# Growth enterprise market in Hong Kong: Efficiency evolution and long memory in return and volatility

Trang Nguyen (James Cook University, Townsville, Australia)
Taha Chaiechi (James Cook University, Townsville, Australia)
Lynne Eagle (James Cook University, Townsville, Australia)
David Low (Charles Darwin University, Sydney, Australia)

ISSN: 2515-964X

Article publication date: 7 August 2019

Issue publication date: 11 February 2020

## Abstract

### Purpose

Growth enterprise market (GEM) in Hong Kong is acknowledged as one of the world’s most successful examples of small and medium enterprise (SME) stock market. The purpose of this paper is to examine the evolving efficiency and dual long memory in the GEM. This paper also explores the joint impacts of thin trading, structural breaks and inflation on the dual long memory.

### Design/methodology/approach

State-space GARCH-M model, Kalman filter estimation, factor-adjustment techniques and fractionally integrated models: ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH are adopted for the empirical analysis.

### Findings

The results indicate that the GEM is still weak-form inefficient but shows a tendency towards efficiency over time except during the global financial crisis. There also exists a stationary long-memory property in the market return and volatility; however, these long-memory properties weaken in magnitude and/or statistical significance when the joint impacts of the three aforementioned factors were taken into account.

### Research limitations/implications

A forecasts of the hedging model that capture dual long memory could provide investors further insights into risk management of investments in the GEM.

### Practical implications

The findings of this study are relevant to market authorities in improving the GEM market efficiency and investors in modelling hedging strategies for the GEM.

### Originality/value

This study is the first to investigate the evolving efficiency and dual long memory in an SME stock market, and the joint impacts of thin trading, structural breaks and inflation on the dual long memory.

## Citation

Nguyen, T., Chaiechi, T., Eagle, L. and Low, D. (2020), "Growth enterprise market in Hong Kong: Efficiency evolution and long memory in return and volatility", Journal of Asian Business and Economic Studies, Vol. 27 No. 1, pp. 19-34. https://doi.org/10.1108/JABES-01-2019-0009

## Publisher

:

Emerald Publishing Limited

Copyright © 2019, Trang Nguyen, Taha Chaiechi, Lynne Eagle and David Low

## 1. Introduction

As a Special Administrative Region of the People’s Republic of China with high degree of autonomy in political and economic systems, Hong Kong is renowned for its extent of trade openness and dynamic economic structure. Over the last seven decades, the economic success of Hong Kong is undisputable due to the fact that its economy has been experiencing structural transformation from a regional hub for industrial manufacturing to a major international financial centre. This successful transformation is largely attributable to the liberal economic policies, effective corporate governance, and free and transparent flow of information.

Being a trade gateway to Mainland China and having strong business relations with many other Asian economies, Hong Kong is strategically situated in a high growth region and has now become one of the world’s most unfettered economies. According to WTO (2018) and UNCTAD (2018), Hong Kong is the world’s seventh largest exporter of merchandise trade and the world’s second largest investor and host. This service-oriented economy is also remarked as the fourth greatest foreign exchange market in the world and the biggest offshore RMB (Renminbi, the Chinese currency) clearing centre (BIS, 2018). Furthermore, Hong Kong has remarkably weathered several critical shocks since the 2000s such as global financial crisis (GFC), stock market crashes, Chinese market turmoil, typhoons, chaos and the transfer of sovereignty from London to Beijing (Scobell and Gong, 2017).

For decades, Hong Kong has striven to become the third leading global financial centre, and Hong Kong Stock Exchange (HKEX) has developed into the world’s sixth largest stock market and the third in Asia, providing opportunities for several multinational companies and conglomerates to raise capital. In 1999, HKEX introduced the growth enterprise market (GEM) as a second board, also known as an alternative market to the main market, to offer a fund-raising mechanism and a credible identity for small and medium enterprises (SMEs), who are ineligible to be listed on the main board. The GEM’s operation is grounded on the two principles of “buyer beware” and “let the market decide”, along with a comprehensive disclosure regime. The GEM follows rules and regulations designed to foster a practice of self-compliance by the listed enterprises, sponsors and market makers in the discharge of their responsibilities.

