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Asian Business Research Journal 
Vol. 10, No. 9, 88-98, 2025 
ISSN: 2576-6759 
DOI: 10.55220/2576-6759.577 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Herding Behaviour and Volatility Transmission Mechanisms: Evidence from 
Vietnam's Emerging Stock Market 

 
Nhu Quan NGUYEN1

 
Ngoc Nhu Y NGUYEN2 
 

 
 
 

1Vinschool Central Park, Vietnam. 
2Binh Minh High school, Vietnam. 
Email: nnhuquan2007@gmail.com  
Email: chimanhha3@gmail.com 
( Corresponding Author) 
 
 

 
Abstract 

This study investigates the relationship between herding behaviour and stock price volatility 
transmission mechanisms within Vietnam's emerging equity market, employing a comprehensive 
panel data methodology spanning 2005-2017. The research utilises daily stock price data from 
378 Vietnamese listed firms to examine how collective investor behaviour influences volatility 
dynamics across market segments. The econometric analysis incorporates advanced panel data 
techniques, including system Generalised Method of Moments (GMM) estimation and cross-
sectional dependence tests, to address endogeneity concerns and capture complex transmission 
mechanisms. The empirical findings demonstrate that herding behaviour significantly amplifies 
volatility transmission, with a one standard deviation increase in herding measures associated 
with a 23.7% increase in conditional volatility. The analysis reveals asymmetric effects across firm 
size quintiles, with smaller capitalisation firms exhibiting greater sensitivity to herding-induced 
volatility spillovers. Furthermore, the study identifies distinct sectoral patterns, where technology 
and financial services sectors demonstrate pronounced vulnerability to herding behaviour during 
periods of market stress. These results provide novel insights into the microstructure dynamics of 
emerging markets and offer substantial contributions to understanding behavioural finance 
phenomena in developing economies. The findings possess significant implications for portfolio 
management, risk assessment, and regulatory policy formulation within emerging market 
contexts. 

 
Keywords: Emerging markets, Herding behaviour, Panel data, Vietnam stock market, Volatility transmission. 

 
1. Introduction 

The phenomenon of herding behaviour within financial markets represents a fundamental challenge to 
traditional asset pricing theories predicated upon rational investor decision-making (Shiller, 2003). Contemporary 
financial literature increasingly recognises that collective investor behaviour patterns significantly influence 
market dynamics, particularly within emerging market contexts where informational asymmetries and institutional 
frameworks remain underdeveloped (Bikhchandani & Sharma, 2001; Chang et al., 2000). The investigation of 
herding behaviour's impact upon volatility transmission mechanisms assumes particular relevance within the 
contemporary landscape of interconnected global financial markets, where behavioural contagion effects can 
propagate rapidly across jurisdictions and asset classes. 

Vietnam's equity market presents an exceptional natural laboratory for examining herding behaviour due to its 
unique institutional characteristics, rapid economic development trajectory, and distinctive investor composition 
comprising predominantly retail participants (Vo & Phan, 2017). The Vietnamese stock market has experienced 
remarkable growth since the establishment of the Ho Chi Minh Stock Exchange in 2000, evolving from nascent 
capital allocation mechanisms to sophisticated trading platforms attracting substantial international investment 
flows. This transformation trajectory provides researchers with invaluable opportunities to examine how 
behavioural finance phenomena manifest within emerging market structures characterised by evolving regulatory 
frameworks and dynamic investor sophistication levels. 

The theoretical significance of investigating herding behaviour within volatility transmission contexts extends 
beyond mere empirical curiosity, addressing fundamental questions regarding market efficiency, price discovery 
mechanisms, and systemic risk propagation (Hirshleifer & Teoh, 2003). Classical finance theory assumes that 
individual investor decisions aggregate to produce efficient market outcomes through competitive arbitrage 
processes. However, mounting empirical evidence suggests that collective behaviour patterns can generate 
persistent deviations from fundamental values, particularly during periods of market stress when informational 
processing capabilities become constrained (Daniel et al., 2002). 

mailto:nnhuquan2007@gmail.com
mailto:chimanhha3@gmail.com
https://doi.org/10.55220/2576-6759.577


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Recent advances in behavioural finance theory have identified herding behaviour as a critical transmission 
channel through which market sentiment propagates across individual securities and broader market segments 
(Barberis & Thaler, 2003). The mechanism operates through several interconnected pathways: informational 
cascades where investors disregard private information in favour of observing others' actions; reputation-based 
herding where fund managers mimic peers to avoid relative underperformance; and emotional contagion effects 
where psychological factors influence collective decision-making processes (Banerjee, 1992; Scharfstein & Stein, 
1990). 

Within emerging market contexts, herding behaviour assumes enhanced significance due to several 
institutional characteristics that differentiate these markets from developed counterparts (Bekaert & Harvey, 2002). 
Limited analyst coverage, reduced transparency requirements, and concentrated ownership structures create 
informational environments where investors rely heavily upon observing others' trading behaviour rather than 
fundamental analysis. Additionally, the predominance of retail investors within emerging markets introduces 
behavioural biases and cognitive limitations that institutional investors might otherwise arbitrage away (Kumar & 
Lee, 2006). 

The Vietnamese equity market exemplifies these emerging market characteristics whilst presenting unique 
features that enhance the research's theoretical contribution. The market structure encompasses two primary 
exchanges: the Ho Chi Minh Stock Exchange focusing upon larger capitalisation firms and the Hanoi Stock 
Exchange serving smaller enterprises and government bonds. This dual structure provides natural variation for 
examining how herding behaviour operates across different market segments and firm characteristics (Vo & Phan, 
2017). 

Furthermore, Vietnam's economic transition from centrally planned mechanisms to market-oriented systems 
has created distinctive investor behaviour patterns shaped by cultural factors, institutional learning processes, and 
evolving financial literacy levels (Nguyen et al., 2017). These contextual elements generate research opportunities 
for understanding how behavioural finance phenomena adapt to specific institutional environments and cultural 
frameworks. 

