Growth enterprise market in Hong Kong: Efficiency evolution and long memory in return and volatility
Journal of Asian Business and Economic Studies
ISSN: 2515964X
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
Statespace GARCHM model, Kalman filter estimation, factoradjustment 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 weakform inefficient but shows a tendency towards efficiency over time except during the global financial crisis. There also exists a stationary longmemory property in the market return and volatility; however, these longmemory 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.
Keywords
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. 1934. https://doi.org/10.1108/JABES0120190009
Publisher
:Emerald Publishing Limited
Copyright © 2019, Trang Nguyen, Taha Chaiechi, Lynne Eagle and David Low
License
Published in Journal of Asian Business and Economic Studies. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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 serviceoriented 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 fundraising 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 selfcompliance 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 sponsordriven 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 underpricing 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 aftermarket 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).
Hong Kong Special Administrative Region Government has longrecognised SMEs as the true economic powerhouse of Hong Kong’s economy. Nevertheless, their growth is obstructed by a credit gap of US$10.2bn (IFC, 2013) due to lack of transparency, low credit rating and high financial risk associated with small businesses. In the SME financing landscape, the GEM emerges as an effective mechanism for SMEs to raise longterm capital. In fact, since its establishment in 1999–2016, the GEM has successfully raised around US$22.7bn for SMEs through IPOs and secondary public offerings. In 2015, the funds raised through this market peaked at US$2.8bn, which is equivalent to a significant 27.9 per cent of the SME credit gap in Hong Kong. Therefore, the GEM is considered one of the world’s most successful examples of SME stock markets (Peterhoff et al., 2014), making it attractive for researchers.
Even though the GEM plays an important role in closing the SME credit gaps in Hong Kong, it has received limited attention. Specifically, market efficiency in this alternative markets and their important roles in fostering economic growth have largely been neglected in the literature. Since the GEM is at an early stage of development, it is hardly conceivable for it to be efficient since it takes time for the price discovery process to fully incorporate new information. However, as market participants become more sophisticated, and the regulatory environment and trading system become better developed over time, the degree of efficiency in such an SME market will gradually improve. Therefore, it is necessary to analyse the evolution of weakform efficiency rather than just addressing the matter of whether or not the market is efficient in the weak form. An analysis of efficiency evolution can reveal a potential tendency towards efficiency and cast some light on underlying causes.
Long memory, also known as longrange persistence, appears when the autocorrelation in return series decays hyperbolically through time. The presence of long memory is a source of market inefficiency and asset bubbles. Moreover, the degree of persistence in stock prices is also a key determinant of financial stability and can make portfolio allocation decisions sensitive to investment horizons. Although dual long memory in market return and volatility have been widely scrutinised in the finance literature, an abundance of studies neglects to account for the joint impacts of factors such as thin trading, structural breaks and inflation on the long memory. Neglecting these factors may lead to omittedvariable bias and spurious longmemory results.
To sum up, the GEM is recognised as a critical financing instrument for SMEs in Hong Kong and one of the world’s most successful examples of SME stock markets. However, very limited research has been dedicated to the GEM. In particular, there is a paucity of research on the GEM’s evolving market efficiency and dual longmemory components in the GEM’s return and volatility. Market efficiency plays significant role in promoting effective allocation of capital to productive investments and stimulating longterm economic growth. On the other hand, the presence of long memory in market return and/or volatility instigates market inefficiency and asset bubbles, leading to the ineffective allocation of capital in the economy. In addition, while dual long memory has been largely examined, a great deal of studies fails to control the joint impacts of thin trading, structural breaks and inflation. The joint impacts of these factors may induce biased longmemory estimate and distort investment decisions.
Consequently, in the absence of such attempts, this paper aims to examine the evolution of weakform efficiency and the joint impacts of thin trading, structural breaks and inflation on longmemory properties in both return and volatility of the GEM. The procedures of a statespace GARCHM model, Kalman filter estimation, factoradjustment techniques and a set of fractionally integrated models (ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH) are adopted for the empirical analysis.
To the best of the authors’ knowledge, this paper is the first attempt at exploring the evolving efficiency and long memory in return and volatility in a stock market for SMEs. Different from previous studies, this paper takes into account the joint effects of factors such as thin trading, structural breaks and inflation on the dual long memory.
2. Literature review
Following the random walk theory, Fama (1970) defines a market as efficient when new information is promptly and accurately reflected in its current prices. In the modern finance literature, market efficiency remains its importance for a favourable nexus between the stock market and economic growth by promoting optimal resources allocation in the economy (LagoardeSegot and Lucey, 2008). Fama classified market efficiency into three forms: weak, semistrong and strong. In this paper, we focus on the weakform version, which posits that succeeding price changes are unpredictable based on all the past trading information.