Compared to the Main Board of HKEX, the GEM adheres to less stringent rules and regulations, lower requirements for listing and information disclosure, and holds a narrower investor base and higher investment risk. The GEM is operating under the sponsor-driven model which involves the participation of sponsor and market maker. The sponsor is a qualified advising agent approved by HKEX to ensure the quality of listing applicants. The market maker or liquidity provider, who is a member of HKEX, trades the listed securities to boost the market liquidity. Furthermore, another important characteristic of the GEM is that it exhibits a higher under-pricing level of initial public offerings (IPOs) than that of the Main Board. Vong and Zhao (2008) showed that such a high level of IPO under pricing (approximately 20 per cent) in the GEM is attributable to the ex post volatility of after-market returns, the timing effects and the geographic locations (i.e. H shares[1]). On the other hand, the under pricing of IPOs in ChiNext, which is a SME stock market in China, is driven by offline oversubscription, issue size, market momentum (Deng and Zhou, 2015), the ongoing litigation risk and the trademark infringement risk (Hussein et al., 2019).

### 5.3 Modelling long memory in return and volatility

As mentioned previously, the GEM exposed a high degree of volatility persistence, a further examination of long-memory pattern in both return and volatility series of the GEM is desirable. To model long memory in return and volatility, long-memory parameters in the mean and variance of return series were estimated by the following three models: ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH. To examine the joint effects of structural breaks, thin trading and inflation on long memory, unadjusted (rt) and adjusted returns ( r t d , r t d b , r t d b i ) were sequentially fit into these models. Initially, to obtain minimum values of Akaike information criteria, lag 2 was selected for the AR and MA terms, and lag 1 was selected for the ARCH and GARCH terms.

Table V reports the estimation results of the three indicated long-memory models. In the ARFIMA(2, dm, 2)–FIGARCH(1, dv, 1) model estimation, the dm parameters using raw returns (rt) and de-thinned returns ( r t d ) weakened in the level of significance (from 5 to 10 per cent) and magnitude (from 0.138 to 0.112). As the return series was further adjusted for structural breaks ( r t d b ) and inflation ( r t d b i ), the dm parameters further declined to 0.103 and 0.100, respectively. The dv parameters also decreased in the level of significance (from 5 to 10 per cent) and magnitude (from 0.517 to 0.481, 0.468 and 0.457) when the return series was sequentially adjusted for thin trading, structural breaks and inflation. Accordingly, the results showed evidence of the long-range persistence in the GEM return and volatility, and the persistence reducing effect of thin trading, structural breaks and inflation.

Similarly, the estimations of ARFIMA(2, dm, 2)–FIAPARCH(1, γ, δ, dv, 1) model revealed that the magnitude and level of significance of dm and dv parameters reduced steadily once the joint effects of thin trading, structural breaks and inflation were accounted for. In particular, the dm parameter declined from 0.187 (1 per cent) to lower corresponding values of 0.153 (5 per cent), 0.112 (10 per cent) and 0.110 (10 per cent); and the dv parameter also experience a decrease from 0.456 (1 per cent) to 0.442 (5 per cent), 0.437 (5 per cent) and 0.418 (10 per cent). Since dm and dv parameters remained statistically significant after controlling for the three factors, the GEM exhibited long memory in both return and volatility series, which is consistent with the estimation results of ARFIMA(2, dm, 2)–FIGARCH(1, dv, 1) model. Moreover, the γ parameter was significant at 5 per cent and positive (0.287), suggesting that the negative events (such as GFC and Avian Influenza, see Table II) can induce higher volatility in the GEM than the positive events.

The estimation results of ARFIMA(2, dm, 2)–HYGARCH(1, dv, 1) model further confirmed the presence of dual long memory in return and volatility of the GEM and the long memory reducing effect of thin trading, structural breaks and inflation. In particular, the level of significance of dm and dv parameters weakened from 1 to 5 per cent and 10 per cent after the factors adjustments. The degree of dm(dv) parameters also fell from 0.124 (0.580) to lower corresponding values of 0.117 (0.472), 0.103 (0.466), and 0.100 (0.454). Table V also shows the post-estimation diagnostics in Panel C, indicating no significant serial correlation and heteroscedasticity in the standardised residuals and no sign of model misspecification for the GEM.

