Conditional Dependence Structure between Oil, Gold and USD Exchange Rates: Nested Copula Based GARCH Model

2017 ◽  
Author(s):  
Sana Braiek ◽  
Rihab Bedoui
Keyword(s):  
Exchange Rates ◽  
Garch Model ◽  
Dependence Structure ◽  
Conditional Dependence

On the study of conditional dependence structure between oil, gold and USD exchange rates

2018 ◽  
Vol 59 ◽  
pp. 134-146 ◽  
Author(s):  
Rihab Bedoui ◽  
Sana Braeik ◽  
Stéphane Goutte ◽  
Khaled Guesmi
Keyword(s):  
Exchange Rates ◽  
Dependence Structure ◽  
Conditional Dependence

scholarly journals Conditional Dependence between Oil Prices and Exchange Rates in BRICS Countries: An Application of the Copula-GARCH Model

2019 ◽  
Vol 12 (2) ◽  
pp. 99
Author(s):  
Yijin He ◽  
Shigeyuki Hamori
Keyword(s):  
Exchange Rates ◽  
Value At Risk ◽  
Tail Dependence ◽  
Oil Prices ◽  
Dependence Structure ◽  
Copula Models ◽  
Conditional Dependence ◽  
Brics Countries ◽  
T Copula ◽  
Student’S T

We studied the dependence structure between West Texas Intermediate (WTI) oil prices and the exchange rates of BRICS1 countries, using copula models. We used the Normal, Plackett, rotated-Gumbel, and Student’s t copulas to measure the constant dependence, and we captured the dynamic dependence using the Generalized Autoregressive Score with the Student’s t copula. We found that negative dependence and significant tail dependence exist in all pairs considered. The Russian Ruble (RUB)–WTI pair has the strongest dependence. Moreover, we treated five exchange rate–oil pairs as portfolios and evaluated the Value at Risk and Expected Shortfall from the time-varying copula models. We found that both reach low values when the oil price falls sharply.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

scholarly journals Copula-Based Assessment of Co-Movement and Tail Dependence Structure Among Major Trading Foreign Currencies in Ghana

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 55
Author(s):  
Prince Osei Mensah ◽  
Anokye M. Adam
Keyword(s):  
Exchange Rates ◽  
Foreign Currency ◽  
Tail Dependence ◽  
Time Range ◽  
Dependence Structure ◽  
Joint Movement ◽  
Positive Dependence ◽  
Study Results ◽  
Foreign Currencies ◽  
Dependence Measures

This paper examines the joint movement and tail dependence structure between the pair of foreign exchange rates (EUR, USD and GBP) against the GHS, using daily exchange rates data expressed in GHS per unit of foreign currencies (EUR, USD and GBP) between the time range of 24 February 2009 and 19 December 2019. We use different sets of both static (time-invariant) and time-varying copulas with different levels of dependence and tail dependence measures, and the study results reveal positive dependence between all exchange rates pairs, though the dependencies for EUR-USD and GBP-USD pairs are not as strong as the EUR-GBP pair. The findings also reveal symmetric tail dependence, and dependence evolves over time. Notwithstanding this, the asymmetric tail dependence copulas provide evidence of upper tail dependence. We compare the copula results to DCC(1,1)-GARCH(1,1) model result and find the copula to be more sensitive to extreme co-movement between the currency pairs. The afore-mentioned findings, therefore, offer forex market players the opportunity to relax in hoarding a particular foreign currency in anticipation of domestic currency depreciation.

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