Counterdiagonal/nonpositive tail dependence in Vine copula constructions: application to portfolio management

2020 ◽  
Author(s):  
Yuri Salazar Flores ◽  
Adán Díaz-Hernández
Keyword(s):  
Portfolio Management ◽  
Tail Dependence ◽  
Vine Copula

scholarly journals Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula

2021 ◽  
Vol 9 (2) ◽  
pp. 30
Author(s):  
John Weirstrass Muteba Mwamba ◽  
Sutene Mwambetania Mwambi
Keyword(s):  
South Africa ◽  
Crude Oil ◽  
South African ◽  
Asset Allocation ◽  
Markov Switching ◽  
Tail Dependence ◽  
Lower Tail ◽  
Oil Market ◽  
Oil Markets ◽  
Vine Copula

This paper investigates the dynamic tail dependence risk between BRICS economies and the world energy market, in the context of the COVID-19 financial crisis of 2020, in order to determine optimal investment decisions based on risk metrics. For this purpose, we employ a combination of novel statistical techniques, including Vector Autoregressive (VAR), Markov-switching GJR-GARCH, and vine copula methods. Using a data set consisting of daily stock and world crude oil prices, we find evidence of a structure break in the volatility process, consisting of high and low persistence volatility processes, with a high persistence in the probabilities of transition between lower and higher volatility regimes, as well as the presence of leverage effects. Furthermore, our results based on the C-vine copula confirm the existence of two types of tail dependence: symmetric tail dependence between South Africa and China, South Africa and Russia, and South Africa and India, and asymmetric lower tail dependence between South Africa and Brazil, and South Africa and crude oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using raw returns, MS GARCH returns, and C-vine and R-vine copula-based returns, and optimize it using a Particle Swarm optimization algorithm with a rebalancing strategy. The results demonstrate an inverse relationship between the risk contribution and asset allocation of South Africa and the crude oil market, supporting the existence of a lower tail dependence between them. This suggests that, when South African stocks are in distress, investors tend to shift their holdings in the oil market. Similar results are found between Russia and crude oil, as well as Brazil and crude oil. In the symmetric tail, South African asset allocation is found to have a well-diversified relationship with that of China, Russia, and India, suggesting that these three markets might be good investment destinations when things are not good in South Africa, and vice versa.

Portfolio management with tail dependence

2018 ◽  
Vol 50 (51) ◽  
pp. 5510-5520
Author(s):  
Daniel Reed Bergmann ◽  
Jose Roberto Ferreira Savoia ◽  
Claudio Felisoni de Angelo ◽  
Eduardo Augusto do Rosário Contani ◽  
Fabiana Lopes da Silva
Keyword(s):  
Portfolio Management ◽  
Tail Dependence

scholarly journals Examining the Interdependence between the Exchange Rates of China and ASEAN Countries: A Canonical Vine Copula Approach

2019 ◽  
Vol 11 (19) ◽  
pp. 5487 ◽  
Author(s):  
Liu ◽  
Wang ◽  
Sriboonchitta
Keyword(s):  
Exchange Rates ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Positive Dependence ◽  
Asymmetric Dependence ◽  
Before And After ◽  
Copula Approach ◽  
Asean Countries ◽  
Vine Copula ◽  
Vine Copulas

Based on the canonical vine (C-vine) copula approach, this paper examines the interdependence between the exchange rates of the Chinese Yuan (CNY) and the currencies of major Association of Southeast Asian Nations (ASEAN) countries. The differences in the dependence structure and degree between currencies before and after the Belt and Road (B&R) Initiative were compared in order to investigate the changing role of the Renminbi (RMB) in the ASEAN foreign exchange markets. The results indicate a positive dependence between the exchange rate returns of CNY and the currencies of ASEAN countries and show the rising power of RMB in the regional currency markets after the B&R Initiative was launched. Besides this, the Malaysian Ringgit proved to be most relevant to the other ASEAN currencies, thus playing an important role in the stability of regional financial markets. Moreover, evidence of tail dependence was found in the returns of three currency pairs after the B&R Initiative, which implies the presence of asymmetric dependence between exchange rates. The results from time-varying C-vine copulas further confirmed the robustness of the results from the static C-vine copulas.

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

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.

scholarly journals Assessing Market Risk in BRICS and Oil Markets: An application of Markov Switching and Vine Copula

2021 ◽  
Author(s):  
John Weirstrass Muteba Mwamba ◽  
Sutene M Mwambi
Keyword(s):  
South Africa ◽  
South African ◽  
Asset Allocation ◽  
Inverse Relationship ◽  
Markov Switching ◽  
Tail Dependence ◽  
Particle Swarm Algorithm ◽  
Lower Tail ◽  
Oil Market ◽  
Vine Copula