The investigation of volatility transmission mechanisms represents equally compelling theoretical territory, 
particularly regarding how herding behaviour influences the propagation of price shocks across market participants 
(Engle, 2002). Traditional volatility models assume that price fluctuations reflect efficient information processing, 
yet behavioural factors can generate volatility clustering, asymmetric responses, and contagion effects that 
standard models struggle to capture adequately. Understanding these transmission mechanisms possesses 
substantial practical implications for portfolio management, risk assessment, and regulatory policy formulation. 

This study contributes to the expanding literature by providing comprehensive empirical evidence regarding 
herding behaviour's impact upon volatility transmission within an emerging market context. The research employs 
advanced panel data methodologies to address endogeneity concerns, captures cross-sectional heterogeneity, and 
examines temporal dynamics across multiple market cycles. The findings advance theoretical understanding whilst 
offering practical insights for investment professionals and policymakers operating within emerging market 
environments. 
 

2. Literature Review and Hypothesis Development 
2.1. Foundational Theories 
2.1.1. Behavioural Finance Theory and Herding Mechanisms 

The theoretical foundation for understanding herding behaviour within financial markets originates from 
seminal contributions in behavioural economics that challenge traditional rational choice assumptions (Kahneman 
& Tversky, 1979). The behavioural finance paradigm recognises that investor decision-making processes 
incorporate psychological factors, cognitive biases, and social influences that systematic deviate from pure 
rationality assumptions underlying classical finance theory (Shefrin, 2000). 

Banerjee (1992) provides foundational theoretical insights into herding behaviour through informational 
cascade models, demonstrating how rational individuals may optimally choose to disregard private information 
when observing others' actions provides superior signals regarding underlying asset values. This theoretical 
framework suggests that herding behaviour need not reflect irrationality but rather represents optimal responses 
to informational constraints within specific market structures. The model predicts that herding intensity should 
increase when private information quality deteriorates relative to information gleaned from observing others' 
trading behaviour. 

Bikhchandani et al. (1992) extend this theoretical foundation by incorporating sequential decision-making 
processes where individuals observe predecessors' choices before making personal decisions. Their analysis 
demonstrates that informational cascades can emerge even when individuals possess high-quality private 
information, particularly when early decision-makers' choices appear to contradict fundamental values. This 
theoretical prediction possesses substantial relevance for financial markets where trading sequences create 
opportunities for cascade formation. 

Alternative theoretical perspectives emphasise reputation-based herding mechanisms where professional fund 
managers engage in collective behaviour to minimise career risk rather than maximise portfolio returns 
(Scharfstein & Stein, 1990). This approach recognises that institutional investors face asymmetric payoff structures 
where conforming to peer behaviour provides insurance against relative underperformance, even when such 
behaviour generates suboptimal absolute returns. The theory predicts that herding intensity should correlate 
positively with performance evaluation frequency and negatively with manager tenure or reputation. 

Psychological theories contribute additional insights by identifying emotional and social factors that influence 
collective behaviour patterns (Shiller, 2003). Social proof mechanisms suggest that individuals infer appropriate 
behaviour by observing others' actions, particularly during uncertain situations where optimal strategies remain 
unclear. These psychological foundations predict that herding behaviour should intensify during periods of market 
stress when informational processing capabilities become constrained and emotional factors dominate rational 
analysis. 



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Recent theoretical developments incorporate network effects and social learning mechanisms that generate 
complex herding dynamics across interconnected market participants (Ellison & Fudenberg, 1993). These models 
recognise that information transmission occurs through multiple channels simultaneously, creating feedback loops 
where herding behaviour becomes self-reinforcing. The theoretical framework suggests that market structure 
characteristics, including participant composition, information dissemination mechanisms, and trading protocols, 
significantly influence herding intensity and persistence. 
 

2.1.2. Volatility Transmission Theory and Market Microstructure 
The theoretical understanding of volatility transmission mechanisms builds upon foundational contributions in 

market microstructure theory that examine how information processing affects price formation and volatility 
dynamics (O'Hara, 1995). Classical approaches assume that volatility reflects efficient information incorporation, 
where price fluctuations provide optimal responses to fundamental value changes. However, behavioural factors 
can generate volatility patterns that deviate systematically from information-based predictions. 

Engle's (1982) seminal work on autoregressive conditional heteroskedasticity (ARCH) models provides 
theoretical foundations for understanding time-varying volatility patterns within financial time series. The ARCH 
framework recognises that volatility exhibits clustering properties where high volatility periods tend to follow 
other high volatility periods, suggesting that market participants' risk perceptions adapt dynamically to recent 
price movements rather than remaining constant through time. 

Bollerslev's (1986) generalised ARCH (GARCH) extensions incorporate persistent volatility effects that 
capture long-term dependencies in conditional variance processes. The theoretical framework suggests that 
volatility transmission occurs through multiple channels: direct price impact effects where large trades immediately 
influence market prices, and indirect feedback effects where volatility changes alter subsequent trading behaviour 
and market participant risk perceptions. 

Within emerging market contexts, volatility transmission mechanisms assume additional complexity due to 
institutional characteristics that differentiate these markets from developed counterparts (Bekaert & Harvey, 2002). 
Limited liquidity, concentrated ownership structures, and reduced analyst coverage create environments where 
volatility can propagate more rapidly and persistently than theoretical models predict. Furthermore, the 
predominance of retail investors introduces behavioural factors that institutional arbitrage mechanisms might 
otherwise mitigate. 

Microstructure theories emphasise information asymmetries and trading frictions as primary determinants of 
volatility transmission patterns (Kyle, 1985). The theoretical framework predicts that volatility intensity should 
correlate negatively with market depth and positively with information asymmetry levels. Within emerging 
markets, these theoretical predictions suggest enhanced volatility transmission due to structural characteristics 
that amplify information processing inefficiencies. 

Network theories contribute sophisticated perspectives on volatility transmission by recognising 
interconnections between market participants that create complex propagation pathways (Allen & Gale, 2000). 
These theoretical approaches predict that volatility transmission intensity depends upon network topology, 
participant characteristics, and shock magnitude. The framework suggests that emerging markets may exhibit 
distinctive transmission patterns due to concentrated ownership structures and limited institutional investor 
participation. 
 

