copula

Risk Governance and Control Financial Markets & Institutions ◽

10.22495/rgcv5i4c1art4 ◽

2015 ◽

Vol 5 (4) ◽

pp. 149-161

Author(s):

Mohammad Mirbagherijam ◽

Mohammad Nabi Shahiki Tash ◽

Gholamreza Zamanian ◽

Amir Safari

Keyword(s):

Standard Model ◽

Risk Measures ◽

Insurance Industry ◽

Tail Dependence ◽

Economic Capital ◽

Aggregation Process ◽

Copula Models ◽

Risk Aggregation ◽

Copula Method ◽

Vine Copula

In this paper, the underwriting risks of the insurance industry of Iran were aggregated using various vine copula classes and historical data of loss ratios which corresponds to each business line. The estimated economic capital (EC) for the entire insurance industry considerably varies across different risk measures and vine copula models. In addition, less than the risk-based capital (RBC) charge assessed based on the standard model of RN69 and amounted to 96,943,391 million of Iran Rials. Therefore, it was concluded that using the Vine copula method and allowing symmetry and tail dependence for pairs of business lines’ risks in the risk aggregation process leads to overestimation of the RBC risk charge, as compared to the estimated results of simple and linear aggregation methods of such standard model. Furthermore, the choice of dependency structure and risk measures have a paramount effect on the aggregate economic capital. Highlights: Estimated aggregated economic capital varies across different risk measures and vine copula models; Selecting the appropriate copula model is an important consideration in risk aggregation process; Using the Vine copula method in the risk aggregation leads to overestimation of the RBC risk charge; The estimated economic capital is less than RBC risk charge calculated under standard model of RN69.

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Vine Copula Models with GLM and Sparsity

SSRN Electronic Journal ◽

10.2139/ssrn.2912029 ◽

2017 ◽

Author(s):

Dezhao Han ◽

Ken Seng Tan ◽

Chengguo Weng

Keyword(s):

Copula Models ◽

Vine Copula

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A goodness-of-fit test for regular vine copula models

Econometric Reviews ◽

10.1080/07474938.2016.1222231 ◽

2016 ◽

Vol 38 (1) ◽

pp. 25-46 ◽

Cited By ~ 7

Author(s):

Ulf Schepsmeier

Keyword(s):

Goodness Of Fit ◽

Goodness Of Fit Test ◽

Copula Models ◽

Vine Copula

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Learning Vine Copula Models for Synthetic Data Generation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015049 ◽

2019 ◽

Vol 33 ◽

pp. 5049-5057

Author(s):

Yi Sun ◽

Alfredo Cuesta-Infante ◽

Kalyan Veeramachaneni

Keyword(s):

Structure Learning ◽

Synthetic Data ◽

Building Blocks ◽

Data Generation ◽

Learning Problem ◽

Copula Models ◽

Dimensional Dependence ◽

Synthetic Data Generation ◽

Real World Datasets ◽

Vine Copula

A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation. We use neural network to find the embeddings for the best possible vine model and generate a structure. Throughout experiments on synthetic and real-world datasets, we show that our proposed approach fits the data better in terms of loglikelihood. Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.

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Flexible dynamic vine copula models for multivariate time series data

Econometrics and Statistics ◽

10.1016/j.ecosta.2019.03.002 ◽

2019 ◽

Vol 12 ◽

pp. 181-197 ◽

Cited By ~ 3

Author(s):

Elif F. Acar ◽

Claudia Czado ◽

Martin Lysy

Keyword(s):

Time Series ◽

Time Series Data ◽

Multivariate Time Series ◽

Series Data ◽

Copula Models ◽

Vine Copula

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Sequential Truncation of R-Vine Copula Mixture Model for High-Dimensional Datasets

International Journal of Mathematics and Mathematical Sciences ◽

10.1155/2021/3214262 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Fadhah Amer Alanazi

Keyword(s):

Mixture Model ◽

Real Data ◽

Computational Time ◽

Model Parameters ◽

Copula Models ◽

Truncation Level ◽

Truncation Method ◽

Tree Estimation ◽

Vine Copula ◽

High Dimensional Datasets

Uncovering hidden mixture dependencies among variables has been investigated in the literature using mixture R-vine copula models. They provide considerable flexibility for modeling multivariate data. As the dimensions increase, the number of the model parameters that need to be estimated is increased dramatically, which comes along with massive computational times and efforts. This situation becomes even much more complex and complicated in the regular vine copula mixture models. Incorporating the truncation method with a mixture of regular vine models will reduce the computation difficulty for the mixture-based models. In this paper, the tree-by-tree estimation mixture model is joined with the truncation method to reduce computational time and the number of parameters that need to be estimated in the mixture vine copula models. A simulation study and real data applications illustrated the performance of the method. In addition, the real data applications show the effect of the mixture components on the truncation level.

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