scholarly journals The Conditional Dependence Structure between Precious Metals: A Copula-GARCH Approach

2015 ◽  
pp. 19-33 ◽  
Author(s):  
Stanisław Wanat ◽  
Monika Papież ◽  
Sławomir Śmiech
Keyword(s):  
Precious Metals ◽  
Dependence Structure ◽  
Conditional Dependence

scholarly journals GGMnonreg: Non-Regularized Gaussian Graphical Models in R

2021 ◽  
Author(s):  
Donald Ray Williams
Keyword(s):  
Graphical Models ◽  
Brain Connectivity ◽  
Dependence Structure ◽  
Gaussian Graphical Models ◽  
Graphical Modeling ◽  
Conditional Dependence ◽  
Common Task ◽  
The Social ◽  
Cortical Regions ◽  
Low Dimensional

Studying complex relations in multivariate datasets is a common task across the sciences. Cognitive neuroscientists model brain connectivity with the goal of unearthing functional and structural associations betweencortical regions. In clinical psychology, researchers wish to better understand the intri-cate web of symptom interrelations that underlie mental health disorders. To this end, graphical modeling has emerged as an oft-used tool in the chest of scientific inquiry. Thebasic idea is to characterize multivariate relations by learning the conditional dependence structure. The cortical regions or symptoms are nodes and the featured connections linking nodes are edges that graphically represent the conditional dependence structure. Graphical modeling is quite common in fields with wide data, that is, when there are more variables (p) thanobservations (n). Accordingly, many regularization-based approaches have been developed for those kinds of data. More recently, graphical modeling has emerged in psychology, where the data is typically long or low-dimensional. The primary purpose of GGMnonreg is to provide methods that were specifically designed for low-dimensional data (e.g., those common in the social-behavioral sciences), for which there is a dearth of methodology.

Conditional dependence structure between oil prices and international stock markets

2019 ◽  
Vol 14 (2) ◽  
pp. 439-467 ◽  
Author(s):  
Wajdi Hamma ◽  
Bassem Salhi ◽  
Ahmed Ghorbel ◽  
Anis Jarboui
Keyword(s):  
Stock Markets ◽  
Oil Prices ◽  
Conditional Variance ◽  
Optimal Portfolio ◽  
Dependence Structure ◽  
Hedging Strategy ◽  
Content Type ◽  
Conditional Dependence ◽  
Hedge Ratios ◽  
Stock Portfolio

Purpose The purpose of this paper is to analyze the optimal hedging strategy of the oil-stock dependence structure. Design/methodology/approach The methodology consists to model the data over the daily period spanning from January 02, 2002 to May 19, 2016 by a various copula functions to better modeling the dependence between crude oil market and stock markets, and to use dependence coefficients and conditional variance to calculate optimal portfolio weights and optimal hedge ratios, and to suggest the best hedging strategy for oil-stock portfolio. Findings The findings show that the Gumbel copula is the best model for modeling the conditional dependence structure of the oil and stock markets in most cases. They also indicate that the best hedging strategy for oil price by stock market varies considerably over time, but this variation depends on both the index introduced and the model used. However, the conditional copula method with skewed student more effective than the other models to minimize the risk of oil-stock portfolio. Originality/value This research implication can be valuable for portfolio managers and individual investors who seek to make earnings by diversifying their portfolios. The findings of this study provide evidence of the importance of stock assets for making an optimal portfolio consisting of oil in the case of investments in oil and stock markets. This paper attempts to fill the voids in the literature on volatility among oil prices and stock markets in two important areas. First, it uses copulas to investigate the conditional dependence structure of the oil crude and stock markets in the oil exporting and importing countries. Second, it uses the dependence coefficients and conditional variance to calculate dynamic hedge ratios and risk-minimizing optimal portfolio weights for oil–stock.

scholarly journals On the Conditional Dependence Structure of Multivariate Random Variables

2006 ◽  
Vol 13 (3) ◽  
pp. 513-524
Author(s):  
Jong-Il Baek ◽  
Sung-Tae Park ◽  
Sung-Mo Chung ◽  
Gil-Hwan Lee ◽  
Gil-Pyo Heo
Keyword(s):  
Random Variables ◽  
Dependence Structure ◽  
Conditional Dependence

EMPIRICAL ANALYSIS OF THE RELATIONSHIP BETWEEN OIL AND PRECIOUS METALS MARKETS

2018 ◽  
Vol 13 (01) ◽  
pp. 1850003 ◽  
Author(s):  
KHALED MOKNI
Keyword(s):  
Crude Oil ◽  
Value At Risk ◽  
Precious Metals ◽  
Market Risk ◽  
Dependence Structure ◽  
Empirical Results ◽  
Dependence Modeling ◽  
Market Risks ◽  
Risk Managers ◽  
The Relationship

The relationship between crude oil and precious metals has been a major issue in economic and financial literature. In this paper, the FIEGARCH-copula framework was used to investigate the co-movements not only between returns, but also between volatilities and market risks among crude oil and precious metals markets. Based on daily crude oil and the major precious metals prices from January 2, 2000 to December 31, 2016, our empirical results are as follows: First, a significant positive and asymmetric relationship between oil and precious metals returns, volatilities and market risk was detected. Second, the dependence structure between oil-silver and oil-gold for returns and volatilities are time varying, while the other pairs are characterized by constant dependence. Third, based on the dependence modeling between daily Value-at-Risk (VaR) for the long and short trading position, empirical results show that the market risk relationship between crude oil and precious metals change over time and increase with VaR’s confidence level. Our findings are of interest for investors and risk managers in portfolio’s design and allow for a reliable framework for returns and risk prediction.

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