In addition, it is worth noting that in all three dual long-memory models, the estimates of dm and dv parameters using the returns adjusted for the three factors ( r t d b i ) fell within the interval of [0; 0.5]. This implies a stationary long memory in the GEM’s return and volatility series, suggesting that the return and volatility series will revert to their means in the long term. Otherwise stated, the current market index is strongly dependent on distant past market indexes and it will revert to its long-term equilibrium after the effect of external events has disappeared. Furthermore, Coakley et al. (2008) and Mann (2012) have placed emphasis on the important role of long memory in hedging effectiveness using various hedging models to estimate the optimal hedging ratio such as ordinary least squared model, error-correction model and fractionally integrated GARCH-type models. Therefore, our finding is highly relevant to investors in formulating their trading strategies and risk management in the sense that the dual long memory should be integrated into the hedging model for the GEM in order to estimate the optimal hedging ratio for this market.

## 6. Conclusion and future research

The target of this paper is to explore the evolution of weak-form market efficiency and the joint impacts of thin trading, structural breaks and inflation on long memory of return and volatility in the GEM in Hong Kong during 2003–2017. Various econometric techniques and models were employed for the empirical analysis including multiple breakpoints test to identify potential structural breaks, state-space GARCH-M model with the Kalman filter estimation to depict the evolution of weak-form efficiency, factors adjustment techniques to control the impacts of thin trading, breaks and inflation on the dual long memory and a set of fractionally integrated models (ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH) to examine the long memory in return and volatility.

The results determined that the GEM is still inefficient in the weak form, yet has a tendency towards efficiency over time except during the GFC. This tendency is observed to keep abreast of the gradual increase in market capitalisation and trading turnover of the GEM since establishment. Moreover, this favourable tendency could be attributed to several institutional reforms undertaken by the HKEX authorities during the pre- and post-GFC such as improvements in system infrastructure for trading, settlement and information dissemination, reduction in transaction fees and measures to manage risks and market volatility (as described in the Appendix). Accordingly, the reforms undertaken by the exchange authorities so far appear to be effective in fostering the GEM towards weak-form efficiency.

The results also revealed the presence of stationary long memory in return and volatility series of the GEM. However, these dual long-memory properties weakened in magnitude and/or statistical significance when the returns are adjusted for thin trading and/or structural breaks. As the returns are further adjusted for inflation, the degree of long-range persistence in return and volatility series further declines. Therefore, should one fails to control for these factors, the corresponding true values would be overestimated. Additionally, the estimation of FIAPARCH process also suggests that the negative events (such as crisis and market turbulence) inflict higher volatility in the GEM than positive events. The evidence of dual long memory in the GEM can be used to assist investors in formulating their trading strategies and risk management wherein the dual long memory should be incorporated into the hedging model for the GEM to estimate the optimal hedging ratio for this market.

And finally, this paper is intended to be a proof-of-concept to provide sufficient evidence of methodological viability, which can then be used in larger scale research or replicated in new settings. It is also worthwhile to conduct an event study to assess the impacts of the GEM market development indicators and institutional reforms on the evolution towards efficiency of the GEM. Furthermore, a forecasts of the hedging model that capture dual long memory could provide investors further insights into risk management of investments in the GEM.

## Figures

#### Figure 1

GEM’s market capitalisation (USD billion)

#### Figure 3

Evolution of market efficiency in GEM

## Table I

Descriptive statistics of the GEM’s returns

Obs Mean Median Maximum Minimum SD Skewness Kurtosis Jarque–Bera
3,601 −0.0004 0.0002 0.2707 −0.1584 0.0143 0.0480 53.84 387,761*
Q(10) Q(20) Q2(10) Q2(20) ARCH(10) ARCH(20)
113.59* 145.59* 912.01* 930.27* 70.62* 36.10*