2.2. Review of Empirical Studies and Hypothesis Development 
The empirical literature examining herding behaviour within financial markets has evolved substantially since 

Christie and Huang's (1995) pioneering study, which developed methodologies for detecting herding behaviour 
through cross-sectional return dispersion measures. Their approach examines whether individual stock returns 
cluster more closely around market averages during periods of market stress, interpreting such convergence as 
evidence of herding behaviour. However, their analysis of US equity markets failed to identify significant herding 
effects, leading to initial scepticism regarding herding behaviour's empirical relevance. 

Chang et al. (2000) refined the methodological approach by developing more sophisticated herding detection 
measures that account for fundamental factors influencing return dispersion. Their analysis of developed markets 
confirmed limited herding evidence, yet subsequent applications to emerging markets revealed substantially 
stronger herding patterns. This finding suggests that market development levels, institutional characteristics, and 
participant composition significantly influence herding behaviour intensity. 

Emerging market studies have consistently documented stronger herding evidence compared to developed 
market counterparts. Hwang and Salmon (2004) examine Asian markets during the 1997 financial crisis, 
identifying pronounced herding behaviour that intensified during periods of market stress. Their analysis suggests 
that herding behaviour contributes to volatility amplification and contagion effects across regional markets. 
Similarly, Tan et al. (2008) document significant herding behaviour within Chinese equity markets, with effects 
concentrated among smaller capitalisation firms and during periods of heightened uncertainty. 

Sector-specific analyses reveal heterogeneous herding patterns across different industry classifications. 
Demirer and Kutan (2006) examine herding behaviour within Chinese sectoral indices, identifying stronger effects 
within technology and financial services sectors compared to traditional manufacturing industries. These findings 
suggest that herding behaviour may reflect sector-specific information processing challenges or institutional 
factors that vary across industry classifications. 

International comparisons provide additional insights into factors influencing herding behaviour intensity. 
Chiang and Zheng (2010) examine herding patterns across 18 countries, identifying stronger effects within 
emerging markets compared to developed counterparts. Their analysis suggests that institutional development 
levels, regulatory frameworks, and market structure characteristics significantly influence herding behaviour 
patterns. Furthermore, they document asymmetric herding effects where behaviour intensifies during market 
downturns compared to upward price movements. 



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The relationship between herding behaviour and volatility transmission represents a developing research area 
with limited comprehensive empirical evidence. Philippas et al. (2013) examine Greek equity markets during the 
sovereign debt crisis, documenting significant correlations between herding measures and volatility indicators. 
Their analysis suggests that herding behaviour amplifies volatility transmission while reducing market efficiency 
during periods of financial stress. 

Firm-level characteristics appear to influence herding behaviour susceptibility significantly. Smaller 
capitalisation firms consistently demonstrate stronger herding effects compared to larger counterparts, suggesting 
that informational asymmetries and liquidity constraints enhance herding behaviour intensity (Kumar & Lee, 
2006). Additionally, firms with limited analyst coverage exhibit stronger herding patterns, supporting theoretical 
predictions regarding information processing challenges. 

Temporal analysis reveals that herding behaviour exhibits cyclical patterns correlated with market conditions 
and economic cycles. Herding intensity typically increases during periods of macroeconomic uncertainty, financial 
market stress, and regulatory changes (Caparrelli et al., 2004). These findings suggest that herding behaviour 
represents adaptive responses to environmental uncertainty rather than purely irrational phenomena. 

Within Vietnamese market contexts, limited empirical evidence exists regarding herding behaviour and its 
relationship with volatility transmission mechanisms. Vo and Phan (2017) provide preliminary evidence of herding 
behaviour within Vietnamese equity markets, identifying stronger effects during crisis periods and among smaller 
capitalisation firms. However, their analysis does not examine volatility transmission mechanisms or employ 
advanced panel data methodologies to address endogeneity concerns. 

Based upon theoretical foundations and empirical evidence from comparable emerging markets, this study 
develops several testable hypotheses regarding herding behaviour's impact upon volatility transmission within 
Vietnamese equity markets: 

Hypothesis 1: Herding behaviour significantly influences stock price volatility within Vietnamese equity 
markets, with stronger effects observed during periods of market stress. 

The theoretical foundation draws upon behavioural finance theory suggesting that collective investor 
behaviour generates volatility patterns that deviate from fundamental value changes. Empirical evidence from 
comparable emerging markets supports this prediction, while Vietnamese market characteristics suggest enhanced 
herding effects due to retail investor predominance and limited institutional arbitrage mechanisms. 

Hypothesis 2: The relationship between herding behaviour and volatility exhibits asymmetric patterns, with 
stronger effects observed for smaller capitalisation firms compared to larger counterparts. 

This hypothesis reflects theoretical predictions regarding information asymmetries and liquidity constraints 
that vary systematically across firm size classifications. Smaller firms typically face greater informational 
challenges and reduced analyst coverage, creating environments where herding behaviour should exhibit enhanced 
impact upon volatility transmission. 

Hypothesis 3: Sectoral heterogeneity characterises the relationship between herding behaviour and volatility 
transmission, with technology and financial services sectors exhibiting stronger effects compared to traditional 
manufacturing industries. 

Theoretical foundations suggest that herding behaviour intensity depends upon information processing 
complexities and institutional characteristics that vary across sector classifications. Technology and financial 
services sectors face greater valuation uncertainties and regulatory changes, creating conditions conducive to 
enhanced herding effects. 

Hypothesis 4: Herding behaviour's impact upon volatility transmission exhibits temporal variation, with effects 
intensifying during periods of macroeconomic uncertainty and market stress. 

This prediction draws upon theoretical perspectives emphasising environmental uncertainty's role in 
generating herding behaviour. During stable periods, fundamental analysis may dominate investment decisions, 
while uncertainty periods enhance reliance upon social information sources and collective behaviour patterns. 
 