An abundance of efficiency studies has mainly focussed on testing whether a stock market is or is not weakform efficient, assuming that market efficiency remains unchanged over different stages of market development. For example, one can refer to studies of Li and Liu (2012), Shaker (2013) and Guermezi and Boussaada (2016). However, understanding the underlying factors that lead a market to become efficient is more essential. As such, the effect of some postulated factors on market efficiency has been examined by Antoniou et al. (1997), Abrosimova et al. (2005) and Lim and Brooks (2009). Using a nonoverlapping subsamples approach, they divided the sample into subsamples based on postulated factors such as improvements in the trading system, changes in legislative framework and the occurrence of financial turbulence. However, a major criticism of this approach lies in its assumption that the tendency towards efficiency takes the form of a discrete change in the underlying coefficient at the predetermined breakpoint.
Prompted by this concern, a body of research employing a timevarying parameter model to depict the evolution of market efficiency has begun to emerge. Emerson et al. (1997) were the first to propose the statespace GARCHM model with Kalman filter estimation to trace evolving market efficiency over time. In their model, timevarying autocorrelation coefficients are adopted to measure a continuous and smooth change in the behaviour of return series and thus the evolution of market efficiency is captured. If the market becomes more efficient through time, this smoothed coefficient will gradually converge towards zero and become insignificant. Following Emerson et al. (1997), several researchers such as Rockinger and Urga (2000), Jefferis and Smith (2005), Abdmoulah (2010) and Charfeddine and Khediri (2016) have proceeded to examine the evolution of stock market efficiency, applying their proposed model. These studies have widely focussed on the main boards of emerging stock markets at their early stage of development, for example, Poland, Hungary, Russia, Morocco, Egypt and Arab countries.
An extensive literature on long memory in stock market returns has begun to emerge since the 1990s. However, a great deal of longmemory studies fails to examine the joint impact of thin trading, structural breaks and inflation on long memory. There exists a number of studies reporting the effect of these factors individually on long memory. Lo and MacKinlay (1990) concluded that thin trading can cause spurious autocorrelations in return series that may result in biased long memory in the return series. Cheung (1993) postulated that a neglect of structural breaks in modelling long memory probably induces an overstated degree of volatility persistence. Longmemory pattern may be adulterated partially by the presence of structural breaks (Granger and Hyung, 2004). Cappelli and D’Elia (2006) documented that a stationary shortmemory process that is subject to structural breaks shows a hyperbolic decay in an autocorrelation structure and other properties of fractionally integrated processes. Cecchetti and Debelle (2006) investigated the inflation persistence in dominant industrial economies and noted that conditional on a break in the mean, the degree of inflation persistence is much smaller than ignoring the break. Belkhouja and Boutahar (2009) reported a lower estimate of long memory in US inflation after accounting for structural shifts. Recently, Ngene et al. (2017) showed that the longmemory estimates for inflationadjusted returns reduce in magnitude or in statistical significance.
3. Methodology
As noted previously, for newly established market such as the GEM, an investigation of the evolution towards efficiency is more relevant than just examining the matter of whether or not the market is efficient. Accordingly, a statespace nonlinear GARCHM model with Kalman filter was employed to examine the market efficiency evolution. The joint effects of structural breaks, thin trading and inflation on long memory in return and volatility were also determined (as failure to account for these factors may result in biased long memory estimates). Therefore, to avoid the possibly biased long memory induced by the aforementioned factors, the GEM return series was at first adjusted for thin trading and then accommodated for breaks and inflation using factoradjustment techniques. The adjusted returns series were sequentially fit into a set of the fractionally integrated models including ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH. The econometric techniques and models employed in this study are described in the following sections.