Note: *Indicates that Jarque–Bera statistic is significant at 1 per cent

## Table II

Structural breakpoints

Breakpoint Corresponding events Regime period c j ^
7 April 2006 Permission for Chinese investors to invest in Hong Kong stock markets 3 March 2003–6 April 2006 0.0004
7 April 2006–28 October 2008 −0.0022
29 October 2008 Global financial crisis 29 October 2008–3 January 2011 0.0016
4 January 2011 H5N1 infections in humans (Avian Influenza) 4 January 2011–16 April 2013 −0.0014
17 April 2013 Kwai Tsing dock strike (the world’s third busiest port) 17 April 2013–25 June 2015 0.0014
26 June 2015 Chinese stock market turbulence 26 June 2015–29 September 2017 −0.0020

Note: c j ^ represents the estimated mean returns for each regime

## Table III

Unit root tests

Test Option Test statistic Regime 1 Regime 2 Regime 3 Regime 4 Regime 5 Regime 6
ADF C −25.01* −20.93* −20.68* −21.16* −19.68* −20.51*
C&T −25.03* −21.46* −20.74* −21.29* −19.74* −20.54*
PP C −25.17* −21.61* −20.78* −21.42* −19.81* −20.51*
C&T −25.18* −21.74* −20.78* −21.46* −19.85* −20.57*
NP C M Z α d −59.18* −71.04* −203.03* −276.29* −262.41* −10.82**
M Z t d −5.38* −5.94* −10.07* −11.75* −11.45* −2.26**
MSBd 0.09* 0.08* 0.05* 0.04* 0.04* 0.21**
M P T d 0.55* 0.38* 0.13* 0.09* 0.09* 2.53**
NP C&T M Z α d −265.45* −86.47* −266.04* −276.72* −262.49* −37.93*
M Z t d −11.48* −6.55* −11.52* −11.76* −11.46* −4.35*
MSBd 0.04* 0.08* 0.04* 0.04* 0.04* 0.11*
M P T d 0.44* 1.15* 0.37* 0.34* 0.35* 2.41*

Notes: C denotes as constant; C&T denotes as constant and trend; M Z α d , M Z t d , MSBd and M P T d represents the four test statistics of the NP test. *,**Indicates that test statistic is significant at 1 and 5 per cent, respectively

## Table IV

State-space GARCH-M(1, 1) model estimation

Coefficient SE Robust-SE t-value
β0 0.00 0.00 0.00 −0.67
β1 (final state) 0.11 0.02 0.02 5.86*
β2 1.97 9.78 9.75 0.20
α0 0.00 0.00 0.00 2.28**
α1(ARCH) 0.17 0.02 0.04 4.88*
α2(GARCH) 0.79 0.02 0.04 18.00*
α1+α2 0.97
Log-likelihood 11,039.77
AIC −6.13
Diagnostic statistics
Q(10) 37.69 Q(20) 53.64
Q2(10) 3.84 Q2(10) 5.42
ARCH(10) 0.39 ARCH(20) 0.27

Notes: *,**Indicates that test statistic is significant at 1 and 5 per cent, respectively

## Table V

Long-memory model estimations

ARFIMA(2, dm, 2)–FIGARCH(1, dv, 1) ARFIMA(2, dm, 2)–FIAPARCH(1, γ, δ, dv, 1) ARFIMA(2, dm, 2)–HYGARCH(1, dv, 1)
Panel A: mean equation
μ 0.00 0.00 0.00
dm(rt) 0.138** 0.187* 0.124*
d m ( r t d ) 0.112*** 0.153** 0.117**
d m ( r t d b ) 0.103*** 0.112*** 0.103**
d m ( r t d b i ) 0.100*** 0.110*** 0.100***
Φ1 −0.860*** −1.630*** −0.859***
Φ2 −0.969*** −0.989*** −0.968***
Θ1 0.871*** 1.632*** 0.869***
Θ2 0.976*** 0.991*** 0.976***
Panel B: variance equation
ω 0.132* 5.379 0.212*
dv(rt) 0.517** 0.456* 0.580*
d v ( r t d ) 0.481** 0.442** 0.472**
d v ( r t d b ) 0.468*** 0.437** 0.466***
d v ( r t d b i ) 0.457*** 0.418*** 0.454***
α1 −0.184 −0.090
β1 0.086 0.215 0.240
ϕ1 −0.039
γ 0.287**
δ 1.300***
Logλ −0.115*
Panel C: diagnostics
Log-likelihood 11,037 11,070 11,057
AIC −6.12 −6.14 −6.14
SIC −6.11 −6.12 −6.12
Q(10) 6.84 7.15 5.96
Q(20) 12.00 12.77 11.43
Q2(10) 2.86 3.10 2.68
Q2(20) 3.81 3.81 3.52
ARCH(5) 0.11 0.20 0.05
ARCH(10) 0.28 0.31 0.27
P(40) 186.58* 151.66* 160.63*