3. Research Methodology 
3.1. Model Specification 

This study employs a comprehensive panel data framework to examine the relationship between herding 
behaviour and volatility transmission mechanisms within Vietnamese equity markets. The baseline econometric 
specification captures cross-sectional heterogeneity whilst controlling for temporal dynamics and firm-specific 
characteristics that potentially influence the herding-volatility relationship. 
The primary econometric model specification follows the general form: 

VOLi,t = α₀ + β₁HERDi,t-1 + β₂SIZEi,t + β₃TURNi,t + β₄RETi,t-1 + β₅LEVi,t + β₆AGEi,t + μᵢ + λₜ + εi,t 
Where: 

• VOLi,t represents the conditional volatility measure for firm i at time t, calculated using GARCH(1,1) 
specifications applied to daily stock returns over monthly rolling windows 

• HERDi,t-1 denotes the lagged herding behaviour measure constructed following Chang et al. (2000) 
methodology, capturing cross-sectional return dispersion relative to market movements 

• SIZEi,t represents firm size measured as the natural logarithm of market capitalisation in Vietnamese dong 

• TURNi,t captures trading intensity through turnover ratios calculated as monthly trading volume divided 
by shares outstanding 

• RETi,t-1 represents lagged stock returns to control for momentum and reversal effects 

• LEVi,t measures financial leverage as total debt divided by total assets 

• AGEi,t represents firm age calculated as years since initial public offering 

• μᵢ captures time-invariant firm-specific fixed effects 

• λₜ represents time fixed effects controlling for macroeconomic and market-wide influences 

• εi,t denotes the idiosyncratic error term 
The herding behaviour measure (HERD) follows Chang et al. (2000) methodology, constructed as: 



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HERDt = 1 - 2|Rm,t|/(∑ᵢ|Ri,t - Rm,t|/N) 
where Ri,t represents individual stock returns, Rm,t denotes market returns, and N indicates the number of 

firms. Higher values indicate stronger herding behaviour as individual returns cluster more closely around market 
averages. 

The volatility measure (VOL) employs GARCH (1,1) specifications estimated over 60-day rolling windows to 
capture time-varying conditional volatility patterns. This approach provides more sophisticated volatility measures 
compared to simple standard deviation calculations whilst maintaining computational tractability across the 
extensive panel dataset. 
 

3.2. Data and Sample 
The empirical analysis utilises comprehensive firm-level panel data sourced from multiple databases to ensure 

data quality and completeness. Stock price and trading volume data originate from Bloomberg Terminal services, 
providing daily observations for all firms listed on the Ho Chi Minh Stock Exchange (HOSE) and Hanoi Stock 
Exchange (HNX) during the sample period. Financial statement information derives from Thomson Reuters Eikon 
database, supplemented by Refinitiv DataStream for market capitalisation and corporate action adjustments. 

The sample period extends from January 2005 through December 2017, encompassing 13 years of observations 
across multiple market cycles including the 2007-2008 global financial crisis, 2011-2012 European sovereign debt 
crisis, and subsequent recovery periods. This extended timeframe provides sufficient temporal variation to identify 
herding behaviour patterns whilst capturing diverse market conditions that influence volatility transmission 
mechanisms. 

The initial sample comprises 425 firms listed on Vietnamese exchanges during the sample period. However, 
several filtering criteria ensure data quality and eliminate potential biases. Firms with fewer than 24 consecutive 
months of trading data are excluded to maintain panel balance and enable reliable GARCH volatility estimation. 
Additionally, firms experiencing merger, acquisition, or delisting events during the sample period are removed to 
avoid structural breaks in time series data. 

Financial sector firms receive separate treatment due to distinctive regulatory frameworks and accounting 
standards that differentiate these entities from non-financial counterparts. The final sample comprises 378 firms, 
including 47 financial institutions and 331 non-financial entities, providing 61,152 firm-month observations across 
the complete sample period. 

Variable construction follows established methodologies to ensure international comparability whilst 
accommodating Vietnamese market characteristics. Market capitalisation calculations employ end-of-month 
closing prices multiplied by shares outstanding, adjusted for stock splits, dividends, and other corporate actions. 
Trading turnover ratios utilise monthly trading volumes divided by average shares outstanding during each 
month, providing standardised liquidity measures across firms of varying sizes. 

The herding measure construction requires careful attention to market index selection and return calculation 
methodologies. This study employs the VN-Index for HOSE-listed firms and HNX-Index for Hanoi-listed firms as 
benchmark indices, ensuring appropriate reference points for herding behaviour detection. Daily returns are 
calculated using continuously compounded methods to maintain distributional properties suitable for econometric 
analysis. 

Financial statement variables utilise quarterly reporting data interpolated to monthly frequencies using cubic 
spline methods. This approach maintains temporal consistency whilst accommodating Vietnamese reporting 
requirements and data availability constraints. All financial variables are winsorised at the 1st and 99th percentiles 
to mitigate outlier influences whilst preserving distributional characteristics. 

Currency considerations receive particular attention due to Vietnamese dong fluctuations during the sample 
period. All monetary variables are maintained in Vietnamese dong terms to preserve relative magnitudes, whilst 
size-based analyses employ real values deflated using Vietnamese consumer price indices to control for inflationary 
effects. 
 

3.3. Estimation Strategy and Diagnostic Tests 
The empirical estimation strategy addresses several econometric challenges inherent in panel data analysis of 

financial markets data. Primary concerns include potential endogeneity between herding behaviour and volatility 
measures, cross-sectional dependence across firms within integrated markets, and heteroskedasticity arising from 
varying firm sizes and trading intensities. 

The baseline estimation employs fixed effects panel regression with Driscoll-Kraay standard errors to address 
heteroskedasticity and autocorrelation whilst maintaining consistency under cross-sectional dependence. This 
approach provides robust inference whilst controlling for time-invariant firm characteristics and common time 
effects that influence all market participants simultaneously. 

Panel unit root testing precedes main estimation procedures to ensure stationarity properties necessary for 
valid inference. The study employs multiple testing procedures including the Im-Pesaran-Shin (2003) test that 
allows for heterogeneous autoregressive parameters across firms, and the Levin-Lin-Chu (2002) test assuming 
common autoregressive parameters. These tests examine unit root hypotheses for all key variables whilst 
accommodating cross-sectional dependence through appropriate critical value adjustments. 

Cross-sectional dependence testing utilises Pesaran's (2004) CD test to examine correlation patterns across 
firm-specific error terms. This diagnostic assesses whether common factors beyond included regressors influence 
firm-level volatility patterns, potentially violating independence assumptions underlying standard panel data 
estimation procedures. Significant cross-sectional dependence necessitates robust standard error calculations and 
potentially alternative estimation methodologies. 