This paper investigates the dynamic tail dependence risk between BRICS economies and world energy market in the context of the COVID-19 financial crisis of 2020, to determine optimal investment decisions based on risk metrics. For this purpose, the study employs a combination of novel statistical techniques ranging from Markov Switching, GARCH and Vine copula. Using a dataset consisting of daily stock and world crude oil prices; we find high probability of transition between lower and higher volatility regimes. Furthermore, our results based on the C-Vine copula confirm the existence of two types of tail dependence: - symmetric tail dependence between South Africa and China; South Africa and Russia; and lower tail dependence between South Africa and India; South Africa and Brazil; South Africa and Oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using C-vine copula-based returns and optimize it using Particle Swarm algorithm with a rebalancing strategy. The results show an inverse relationship between the risk contribution and asset allocation of South Africa and oil market supporting the existence of lower tail dependence between them. This suggests that when South African stocks are in distress, investors tend to shift their holdings in oil market. Similar results are found between China and oil. In the upper tail, South African asset allocation is found to have an inverse relationship with that of Brazil, Russia and India suggesting that these three markets might be good investment destinations when things are not good in South Africa and vice-versa.

scholarly journals The tail dependence of the carbon markets: The implication of portfolio management

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0238033
Author(s):  
Fang Zhang ◽  
Zhengjun Zhang
Keyword(s):  
Portfolio Management ◽  
Tail Dependence ◽  
Carbon Markets

Time-Dependent Reliability Analysis Using a Vine-ARMA Load Model

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Stochastic Processes ◽  
Reliability Analysis ◽  
Arma Model ◽  
Tail Dependence ◽  
Time Dependent ◽  
Arma Models ◽  
Non Gaussian ◽  
Vine Copula ◽  
Stochastic Loads ◽  
Gaussian Stochastic Processes

A common strategy for the modeling of stochastic loads in time-dependent reliability analysis is to describe the loads as independent Gaussian stochastic processes. This assumption does not hold for many engineering applications. This paper proposes a Vine-autoregressive-moving average (Vine-ARMA) load model for time-dependent reliability analysis, in problems with a vector of correlated non-Gaussian stochastic loads. The marginal stochastic processes are modeled as univariate ARMA models. The correlations among different univariate ARMA models are captured using the Vine copula. The ARMA model maintains the correlation over time. The Vine copula represents not only the correlation among different ARMA models but also the tail dependence of different ARMA models. Therefore, the developed Vine-ARMA model can flexibly model a vector of high-dimensional correlated non-Gaussian stochastic processes with the consideration of tail dependence. Due to the complicated structure of the Vine-ARMA model, new challenges are introduced in time-dependent reliability analysis. In order to overcome these challenges, the Vine-ARMA model is integrated with a single-loop Kriging (SILK) surrogate modeling method. A hydrokinetic turbine blade subjected to a vector of correlated river flow loads is used to demonstrate the effectiveness of the proposed method.

Time-Dependent Reliability Analysis Using a New Multivariate Stochastic Load Model

2016 ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Stochastic Processes ◽  
Reliability Analysis ◽  
Arma Model ◽  
Tail Dependence ◽  
Time Dependent ◽  
Arma Models ◽  
Non Gaussian ◽  
Vine Copula ◽  
Stochastic Loads ◽  
Gaussian Stochastic Processes

A common strategy for the modeling of stochastic loads in time-dependent reliability analysis is to describe the loads as independent Gaussian stochastic processes. This assumption does not hold for many engineering applications. This paper proposes a Vine-autoregressive-moving average (Vine-ARMA) load model for time-dependent reliability analysis, in problems with a vector of correlated non-Gaussian stochastic loads. The marginal stochastic processes are modeled as univariate ARMA models. The correlations between different univariate ARMA models are captured using the Vine-copula. The ARMA model maintains the correlation over time. The Vine-copula represents not only the correlation between different ARMA models, but also the tail dependence of different ARMA models. The developed Vine-ARMA model therefore can flexibly model a vector of high-dimensional correlated non-Gaussian stochastic processes with the consideration of tail dependence. Due to the complicated structure of the Vine-ARMA model, new challenges are introduced in time-dependent reliability analysis. In order to overcome these challenges, the Vine-ARMA model is integrated with a recently developed single-loop Kriging (SILK) surrogate modeling method. A hydrokinetic turbine blade subjected to a vector of correlated river flow loads is used to demonstrate the effectiveness of the proposed method.

scholarly journals EXTREMAL DEPENDENCIES ON COMMODITY FUTURES MARKETS

2019 ◽  
Vol XXI (4) ◽  
pp. 163-171
Author(s):  
Małgorzata Just
Keyword(s):  
Portfolio Management ◽  
Extreme Values ◽  
Tail Dependence ◽  
Commodity Futures ◽  
Futures Contracts ◽  
Subprime Crisis ◽  
Rates Of Return ◽  
Commodity Futures Markets ◽  
Agricultural Futures ◽  
Heating Oil

The aim of this study was to assess dependencies between extreme rates of return from commodity futures contracts on selected markets in the years 2000-2018. In periods of upheavals and turbulences, in markets for investors and portfolio management, it is crucial to estimate the probability of risk factors simultaneously taking extreme values. The analyses were conducted on dependencies between extreme rates of return (asymptotic dependencies) on markets of futures contracts for energy, metals and agricultural products in the years 2000-2018, applying the Copula-ARMA-GARCH models and tail dependence coefficients. Relatively strong and permanent asymptotic dependencies were found for pairs of futures contracts for crude oil and heating oil, while either no such dependencies were observed or only appeared during the subprime crisis and assumed very low values for other energy pairs of futures contracts and pairs of agricultural futures contracts, in which at least one of the contracts was concluded for soft commodities.

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