VaR and CVaR Estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach

Ruenes, Eduardo Sánchez; Mora, José Antonio Núñez; Aragón, Martha Beatriz Mota

doi:10.24275/etypuam/ne/522020/sanchez

Cited by 3 publications

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“…Nevertheless, empirical studies frequently focus on univariate cases. In this paper we have extended previous works (Núñez et al 2018;Sánchez-Ruenes et al 2020) to model returns of BRIC Indexes and Crude Oil prices using an MNIG distribution.…”

Section: Multivariate Normal Inverse Gaussian (Mnig) Distributionmentioning

confidence: 95%

Generalized Hyperbolic Distribution and Portfolio Efficiency in Energy and Stock Markets of BRIC Countries

Núñez-Mora

Sánchez-Ruenes

2020

IJFS

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Oil, also called black gold, is considered as the commodity which has the greatest impact on the world’s economy, and it has been studied in terms of its relationship and effects on macroeconomic variables such as Gross Domestic Product (GDP), inflation, trade balance, exchange rate and some others. Likewise, the relationship of oil with the financial market has been deepened and is very interesting in the case of emergent economies such as Brazil, Russia, India and China (BRIC) countries. There are many studies and approaches to this topic, but few of them focus on seeking investment opportunities through the diversification of these variables and therefore creating efficient portfolios using other distribution from the norm. This research proposes the construction of diversified portfolios with the returns of the indexes and oil mixes of the BRIC countries modeled under a Normal Inverse Gaussian (NIG) distribution, which is a notable member of the Generalized Hyperbolic (GH) family, and analyzing the effect on investment, by the inclusion of each variable into the portfolio. An important property of the GH family is that the correlations matrix of the returns is obtained from estimation of the parameters of empirical distribution through maximum likelihood. The results show in an optimal configuration, that each instrument of India, China and Brazil, contributes to the portfolio efficiency, in contrast to the index and oil mix of Russia, that do not contribute significantly.

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Section: Multivariate Normal Inverse Gaussian (Mnig) Distributionmentioning

confidence: 95%

Generalized Hyperbolic Distribution and Portfolio Efficiency in Energy and Stock Markets of BRIC Countries

Núñez-Mora

Sánchez-Ruenes

2020

IJFS

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A Review of Generalized Hyperbolic Distributions

Jiang,

Nadarajah,

Hitchen

2023

Comput Econ

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Portfolio Diversification and Optimization at Industry Level, Evidence from Turkey

AZİMOVA¹

2022

Journal of Yaşar University

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This paper applies the mean-variance, mean-VaR, and mean-CVaR portfolio optimization approach to investigate opportunities for domestic diversification from Turkey investors' viewpoints. We explore diversification potential and investment opportunities at the industry level for the time period between 2007 and 2020. The study uses factor analysis to determine domestic diversification opportunities and measure the optimal weight of sectors in the market index. Results from factor analysis show that for investors who desire to create a domestic portfolio considerable diversification opportunities are available. Portfolio optimization analysis indicates that the wholesale, retail trade and transportation industries should be prioritized by the policymakers, as these industries earn the highest returns at a given risk level.

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VaR and CVaR Estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach

Cited by 3 publications

References 0 publications

Generalized Hyperbolic Distribution and Portfolio Efficiency in Energy and Stock Markets of BRIC Countries

Generalized Hyperbolic Distribution and Portfolio Efficiency in Energy and Stock Markets of BRIC Countries

A Review of Generalized Hyperbolic Distributions

Portfolio Diversification and Optimization at Industry Level, Evidence from Turkey

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