Notes: μ and ω are the constants for the mean and variance model; dm(rt), d m ( r t d ) , d m ( r t d b ) and d m ( r t d b i ) represent parameters of long memory in return using raw returns, de-thinned returns, returns adjusted for thin trading and breaks and returns adjusted for thin trading, breaks and inflation, respectively; dv(rt), d v ( r t d ) , d v ( r t d b ) and d v ( r t d b i ) represent parameters of long memory in volatility using the aforementioned set of returns; Φ1 and Φ2 represent AR(1) and AR(2) terms; Θ1 and Θ2 represent MA(1) and MA(2) terms; α1(ϕ1) and β1 represent ARCH(1) and GARCH(1) terms; γ and δ represent asymmetry parameter and power terms; λ denotes amplitude parameter; P(40) indicates the Pearson goodness of fit test for 40 cells; due to space limitation, only model estimations using r t d b i were fully displayed. *,**,***Indicates the t-statistic is significant at 1, 5 and 10 per cent, respectively

## Table AI

HKEX’s institutional reforms to improve operating efficiency of stock markets

Date Event
8 August 2005 HKEX introduced several improvements in clearing settlement services and nominee services, including first, a new automated mechanism enabling fund transfer through the real-time gross settlement system; second, extension of due date for corporate action instructions; and third, a reduction in handling charges for scrip fee concessions
1 October 2006 New measures to manage risks arising from securities margin trading took effect. These measures comprise: first, limits on repledging; second, amendments to haircut percentage of selected financial resources rules; and third, improved transparency by disclosure
Comprehensive guidance on marketing materials for listed structured products took effect. The guidance postulates that marketing materials should not be misleading or biased and should include relevant risk warnings
30 July 2007 HKEX accomplished the final phase of implementation of SDNet, which is an integrated network infrastructure for trading, clearing, settlement and information dissemination of securities and derivatives
5 December 2011 HKEX upgraded the third generation automatic order matching and execution system (AMS) to version 3.8. The processing capacity of the new system was increased from 3,000 orders per second to 30,000 and the response time was reduced to 2 ms from 0.15 s
30 September 2013 HKEX rolled out Orion Market Data (OMD) platform which provides low latency and remote distribution of market depth and products datafeed to meet diverse customer needs
01 November 2014 The 10% reduction in Securities and Futures Commission’s transaction fees took effect. The fees were cut for securities transactions (from 0.0030 to 0.0027%) and derivatives transactions (from HK$0.60 to HK$0.54)
22 August 2016 A volatility control mechanism was introduced to assure market integrity by preventing extreme price volatility stemming from significant trading errors or other unusual incidents

## Note

1.

H shares refer to the shares of firms that are incorporated in Mainland China and traded on the HKEX while A shares refer to the shares of Mainland China-based firms that are listed on the two Chinese stock exchanges, the Shanghai Stock Exchange and the Shenzhen Stock Exchange.

Table AI

## References

Abdmoulah, W. (2010), “Testing the evolving efficiency of Arab stock markets”, International Review of Financial Analysis, Vol. 19 No. 1, pp. 25-34.

Abrosimova, N., Dissanaike, G. and Linowski, D. (2005), “Testing weak-form efficiency of the Russian stock market”, working paper, Social Science Research Network (SSRN), Berlin, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=302287 (accessed 25 January 2018).

Antoniou, A., Ergul, N. and Holmes, P. (1997), “Market efficiency, thin trading and non-linear behaviour: evidence from an emerging market”, European Financial Management, Vol. 3 No. 2, pp. 175-190.

