Correlations and risk contagion between mixed assets and mixed-asset portfolio VaR measurements in a dynamic view: An application based on time varying copula models

2016 ◽  
Vol 444 ◽  
pp. 940-953 ◽  
Author(s):  
Yingying Han ◽  
Pu Gong ◽  
Xiang Zhou
Keyword(s):  
Time Varying ◽  
Copula Models ◽  
Dynamic View ◽  
Asset Portfolio

Household margin insurance of agricultural sector in Indonesia using a farmer exchange rate index

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Atina Ahdika ◽  
Dedi Rosadi ◽  
Adhitya Ronnie Effendie ◽  
Gunardi
Keyword(s):  
Exchange Rate ◽  
Value At Risk ◽  
Agricultural Sector ◽  
Time Varying ◽  
Risk Modeling ◽  
Copula Models ◽  
Content Type ◽  
Premium Calculation ◽  
Index Ratio ◽  
Margin Insurance

PurposeFarmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.Design/methodology/approachThis research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.FindingsFirst, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.Originality/valueThis research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

scholarly journals Modeling indemnity of revenue-based crop insurance in Indonesia using time-varying copula models

2019 ◽  
Author(s):  
Atina Ahdika ◽  
Dedi Rosadi ◽  
Adhitya Ronnie Effendie ◽  
Gunardi
Keyword(s):  
Crop Insurance ◽  
Time Varying ◽  
Copula Models

scholarly journals On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1859
Author(s):  
Jong-Min Kim ◽  
Seong-Tae Kim ◽  
Sangjin Kim
Keyword(s):  
Gold Price ◽  
Gaussian Copula ◽  
Time Varying ◽  
Conditional Correlation ◽  
Copula Models ◽  
Marginal Regression ◽  
High Volatility ◽  
Asymmetric Garch ◽  
Relationship Of ◽  
The Relationship

This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.

scholarly journals The Time-Varying Nature of Reits

2018 ◽  
Vol 26 (1) ◽  
pp. 26-38
Author(s):  
Bing Zhu
Keyword(s):  
Real Estate ◽  
Time Varying ◽  
Property Market ◽  
True Nature ◽  
Optimal Weights ◽  
Long Run ◽  
Asset Portfolio ◽  
Asset Class ◽  
Informational Environment

Abstract This paper investigates changes in the nature of REITs by estimating the time-varying long-run relationship among securitized real estate, direct real estate, and stock performance. The informational environment of U.S. REITs has matured gradually since their introduction. As more information on this asset class has become available, the “true” nature of REITs has thus become more apparent. We find that the long-term elasticity of direct real estate total returns on REIT total returns has increased since 1980, and became significant at the beginning of the 1990s, while the elasticity of general equity total returns remained insignificant. During the 2000s, the underlying property market was able to predict nearly 30% of REIT variance in the long term. Consequently, ignoring changes in the “nature” of REITs may lead to an underestimation of the influence from the underlying property market, and misspecification of the optimal weights in the long-term inter-asset portfolio.

scholarly journals Dynamic Networks in the Emotional Brain

2016 ◽  
Vol 23 (4) ◽  
pp. 383-396 ◽  
Author(s):  
Luiz Pessoa ◽  
Brenton McMenamin
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Dynamic Networks ◽  
Brain Regions ◽  
Time Varying ◽  
Brain Circuits ◽  
Mental Faculty ◽  
Dynamic View ◽  
The Impact ◽  
Perception Action

Research on the emotional brain has often focused on a few structures thought to be central to this type of processing—hypothalamus, amygdala, insula, and so on. Conceptual thinking about emotion has viewed this mental faculty as linked to broader brain circuits, too, including early ideas by Papez and others. In this article, we discuss research that embraces a distributed view of emotion circuits and efforts to unravel the impact on emotional manipulations on the processing of several large-scale brain networks that are chiefly important for mental operations traditionally labeled with terms such as “perception,” “action,” and “cognition.” Furthermore, we describe networks as dynamic processes and how emotion-laden stimuli strongly affect network structure. As networks are not static entities, their organization unfolds temporally, such that specific brain regions affiliate with them in a time-varying fashion. Thus, at a specific moment, brain regions participate more strongly in some networks than others. In this dynamic view of brain function, emotion has broad, distributed effects on processing in a manner that transcends traditional boundaries and inflexible labels, such as “emotion” and “cognition.” What matters is the coordinated action that supports behaviors.

scholarly journals A Comparison Study of Copula Models for European Financial Index Returns

2017 ◽  
Vol 9 (10) ◽  
pp. 155
Author(s):  
Paula V. Tofoli ◽  
Flavio A. Ziegelmann ◽  
Osvaldo Candido
Keyword(s):  
Subprime Crisis ◽  
Time Varying ◽  
Comparison Study ◽  
Markov Switching Model ◽  
Copula Models ◽  
New Approach ◽  
Switching Model ◽  
Long Period ◽  
Financial Index ◽  
Over Time

In this paper, we introduce a new approach to modeling dependence between international financial returns over time, combining time-varying copulas and the Markov switching model. We apply these copula models and also those proposed by Patton (2006), Jondeau and Rockinger (2006) and Silva Filho, Ziegelmann, and Dueker (2012) to the return data of the FTSE-100, CAC-40 and DAX indexes. We are particularly interested in comparing these methodologies in terms of the resulting dynamics of dependence and the models’ abilities to forecast possible capital losses. Because risks related to extreme events are important for risk management, we compare and select the models based on VaR forecasts. Interestingly, all the models identify a long period of high dependence between the returns beginning in 2007, when the subprime crisis was evolving. Surprisingly, the elliptical copulas perform best in forecasting the extreme quantiles of the portfolios returns.

Reserve modelling and the aggregation of risks using time varying copula models

2017 ◽  
Vol 67 ◽  
pp. 149-158 ◽  
Author(s):  
Sawssen Araichi ◽  
Christian de Peretti ◽  
Lotfi Belkacem
Keyword(s):  
Time Varying ◽  
Copula Models

Time-varying copula models in the shipping derivatives market

2016 ◽  
Vol 53 (3) ◽  
pp. 1039-1058 ◽  
Author(s):  
Wenming Shi ◽  
Kevin X. Li ◽  
Zhongzhi Yang ◽  
Ganggang Wang
Keyword(s):  
Time Varying ◽  
Copula Models
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