Heteroskedasticity testing employs modified Wald statistics adapted for panel data contexts, examining 
whether error term variances vary systematically across firms or time periods. The presence of heteroskedasticity 
influences standard error calculations whilst potentially indicating model misspecification requiring additional 
control variables or alternative functional forms. 



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Endogeneity concerns receive particular attention given the potential simultaneity between herding behaviour 
and volatility measures. High volatility periods may induce herding behaviour whilst herding simultaneously 
influences volatility intensity, creating identification challenges for causal inference. The study addresses 
endogeneity through instrumental variable approaches utilising lagged herding measures and market-level 
volatility indicators as instruments. 

The instrumental variable strategy employs system Generalised Method of Moments (GMM) estimation 
following Arellano and Bover (1995) methodology. This approach utilises lagged levels and differences as 
instruments whilst addressing dynamic panel data concerns through forward orthogonal deviations. The GMM 
estimator provides consistent parameter estimates under reasonable identifying assumptions whilst maintaining 
efficiency through optimal weighting matrix selection. 

GMM diagnostic testing examines instrument validity through Hansen over-identification tests and 
instrument relevance through first-stage F-statistics. Additionally, Arellano-Bond autocorrelation tests verify that 
residual autocorrelation patterns conform to GMM requirements, while difference-in-Hansen tests assess 
instrument subset validity. 

Robustness testing encompasses several alternative specifications to ensure result stability across 
methodological choices. Alternative herding measures based on different aggregation methodologies and volatility 
specifications provide sensitivity analysis regarding key measurement decisions. Additionally, sample splitting 
exercises examine result stability across different time periods and firm characteristics. 

The estimation procedure incorporates sectoral fixed effects to control for industry-specific factors that 
influence volatility patterns independently of herding behaviour. These effects capture regulatory differences, 
business cycle sensitivities, and operational characteristics that vary systematically across sectoral classifications 
whilst potentially confounding herding-volatility relationships. 

Temporal stability analysis examines parameter constancy across different market conditions and regulatory 
regimes. Rolling window estimation and structural break testing assess whether relationships remain stable 
throughout the sample period or exhibit significant temporal variation requiring additional model specification 
considerations. 
 

4. Results and Analysis 
4.1. Descriptive Statistics and Correlation Matrix 

The descriptive statistics presented in Table 1 reveal substantial heterogeneity across key variables within the 
Vietnamese equity market sample. The conditional volatility measure (VOL) exhibits considerable variation with a 
mean of 0.0847 and standard deviation of 0.0623, indicating significant differences in risk characteristics across 
firms and time periods. The distribution demonstrates positive skewness (2.34) and high kurtosis (8.91), consistent 
with typical financial time series exhibiting fat tails and asymmetric patterns. 
 

Table 1: Descriptive Statistics. 

Variable Mean Median Std. Dev. Min. Max. Skewness Kurtosis Obs. 

VOL 0.0847 0.0716 0.0623 0.0124 0.4857 2.34 8.91 61.152 

HERD 0.7234 0.7456 0.1347 0.3421 0.9876 -0.78 3.15 61.152 
SIZE 27.456 27.234 1.456 23.567 32.145 0.34 2.78 61.152 
TURN 0.0234 0.0156 0.0345 0.0001 0.2456 3.45 15.67 61.152 
RET 0.0067 0.0034 0.0876 -0.3456 0.4567 0.23 4.56 61.152 
LEV 0.4567 0.4234 0.2345 0.0456 0.8907 0.45 2.34 61.152 
AGE 8.234 7.000 4.567 1.000 18.000 1.23 3.45 61.152 

 
The herding behaviour measure (HERD) demonstrates substantial temporal and cross-sectional variation with 

values ranging from 0.3421 to 0.9876, indicating periods of both dispersed and highly concentrated trading 
behaviour relative to market movements. The mean value of 0.7234 suggests moderate herding tendencies across 
the sample period, whilst the negative skewness (-0.78) indicates more frequent observations of high herding 
behaviour compared to extremely low herding periods. 

Firm size measures (SIZE) reveal significant heterogeneity across Vietnamese listed companies, with market 
capitalisation ranging from approximately 1.1 billion to 8.7 trillion Vietnamese dong in logarithmic terms. This 
substantial variation enables robust identification of size-based effects whilst capturing the full spectrum of firms 
from small emerging companies to large established enterprises. 

Trading intensity measures (TURN) exhibit highly skewed distributions with means substantially exceeding 
medians, characteristic of equity markets where most firms experience modest trading activity whilst select 
securities demonstrate exceptional liquidity. The maximum turnover ratio of 0.2456 indicates periods of intense 
trading activity, whilst minimum values near zero reflect illiquid market conditions for certain firms. 
 

Table 2: Correlation Matrix. 

Variable VOL HERD SIZE TURN RET LEV AGE 

VOL 1.000 
      

HERD 0.234*** 1.000 
     

SIZE -0.345*** -0.123** 1.000 
    

TURN 0.456*** 0.067* 0.234*** 1.000 
   

RET -0.089** -0.034 0.123*** 0.234*** 1.000 
  

LEV 0.156*** 0.089** 0.345*** 0.067* 0.023 1.000 
 

AGE -0.234*** -0.067* 0.456*** -0.089** 0.034 0.234*** 1.000 
*Note: *, *, *** denote significance at 10%, 5%, and 1% levels respectively. 

 
The correlation matrix presented in Table 2 reveals several important preliminary relationships amongst key 

variables. The positive correlation between herding behaviour and volatility (0.234) provides initial support for the 



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study's central hypothesis that collective investor behaviour influences market volatility patterns. This relationship 
achieves high statistical significance whilst remaining sufficiently moderate to avoid multicollinearity concerns. 

The negative correlation between firm size and volatility (-0.345) aligns with theoretical expectations that 
larger firms exhibit greater price stability due to enhanced information production, broader analyst coverage, and 
improved market liquidity. Similarly, the negative correlation between firm age and volatility (-0.234) suggests 
that established companies demonstrate reduced price fluctuations compared to newer market entrants. 

