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

Duong, Duy; Huynh, Toan Luu Duc

doi:10.1186/s40854-019-0168-7

Cited by 12 publications

(6 citation statements)

References 63 publications

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“…(2014) found stock market volatility in Indonesia, the Philippines and Thailand was related and had a tail dependence. These results were consistent with those of Duong and Huynh (2020), who found the left- and right-tail dependence on the volatility of stock markets in Vietnam, Thailand, Singapore, the Philippines, Malaysia and Indonesia. Pongkongkaew et al.…”

Section: Literature Reviewsupporting

confidence: 92%

“…Studies undertaken before the COVID-19 pandemic revealed the existence of previous volatility in ASEAN stock markets. Stock markets in the ASEAN Economic Community (AEC) have also been found to have relationships with each other and other stock markets outside the region (Click and Plummer, 2005;Janor and Ali, 2007;Sriboonchitta et al, 2014;Duong and Huynh, 2020;Chitkasame and Tansuchat, 2019;Pongkongkaew et al, 2020;Lim, 2007;Jakpar et al, 2013;Lean and Smyth, 2014).…”

Section: Introductionmentioning

confidence: 99%

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An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

Puarattanaarunkorn,

Autchariyapanitkul,

Kiatmanaroch

2023

AJEB

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PurposeUnlimited quantitative easing (QE) is one of the monetary policies used to stimulate the economy during the coronavirus disease 2019 (COVID-19) pandemic. This policy has affected the financial markets worldwide. This empirical research aims at studying the dependence among stock markets before and after unlimited QE announcements.Design/methodology/approachThe copula-based GARCH (1,1) and minimum spanning tree models are used in this study to analyze 14 series of stock market data, on 6 ASEAN and 8 other countries outside the region. The data are divided into two periods to compare the differences in dependence.FindingsThe findings show changes in dependence among the volatility of daily returns in 14 stock markets during each period. After the unlimited QE announcement, the upper tail dependence became more apparent, while the role of the lower tail dependence was reduced. The minimum spanning tree can show the close relationships between stock markets, indicating changes in the connection network after the announcement.Originality/valueThis study allows the dependency to be compared between stock market volatility before and after the announcement of unlimited QE during the COVID-19 pandemic. Moreover, the study fills the literature gap by combining the copula-based GARCH and the minimum spanning tree models to analyze and reveal the systemic network of the relationships.

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Section: Literature Reviewsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

Puarattanaarunkorn,

Autchariyapanitkul,

Kiatmanaroch

2023

AJEB

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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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“…Some researchers have also used the time-varying copula model to study a particular financial market. Duong and Huynh [21] and Wu et al [22] studied the risk in the stock market. e latter focused on the impact of RMB exchange rate and equity spillover effects and found a positive relationship between them.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Dependence Structure and Risk Spillover of Internet Money Funds Based on C‐Vine Copula and Time‐Varying t‐Copula

LinLin

SunHan

et al. 2021

Complexity

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Internet money funds (IMFs) are the most widely involved products in the Internet financial products market. This research utilized the C-vine copula model to study the risk dependence structure of IMFs and then introduces the time-varying t-copula model to analyze the risk spillover of diverse IMFs. The results show the following: (1) The risks of Internet-based IMFs, bank-based IMFs, and fund-based IMFs have obvious dependence structure, and the degree of risk dependence among different categories of IMFs is significantly different. (2) There are risk spillover effects among diverse IMFs, and their risk dependence relationship is characterized by cyclical feature. (3) The risk spillover effect among diverse IMFs is pronounced, and dynamic risk dependence between IMFs is characterized by synchronization.

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

Cited by 12 publications

References 63 publications

An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

Impact of COVID-19 pandemic on the energy markets

Research on the Dependence Structure and Risk Spillover of Internet Money Funds Based on C‐Vine Copula and Time‐Varying t‐Copula

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