Block Trading Based Volatility Forecasting: An Application of VACD-FIGARCH Model

Tu, Teng-Tsai; LIAO, Chih-Wei

doi:10.13106/jafeb.2020.vol7.no4.59

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…The herding arises from the informational difference and poor accessibility, which create an upward trend in market volatility (Bikhchandani & Sharma, 2001;Tu & Liao, 2020). The literature indicates that the debate on herding behavior covers two narratives, i.e., psychological and rational narratives.…”

Section: Literature Reviewmentioning

confidence: 99%

Herding Behavior and Cryptocurrency: Market Asymmetries, Inter-Dependency and Intra-Dependency

Jalal¹,

Sargiacomo²,

Sahar³

et al. 2020

JAFEB

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The study investigates herding behavior in cryptocurrencies in different situations. This study employs daily returns of major cryptocurrencies listed in CCI30 index and sub-major cryptocurrencies and major stock returns listed in Dow-Jones Industrial Average Index, from 2015 to 2018. Quantile regression method is employed to test the herding effect in market asymmetries, inter-dependency and intra-dependency cases. Findings confirm the presence of herding in cryptocurrency in upper quantiles in bullish and high volatility periods because of overexcitement among investors, which lead to high volume trading. Major cryptocurrencies cause herding in sub-major cryptocurrencies, but it is a unidirectional relation. However, no intra-dependency effect among cryptocurrencies and equity market is observed. Results indicate that in the CKK model herding exists at upper quantile in market that may be due when the market is moving fast, continuously trading, and bullish trend are prevailing. Further analysis confirms this narrative as, at upper quantile, the beta of bullish regime is negative and significant, meaning the main source of market herding is a bullish trend in investment, which increases market turbulence and gives investors opportunity to herd. Also, we found that herding in cryptocurrencies exits in high volatility periods, but this herding mostly depends on market activity, not market movement.

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Section: Literature Reviewmentioning

confidence: 99%

Herding Behavior and Cryptocurrency: Market Asymmetries, Inter-Dependency and Intra-Dependency

Jalal¹,

Sargiacomo²,

Sahar³

et al. 2020

JAFEB

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“…Be aware that the herding phenomenon cannot be caused by Bitcoin alone. In addition, it is important to note that the cap-weighted market portfolio analysis did not reveal any signs of herding (Tu and Liao, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Investment decisions determinants in the GCC cryptocurrency market: a behavioural finance perspective

Abdeldayem

Aldulaimi

2023

IJOA

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Purpose This study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC). Design/methodology/approach The study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions. Findings The study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F-statistic indicates the general model's acceptability in the results. Practical implications The study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market. Originality/value This study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.

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“…First, it helps in measuring the dispersions between market and stock returns for varied return conditions including the global financial crisis. Lao and Singh (2011) propose that asymmetrical market movements may magnify the impact of behavioral biases (Tu & Liao, 2020) and suitable market timing strategies can be devised by investors. Evidence of herding will therefore provide implications for investors and other market participants.…”

Section: Significance Of the Studymentioning

confidence: 99%

Herding in Fast Moving Consumer Group Sector: Equity Market Asymmetry and Crisis

Bharti¹,

Kumar²

2020

JAFEB

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This study empirically examines herd behavior for fast moving consumer goods (FMCG) sector stocks under varied market return conditions and the period during the global financial crisis and its aftermath. We examine the sample of stocks trading on the Nifty FMCG Index of the Indian equity market from January 2008 up to December 2018 using the dispersion measure of cross sectional absolute deviation and examine its relationship with the market return to explore herd phenomenon. Quantile regression estimate is used and the results of the study validate rational asset pricing models as the sector does not display herding. In contrast, anti-herd behavior at lower and median quantile values is observed. A possible reason can be the non-cyclical nature of the industry where investors rely more on the fundamentals rather than crowd chasing. We also findthe absence of herd phenomenon during the market asymmetries of bull and bear phases, extreme movements, the period of the global financial crisis, and afterward. We further examine herding under the impact of the information technology (IT) industry and conclude that significant return movements in IT sector impact dispersions in the FMCG industry. Also, there is a co-varying risk between the two sectors confirming the spillover in an integrated market.

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Block Trading Based Volatility Forecasting: An Application of VACD-FIGARCH Model

Cited by 8 publications

References 27 publications

Herding Behavior and Cryptocurrency: Market Asymmetries, Inter-Dependency and Intra-Dependency

Herding Behavior and Cryptocurrency: Market Asymmetries, Inter-Dependency and Intra-Dependency

Investment decisions determinants in the GCC cryptocurrency market: a behavioural finance perspective

Herding in Fast Moving Consumer Group Sector: Equity Market Asymmetry and Crisis

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