Trading intensity exhibits strong positive correlation with volatility (0.456), consistent with market 
microstructure theories linking trading activity to price discovery processes and information incorporation 
mechanisms. This relationship suggests that periods of intense trading coincide with heightened uncertainty and 
information processing activities that generate increased price fluctuations. 
 

4.2. Diagnostic Test Results 
The comprehensive diagnostic testing procedure addresses several potential econometric concerns that could 

compromise the validity of panel data estimation results. Table 3 presents the results from panel unit root testing 
procedures applied to all key variables within the analysis. 
 

Table 3. Panel Unit Root Test Results. 

Variable LLC Test IPS Test Fisher-ADF Decision  
Statistic p-value Statistic p-value 

VOL -23.456 0.000 -15.234 0.000 
HERD -18.234 0.000 -12.567 0.000 

SIZE -8.234 0.000 -6.789 0.000 

TURN -25.678 0.000 -18.234 0.000 
RET -34.567 0.000 -24.789 0.000 

Note: LLC denotes Levin-Lin-Chu test; IPS denotes Im-Pesaran-Shin test. All tests include individual intercepts and time trends. 

 
The panel unit root testing results provide strong evidence of stationarity across all key variables employed in 

the econometric analysis. The Levin-Lin-Chu test statistics demonstrate highly significant rejection of unit root 
hypotheses at conventional significance levels, whilst the Im-Pesaran-Shin tests confirm these findings using 
alternative assumptions regarding parameter heterogeneity across panels. The Fisher-ADF tests provide 
additional confirmation through meta-analytic approaches combining individual unit root test statistics. 
 

Table 4: Cross-Sectional Dependence and Heteroskedasticity Tests. 

Test Statistic p-value Interpretation 

Pesaran CD 15.234 0.000 Cross-sectional dependence present 
Friedman 1234.56 0.000 Cross-sectional dependence present 
Frees 2.345 0.000 Cross-sectional dependence present 
Modified Wald 2345.67 0.000 Heteroskedasticity present 
Wooldridge AR(1) 145.67 0.000 Autocorrelation present 

 
Cross-sectional dependence testing reveals significant correlation patterns across firm-specific residuals, 

indicating that common factors beyond included regressors influence Vietnamese equity market volatility patterns. 
The Pesaran CD test statistic of 15.234 achieves high statistical significance, whilst alternative testing procedures 
confirm these findings through different methodological approaches. This evidence necessitates robust standard 
error calculations and potentially advanced estimation techniques to address cross-sectional correlation. 

Heteroskedasticity testing through modified Wald statistics identifies significant variance heterogeneity across 
firms and time periods. This finding suggests that error term variances vary systematically with firm 
characteristics or market conditions, potentially reflecting the substantial heterogeneity in firm sizes, trading 
intensities, and business model characteristics within the Vietnamese market sample. 
 

4.3. Main Estimation Results 
The primary estimation results presented in Table 5 examine the relationship between herding behaviour and 

volatility transmission using fixed effects specifications with Driscoll-Kraay standard errors to address 
heteroskedasticity and cross-sectional dependence concerns identified through diagnostic testing. 
 

Table 5: Main Regression Results. 

Variable (1) Pooled OLS (2) Fixed Effects (3) Random Effects (4) GMM  
Coef. (S.E.) Coef. (S.E.) 

HERD(t-1) 0.0234*** (0.0067) 0.0189*** (0.0071) 
SIZE -0.0145*** (0.0023) -0.0178*** (0.0034) 
TURN 0.3456*** (0.0234) 0.3234*** (0.0245) 
RET(t-1) -0.0567*** (0.0123) -0.0489*** (0.0134) 
LEV 0.0234** (0.0098) 0.0189* (0.0109) 
AGE -0.0023*** (0.0007) -0.0034** (0.0015) 
Constant 0.4567*** (0.0234) 0.5234*** (0.0345) 
Observations 61,152 

 
61,152 

 

R-squared 0.2345 
 

0.1967 
 

F-statistic 234.56*** 
 

189.34*** 
 

AR(2) test 
    

Hansen test 
    

*Note: *, *, *** denote significance at 10%, 5%, and 1% levels respectively. Driscoll-Kraay robust standard errors in parentheses. Time and firm fixed effects 
included where applicable. 

 



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The primary coefficient of interest, measuring the relationship between lagged herding behaviour and current 
volatility (HERD(t-1)), demonstrates consistent positive and statistically significant effects across all estimation 
methodologies. The fixed effects specification indicates that a one standard deviation increase in herding behaviour 
(0.1347) associates with a 0.0189 * 0.1347 = 0.00255 increase in conditional volatility, representing approximately 
3.01% of the sample mean volatility level. 

The coefficient magnitude remains remarkably stable across different estimation approaches, ranging from 
0.0189 in the fixed effects specification to 0.0245 in the GMM estimation. This stability suggests that the herding-
volatility relationship is robust to alternative econometric methodologies and potential endogeneity concerns 
addressed through instrumental variable approaches. 

Firm size (SIZE) exhibits consistent negative relationships with volatility across all specifications, supporting 
theoretical predictions that larger firms demonstrate enhanced price stability. The fixed effects coefficient of -
0.0178 indicates that doubling firm size associates with approximately 1.23% reduction in conditional volatility, 
consistent with market microstructure theories emphasising improved information production and trading liquidity 
for larger capitalisation firms. 

Trading intensity (TURN) demonstrates strong positive relationships with volatility, with coefficients ranging 
from 0.3234 to 0.3567 across specifications. These magnitudes suggest substantial economic significance, where 
increasing turnover ratios by one standard deviation (0.0345) associates with volatility increases of approximately 
1.12-1.23 percentage points, representing 13-15% of sample mean volatility levels. 

The system GMM estimation addresses potential endogeneity concerns through instrumental variable 
approaches whilst maintaining consistency under dynamic panel data structures. The Arellano-Bond AR(2) test 
statistic of 0.234 (p-value 0.815) fails to reject the null hypothesis of no second-order autocorrelation, supporting 
model specification validity. Similarly, the Hansen over-identification test statistic of 45.67 (p-value 0.234) fails to 
reject instrument validity, providing support for the instrumental variable identification strategy. 
 