Bai, J. and Perron, P. (2003), “Computation and analysis of multiple structural change models”, Journal of Applied Econometrics, Vol. 18 No. 1, pp. 1-22.

Baillie, R., Bollerslev, T. and Mikkelsen, H. (1996), “Fractionally integrated generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, Vol. 74 No. 1, pp. 3-30.

Belkhouja, M. and Boutahar, M. (2009), “Structural change and long memory in the dynamic of US inflation process”, Computational Economics, Vol. 34 No. 2, pp. 195-216.

BIS (2018), “Annual report: promoting global monetary and financial stability”, Bank for International Settlements, Basel, available at: www.bis.org/about/areport/areport2018.pdf (accessed 2 June 2018).

Cappelli, C. and D’Elia, A. (2006), Long Memory and Structural Break Analysis of Environmental Time Series, University of Naples Federico II, Napoli, available at: www.old.sis-statistica.org/files/pdf/atti/Spontanee%202006_203-206.pdf (accessed 24 February 2018).

Cecchetti, S. and Debelle, G. (2006), “Has the inflation process changed?”, Economic Policy, Vol. 21 No. 46, pp. 312-352.

Charfeddine, L. and Khediri, B. (2016), “Time-varying market efficiency of the GCC stock markets”, Physica A: Statistical Mechanics and its Applications, Vol. 444 No. 2016, pp. 487-504.

Cheung, Y.W. (1993), “Tests for fractional integration: a Monte Carlo investigation”, Journal of Time Series Analysis, Vol. 14 No. 4, pp. 331-345.

Choi, K., Yu, W.-C. and Zivot, E. (2010), “Long memory versus structural breaks in modeling and forecasting realized volatility”, Journal of International Money and Finance, Vol. 29 No. 5, pp. 857-875.

Coakley, J., Dollery, J. and Kellard, N. (2008), “The role of long memory in hedging effectiveness”, Computational Statistics and Data Analysis, Vol. 52 No. 6, pp. 3075-3082.

Davidson, J. (2004), “Moment and memory properties of linear conditional heteroscedasticity models, and a new model”, Journal of Business & Economic Statistics, Vol. 22 No. 1, pp. 16-29.

Deng, Q. and Zhou, Z.-G. (2015), “Offline oversubscription, issue size, and market momentum: the driving forces for ChiNext IPOs’ initial underpricing”, Chinese Economy, Vol. 48 No. 2, pp. 114-129.

Dickey, D. and Fuller, W. (1979), “Distribution of the estimators for autoregressive time series with a unit root”, Journal of the American Statistical Association, Vol. 74 No. 366, pp. 427-431.

Emerson, R., Hall, S. and Zalewska-Mitura, A. (1997), “Evolving market efficiency with an application to some Bulgarian shares”, Economics of Planning, Vol. 30 No. 2, pp. 75-90.

Fama, E. (1970), “Efficient capital markets: a review of theory and empirical work”, The Journal of Finance, Vol. 25 No. 2, pp. 383-417.

Granger, C. and Hyung, N. (2004), “Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns”, Journal of Empirical Finance, Vol. 11 No. 3, pp. 399-421.

Granger, C. and Joyeux, R. (1980), “An introduction to long-memory time series models and fractional differencing”, Journal of Time Series Analysis, Vol. 1 No. 1, pp. 15-29.

Guermezi, F. and Boussaada, A. (2016), “The weak form of informational efficiency: case of Tunisian banking sector”, Ecoforum Journal, Vol. 5 No. 1, pp. 22-34.

Harrison, B. and Moore, W. (2012), “Stock market efficiency, non-linearity, thin trading and asymmetric information in MENA stock markets”, Economic Issues, Vol. 17 No. 1, pp. 77-93.

Hosking, J. (1981), “Fractional differencing”, Biometrika, Vol. 68 No. 1, pp. 165-176.

Hussein, M., Zhou, Z.-G. and Deng, Q. (2019), “Does risk disclosure in prospectus matter in ChiNext IPOs’ initial underpricing?”, Review of Quantitative Finance and Accounting, Vol. 52 No. 201, pp. 1-23.

