Portfolio optimization from a Copulas-GJR-GARCH-EVT-CVAR model: Empirical evidence from ASEAN stock indexes

Nguyen, Sang Phu; Huynh, Toan Luu Duc

doi:10.3934/qfe.2019.3.562

Cited by 21 publications

(9 citation statements)

References 44 publications

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“…ARCH models do not use the past standard deviations but formulate conditional variance with maximum likelihood procedure. GARCH model (Benzid and Chebb 2020 ) that also allows for asymmetrical dependencies includes the GJR-GARCH proposed by Glosten et al ( 1993 ) and application of GJR-GARCH model in the ASEAN (Association of Southeast Asian Nations) market well documented by Nguyen and Huynh ( 2019 ) and Duong and Huynh ( 2020 ). In order to regard information of asymmetry of the DONs, we model the energy market volatility using the GJR-GARCH (1,1) model as follows:…”

Section: Empirical Model and Hypothesis Developmentmentioning

confidence: 99%

Impact of COVID-19 pandemic on the energy markets

Shaikh

2021

Econ Change Restruct

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This article aims to uncover the effects of the COVID-19 pandemic on the energy markets in terms of energy stock indexes, energy futures, ETFs, and implied volatility indexes. We model the volatility of energy markets and demonstrate the effects of various phases of the pandemic outbreak (COVID-19) on the energy market. COVID-19-induced uncertainty indicators like the growth of the infection, economic policy uncertainty (EPU), and infectious diseases market volatility (IDsMV) have shown pronounced effects on energy markets’ historical volatility. The volatility of energy ETFs–stocks appears to be more resilient in line with S&P 500 energy stocks. WTI crude oil market has shown an unprecedented overreaction amid pandemic outbreaks and traded with an extreme volatility level. The investors’ sentiment in the energy market was factually higher on the tail events, indicating that fearful investors rushed toward put options and paid an excess premium to protect from unparalleled risk in the energy market.

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Section: Empirical Model and Hypothesis Developmentmentioning

confidence: 99%

Impact of COVID-19 pandemic on the energy markets

Shaikh

2021

Econ Change Restruct

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“…Li and Zeng (2018) further contributed to the literature by estimating dependence structure among ASEAN countries, the United States, and China. Recently, Nguyen and Huynh (2019) constructed the portfolio of the ASEAN community through calculating the dependence structure. We have gathered that the previous results have contributed to the literature in terms of empirical methodologies and the variety of assets.…”

Section: Literature Reviewmentioning

confidence: 99%

Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches

Duong

Huynh

2020

Financ Innov

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This study contributes a rich set of quantitative methodologies including a nonparametric approach (Chi-plots and K-plots) as well as copulas (traditional and timevarying with Student's t-copulas) to the existing literature in terms of determining the dependence structure in ASEAN stock markets. Drawing on the emerging ASEAN equity returns of six countries from January 2001 to December 2017, we found that Student's t-copulas under time-varying approach is the most appropriate approach to explain these co-movements. Among all research return pairs, the dependence between Vietnam and other ASEAN equity indices has the lowest value. Meanwhile, all couples show left-and right-tail dependence by each pair for pre-and postfinancial shocks. Hence, diversification across these pairs of equity markets from ASEAN is still adequate for international investors, though it might trigger contagion risks.

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“…Taking into account the ESORD calculation methodology, the IT system generated the results of selecting specific investment options, subject to balanced demand assessments. The method also allows for portfolio optimization, when an investor would like to get involved in more than one investment [17]. Table 3 provides a visual representation of the evaluation of variants generated by computer algorithms for a specific evaluation methodology.…”

Section: Resultsmentioning

confidence: 99%

Cash-Flow Schedules Optimization within Life Cycle Costing (LCC)

et al. 2020

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Investment and construction plans, architectural and construction decisions, and spatial and technology-related decisions made at the early stages of a project have a significant impact on meeting the investment goals and customer expectations. Decision making is a very time-consuming and complicated process (due to the complexity of construction processes). The whole difficulty comes to specifying the appropriate criteria for assessing the given activities, providing answers to the questions of the decision-making bodies. A set of appropriate criteria and mathematical tools (such as computer algorithms with multi-criteria analysis) can significantly improve and accelerate the decision-making process. This article combines ESORD (an IT tool that allows you to compare different types of solutions based on mathematical calculations) with the Monte Carlo method. The developed approach can help the investor to optimize their cash-flow schedule. The original method enables the client to select a construction project variant characterized by the best economical and sustainable parameters, while taking into account customers’ demands.

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Portfolio optimization from a Copulas-GJR-GARCH-EVT-CVAR model: Empirical evidence from ASEAN stock indexes

Cited by 21 publications

References 44 publications

Impact of COVID-19 pandemic on the energy markets

Impact of COVID-19 pandemic on the energy markets

Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches

Cash-Flow Schedules Optimization within Life Cycle Costing (LCC)

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