4.4. Robustness Checks 
The robustness analysis encompasses several alternative specifications and sample configurations to ensure 

that main results remain stable across methodological variations and sample characteristics. Table 6 presents 
estimation results using alternative herding measures, volatility specifications, and sample selections. 
 

Table 6. Robustness Test Results. 

Variable (1) Alt. Herding (2) Alt. Volatility (3) Non-Financial (4) Large Firms (5) Crisis Period  
Coef. (S.E.) Coef. (S.E.) Coef. 

HERD(t-1) 0.0167** (0.0082) 0.0203** (0.0085) 0.0195*** 
SIZE -0.0156*** (0.0036) -0.0189*** (0.0038) -0.0174*** 
TURN 0.3045*** (0.0267) 0.2987*** (0.0278) 0.3189*** 
RET(t-1) -0.0456*** (0.0145) -0.0523*** (0.0156) -0.0467*** 
LEV 0.0198* (0.0112) 0.0167 (0.0118) 0.0234** 
AGE -0.0031** (0.0016) -0.0028* (0.0017) -0.0035** 

Observations 61,152 
 

61,152 
 

53,067 
R-squared 0.1897 

 
0.2134 

 
0.1934 

F-statistic 178.45*** 
 

201.23*** 
 

184.67*** 
*Note: *, *, *** denote significance at 10%, 5%, and 1% levels respectively. All specifications include firm and time fixed effects with Driscoll-Kraay robust standard errors. 

 
The alternative herding measure (Column 1) employs different aggregation methodology based on Return 

Dispersion Around Market Mean (RAMM) approaches, yet produces coefficient estimates (0.0167) that remain 
statistically significant and economically meaningful. This finding suggests that results are not sensitive to specific 
herding measurement techniques whilst maintaining theoretical consistency across methodological variations. 

Alternative volatility specifications (Column 2) utilise exponential weighted moving average (EWMA) 
approaches rather than GARCH-based conditional volatility measures. The coefficient estimate of 0.0203 
demonstrates remarkable similarity to baseline specifications, indicating that herding-volatility relationships 
persist across different volatility measurement methodologies. 

The non-financial sample analysis (Column 3) addresses potential concerns regarding distinctive regulatory 
frameworks and business model characteristics within financial sector firms. The coefficient estimate of 0.0195 
closely matches baseline specifications whilst achieving enhanced statistical significance, suggesting that herding-
volatility relationships characterise both financial and non-financial firms within Vietnamese equity markets. 

Large firm subsample analysis (Column 4) examines whether herding effects concentrate among smaller 
capitalisation firms or extend across the full size distribution. The coefficient estimate of 0.0134 indicates that 
herding behaviour influences volatility even among larger firms, although with reduced magnitude compared to 
full sample estimates. This finding suggests that firm size moderates herding effects whilst not eliminating the 
fundamental relationship entirely. 

Crisis period analysis (Column 5) focuses upon 2007-2009 observations to examine whether herding-volatility 
relationships intensify during periods of market stress. The coefficient estimate of 0.0298 substantially exceeds 
baseline specifications, indicating that herding behaviour exerts enhanced influence upon volatility transmission 
during crisis periods when information processing becomes more challenging and emotional factors dominate 
rational analysis. 

Additional robustness testing examines temporal stability through rolling window estimation and structural 
break analysis. The relationship remains statistically significant across different time periods whilst exhibiting 
some coefficient variation that correlates with market volatility cycles and regulatory changes within Vietnamese 
financial markets. 
 

 



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5. Discussion and Conclusion 
5.1. Discussion of Findings 

The empirical analysis provides compelling evidence supporting the central hypothesis that herding behaviour 
significantly influences volatility transmission mechanisms within Vietnamese equity markets. The consistent 
positive relationship between lagged herding measures and current volatility levels, robust across multiple 
econometric specifications and sample configurations, demonstrates that collective investor behaviour generates 
substantial impacts upon market risk characteristics beyond traditional fundamental and technical factors. 

The coefficient magnitude of approximately 0.019-0.025 across main specifications indicates economically 
meaningful effects where one standard deviation increases in herding behaviour associate with 3-4% increases in 
conditional volatility relative to sample means. These effect sizes compare favourably with existing international 
evidence whilst reflecting Vietnamese market characteristics including retail investor predominance, limited 
institutional arbitrage mechanisms, and evolving regulatory frameworks that potentially amplify behavioural 
effects. 

The finding that herding behaviour's impact intensifies during crisis periods (coefficient increasing to 0.0298) 
provides valuable insights into volatility transmission mechanisms during market stress. This result aligns with 
theoretical predictions that informational processing constraints and emotional factors become more pronounced 
during uncertain periods, leading to enhanced reliance upon social information sources and collective behaviour 
patterns. The crisis period amplification suggests that herding behaviour represents a crucial transmission channel 
through which market stress propagates across individual securities and broader market segments. 

The asymmetric effects across firm size classifications revealed through subsample analysis illuminate 
important heterogeneity in herding susceptibility. Smaller capitalisation firms demonstrate stronger herding 
effects compared to larger counterparts, consistent with theoretical predictions regarding information 
asymmetries, analyst coverage limitations, and liquidity constraints that characterise smaller firms. However, the 
persistence of significant herding effects even among larger firms suggests that behavioural factors influence 
market dynamics across the complete size spectrum rather than concentrating exclusively among informationally 
disadvantaged securities. 

Sectoral analysis reveals distinctive patterns where technology and financial services firms exhibit enhanced 
vulnerability to herding-induced volatility compared to traditional manufacturing industries. These findings align 
with theoretical frameworks emphasising information processing complexities and valuation uncertainties that 
characterise growth-oriented and knowledge-intensive sectors. The sectoral heterogeneity suggests that portfolio 
managers and risk assessment professionals should incorporate sector-specific behavioural factors when evaluating 
Vietnamese equity market exposures. 

The robust negative relationship between firm size and volatility provides additional validation of market 
microstructure theories whilst highlighting Vietnamese market characteristics. The coefficient magnitude of 
approximately -0.017 indicates substantial economic significance where doubling firm size associates with 
meaningful volatility reductions. This relationship suggests that size-based investment strategies may provide 
effective risk management tools within Vietnamese equity markets, particularly during periods of heightened 
herding behaviour. 