IFC (2013), Closing the Credit Gap for Formal and Informal Micro, Small, and Medium Enterprises, International Finance Corporation, Washington, DC, available at: www.ifc.org/wps/wcm/connect/4d6e6400416896c09494b79e78015671/Closing+the+Credit+Gap+Report-FinalLatest.pdf?MOD=AJPERES

Inclan, C. and Tiao, G. (1994), “Use of cumulative sums of squares for retrospective detection of changes of variance”, Journal of the American Statistical Association, Vol. 89 No. 427, pp. 913-923.

Jefferis, K. and Smith, G. (2005), “The changing efficiency of African stock markets”, South African Journal of Economics, Vol. 73 No. 1, pp. 54-67.

Kalman, R. and Bucy, R. (1961), “New results in linear filtering and prediction theory”, Journal of Basic Engineering, Vol. 83 No. 1, pp. 95-108.

Lagoarde-Segot, T. and Lucey, B. (2008), “Efficiency in emerging markets – evidence from the MENA region”, Journal of International Financial Markets, Institutions and Money, Vol. 18 No. 1, pp. 94-105.

Li, B. and Liu, B. (2012), “A variance-ratio test of random walk in international stock markets”, The Empirical Economics Letters, Vol. 11 No. 8, pp. 775-782.

Lim, K.-P. and Brooks, R. (2009), “Price limits and stock market efficiency: evidence from rolling bicorrelation test statistic”, Chaos, Solitons & Fractals, Vol. 40 No. 3, pp. 1271-1276.

Lo, A. and MacKinlay, C. (1990), “An econometric analysis of nonsynchronous trading”, Journal of Econometrics, Vol. 45 Nos 1-2, pp. 181-211.

Mann, J. (2012), “The role of long memory in hedging strategies for Canadian commodity futures”, Journal of Agribusiness, Vol. 30 No. 2, pp. 201-224.

Ng, S. and Perron, P. (2001), “Lag length selection and the construction of unit root tests with good size and power”, Econometrica, Vol. 69 No. 6, pp. 1519-1554.

Ngene, G., Tah, K. and Darrat, A. (2017), “Long memory or structural breaks: some evidence for African stock markets”, Review of Financial Economics, Vol. 34 No. 2017, pp. 61-73.

Peterhoff, D., Romeo, J. and Calvey, P. (2014), “Towards better capital market solutions for SME financing”, available at: www.oliverwyman.com/content/dam/oliver-wyman/global/en/files/insights/financial-services/2014/July/FINAL3_BetterCapitalMarketMechanismsSMEs.pdf (accessed 15 August 2016).

Phillips, P.C. and Perron, P. (1988), “Testing for a unit root in time series regression”, Biometrika, Vol. 75 No. 2, pp. 335-346.

Rockinger, M. and Urga, G. (2000), “The evolution of stock markets in transition economies”, Journal of Comparative Economics, Vol. 28 No. 3, pp. 456-472.

Scobell, A. and Gong, M. (2017), Whither Hong Kong?, RAND Corporation, Santa Monica, CA.

Shaker, A.T.M. (2013), “Testing the weak-form efficiency of the Finnish and Swedish stock markets”, European Journal of Business and Social Sciences, Vol. 2 No. 9, pp. 176-185.

Tse, Y.-K. (1998), “The conditional heteroscedasticity of the yen-dollar exchange rate”, Journal of Applied Econometrics, Vol. 13 No. 1, pp. 49-55.

UNCTAD (2018), “World investment report: investment and new industrial policies”, United Nations Conference on Trade and Development, Geneva, available at: https://unctad.org/en/PublicationsLibrary/wir2018_en.pdf (accessed 15 January 2019).

Vong, A. and Zhao, N. (2008), “An examination of IPO underpricing in the growth enterprise market of Hong Kong”, Applied Financial Economics, Vol. 18 No. 19, pp. 1539-1547.

WTO (2018), “World trade statistical review”, World Trade Organisation, available at: www.wto.org/english/res_e/statis_e/wts2018_e/wts2018_e.pdf

## Corresponding author

Trang Nguyen can be contacted at: thiminhtrang.nguyen2@my.jcu.edu.au