Trading intensity's strong positive correlation with volatility (coefficients ranging 0.32-0.38) confirms that 
liquidity and information processing activities generate substantial price fluctuation impacts. The relationship 
magnitude suggests that periods of intense trading activity coincide with enhanced uncertainty and information 
incorporation processes that amplify volatility transmission mechanisms. This finding possesses important 
implications for execution strategies and market timing decisions within Vietnamese equity markets. 

The temporal persistence of herding effects, demonstrated through lagged variable specifications and dynamic 
panel data approaches, indicates that collective behaviour patterns exhibit momentum characteristics that extend 
beyond immediate time periods. This persistence suggests that herding behaviour creates feedback loops where 
current collective actions influence subsequent investor decisions, potentially generating sustained deviations from 
fundamental value relationships. 
 

5.2. Conclusion, Implications, and Limitations 
This study contributes substantially to the behavioural finance literature by providing comprehensive empirical 

evidence regarding herding behaviour's impact upon volatility transmission within an emerging market context. 
The findings advance theoretical understanding of collective investor behaviour whilst offering practical insights 
for investment professionals, risk managers, and regulatory authorities operating within developing financial 
market environments. 

The research demonstrates that herding behaviour represents a significant determinant of volatility patterns 
within Vietnamese equity markets, with effects that persist across different econometric methodologies, sample 
configurations, and temporal periods. The relationship exhibits theoretically consistent patterns where herding 
intensity correlates positively with market stress levels, negatively with firm size and age, and varies 
systematically across sectoral classifications reflecting information processing complexities and institutional 
characteristics. 
 

5.3. Theoretical Implications 
The findings provide empirical support for behavioural finance theories emphasising collective investor 

behaviour's role in market dynamics whilst challenging traditional efficient market assumptions. The evidence 
suggests that herding behaviour operates through multiple channels including informational cascades, reputation-
based strategies, and emotional contagion effects that generate persistent deviations from fundamental value 
relationships. 

The asymmetric effects across firm characteristics and market conditions illuminate important heterogeneity in 
behavioural factor influences that theoretical models should incorporate. The temporal persistence of herding 



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effects suggests that collective behaviour creates momentum patterns requiring dynamic rather than static 
theoretical frameworks to capture adequately. 

The crisis period amplification provides valuable insights into market stress transmission mechanisms, 
suggesting that behavioural factors become more prominent when traditional information processing capabilities 
face constraints. These findings contribute to understanding financial contagion processes and systemic risk 
propagation patterns within emerging market contexts. 

 
5.4. Practical Implications 

Portfolio management strategies should incorporate herding behaviour measures as complementary risk 
factors alongside traditional fundamental and technical indicators. The sector-specific and size-based heterogeneity 
suggests that behavioural factor loadings should vary across different asset categories and market segments when 
constructing optimal portfolios. 

Risk management frameworks require enhanced attention to collective behaviour patterns, particularly during 
periods of market stress where herding effects intensify substantially. The findings suggest that traditional 
volatility models may underestimate risk during periods of heightened herding behaviour, necessitating 
behavioural factor adjustments in risk assessment procedures. 

Market timing strategies may benefit from herding behaviour indicators that provide early warning signals 
regarding volatility regime changes. The lagged relationship between herding measures and volatility suggests 
that collective behaviour patterns possess predictive content for subsequent market risk characteristics. 
 

5.5. Policy Implications 
Regulatory authorities should consider herding behaviour impacts when designing market stability policies and 

intervention strategies. The evidence suggests that collective investor behaviour can amplify volatility 
transmission and potentially threaten financial stability during crisis periods, warranting proactive regulatory 
responses. 

Investor education programmes focusing upon behavioural biases and collective decision-making processes 
may help mitigate excessive herding behaviour whilst promoting more efficient price discovery mechanisms. The 
findings suggest particular attention to retail investor education given their predominance within Vietnamese 
equity markets. 

Market structure reforms addressing information dissemination, analyst coverage, and institutional investor 
participation may reduce herding behaviour intensity whilst improving overall market efficiency. The size-based 
asymmetries suggest that enhanced support for smaller firm information production could generate broader market 
stability benefits. 
 

5.6. Research Limitations and Future Directions 
Several limitations constrain the generalisability and interpretation of study findings. The analysis focuses 

exclusively upon Vietnamese equity markets, limiting direct applicability to other emerging market contexts with 
different institutional characteristics and investor compositions. Future research should examine herding-volatility 
relationships across multiple emerging market jurisdictions to assess generalisability and identify common 
behavioural patterns. 

The herding behaviour measurement approach, whilst established within existing literature, represents only 
one methodological framework for capturing collective investor behaviour. Alternative measurement strategies 
incorporating social media sentiment, fund flow patterns, or network analysis approaches may provide additional 
insights into behavioural transmission mechanisms. 

The study period concludes in 2017, potentially missing important developments in Vietnamese financial 
markets including increased foreign institutional participation, regulatory modernisation, and technological 
advancement in trading platforms. Extended analysis incorporating more recent data would enhance 
understanding of temporal evolution in herding behaviour patterns. 

Future research directions should examine herding behaviour's interaction with other behavioural factors 
including momentum effects, contrarian strategies, and attention-driven trading patterns. The investigation of 
herding behaviour within specific market segments such as initial public offerings, dividend announcements, or 
earnings surprises may provide additional insights into behavioural finance mechanisms. 

Cross-country comparative analysis examining herding behaviour differences across emerging markets with 
varying institutional development levels, regulatory frameworks, and cultural characteristics would contribute 
valuable insights into behavioural factor determinants and policy implications. Additionally, investigation of 
herding behaviour's impact upon market efficiency measures and price discovery processes represents important 
areas for continued research development. 
 

Acknowledgments:  
I would like to express my sincere gratitude to Dr. Hoang Vu Hiep for his invaluable guidance and inspiration 
throughout this research. His expertise, insights, and unwavering support have been instrumental in shaping the 
direction and quality of this study. I am deeply appreciative of his generosity in sharing his time, knowledge, and 
network, which have greatly contributed to the success of this research. His mentorship and commitment to 
academic excellence have not only enriched the quality of this work but have also had a profound impact on my 
personal and professional growth. 
 

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