3.1 Multiple breakpoints test
To test for multiple structural breakpoints in the mean returns, Bai and Perron (2003) approach was used. The break dates are estimated using the regression with T periods and m potential breaks as follows:
In addition, to identify multiple structural breaks in the unconditional variance of returns (volatility breaks), the iterated cumulative sum of squares (ICSS) algorithm which introduced by Inclan and Tiao (1994) is used. Initially, the cumulative sum of squared observations from the beginning of the residual series (ε_{t}) obtained from the AR(1) process of the GEM return series (R_{2t}) to the kth point in time is determined as follows:
The statistic D_{k} is then defined as:
When plotting the D_{k} against k, it is a horizontal line. If there are volatility breaks, the statistic D_{k} will deviate from 0, otherwise, it will oscillate around 0. When the maximum absolute value of D_{k},
3.2 Statespace GARCHM model with Kalman filter
To illustrate the efficiency evolution, the statespace GARCHM(1, 1) model with Kalman filter was employed. This model allows not only for the timevarying dependency of return and volatility series on its first lagged value but also quantify the degree of volatility persistence and risk premium. It is presented in a dynamic system of space equation and state equations as follows:
Equation (5) is the space equation, where parameter β_{1t} represents the timevarying AR(1) coefficient and β_{2} parameter represent the risk premium. Equation (6) is the state equation estimating the conditional variance of return (h_{t}), which is a function of the ARCH term
3.3 Adjustment for thin trading, structural breaks and inflation
Following Harrison and Moore (2012) approach, the timevarying AR(1) coefficient and the residuals were extracted from the statespace AR(1) model to adjust the returns for thin trading. This model contains Equation (3), but without the conditional variance (h_{t}), and Equation (5), as stated above. The dethinned return series (
The unadjusted (r_{t}) and adjusted return series (
3.4 Fractionally integrated models
To model long memory in the returns and volatility, a joint model of ARFIMA (Granger and Joyeux, 1980; Hosking, 1981) and FIGARCH (Baillie et al., 1996) was adopted. The ARFIMA (p, d_{m}, q)–FIGARCH (p, d_{v}, q) model is written in the following polynomial forms:
The degree of volatility persistence was also estimated using FIAPARCH model (Tse, 1998) and HYGARCH model (Davidson, 2004). Superior to FIGARCH, FIAPARCH captures the asymmetric effect in the conditional variance while HYGARCH releases the unitamplitude restriction to account for both volatility persistence and covariance stationarity. The FIAPARCH(p, γ, δ, d_{v}, q) model and the HYGARCH(p, λ, d_{v}, q) model can be written as:
4. Data
Data used in this paper are daily closing prices of the S&P/HKEX GEM Index. Data were retrieved from the Bloomberg Database for the period 3 March 2003–30 September 2017. The sample period starts from the date that the HKEX launched the index and allows us to observe the effect of the GFC and several institutional reforms undertaken by HKEX authorities during the recent decades. Also, monthly consumer price indices for Hong Kong were obtained from the IMF’s International Financial Statistics and then converted into daily series using the frequency conversion technique.
The price series was transformed into return series using the logarithmic form, r_{t}=ln(P_{t}/P_{t−1}), where P_{t} and P_{t−1} denote index closing prices at time t and t−1.
Table I displays the characteristics of the GEM return series during the sample period. The market return is positively skewed, indicating that the series is asymmetrical and flatter to the right compared to Gaussian (normal) distribution. The significant kurtosis implies that the return series is also leptokurtic and has sharp peaks. The Jarque–Bera joint test of symmetry and mesokurtosis further confirms the return series is nonGaussian (nonnormal) distributed. The Ljung–Box Q and Q^{2} statistics up to lag 10 and 20 were highly significant, suggesting longrange dependencies in the mean and variance of the return series. The Engle ARCH statistics up to lag 5 and 10 showed the presence of conditional heteroscedasticity in the return series.
5. Findings and discussion
5.1 Detecting structural breaks
Before modelling the evolution towards efficiency and long memory, the presence of structural breaks in the GEM return and volatility series was tested using the Bai and Perron (2003) approach and the ICSS algorithm. The results consistently showed five breakpoints in the return and volatility series. The detected breakpoints appear to correspond to major pandemic, political, macroeconomic and financial events as described in Table II.
5.2 Evolving market efficiency
In this section, we investigated whether the GEM evolves towards efficiency over time, as this market has been gradually growing in terms of market capitalisation and liquidity, and the HKEX authorities have undertaken several efforts to improve the operational efficiency of the market. For this purpose, a statespace GARCHM(1, 1) model with Kalman filter estimation was applied on daily return series of the GEM. The model, which accommodates nonlinearity and timevarying AR(1) coefficient, is capable of capturing the changing degree of market inefficiency through time and a potential tendency towards efficiency in the GEM.
As a preanalysis step, stationarity of the daily return series was assessed to avoid the problem of spurious regression. Due to the presence of five structural breaks, the study sample was divided into six subsamples according to six break regimes as in Table II. The six individual subsamples were tested for stationarity using three prevalent unit root tests including the Augmented Dickey and Fuller (1979) (ADF), Phillips and Perron (1988) (PP) and Ng and Perron (2001) (NP). As shown in Table III, the ADF, PP and NP test statistics unanimously rejected the null of nonstationarity at 1 and 5 per cent level of significance. Thus, the GEM return series is stationary and ready for time series model estimation.
Table IV reports results of the statespace GARCHM(1, 1) model estimation. In the model, the intercept (β_{0}) represents nonmeasurable factors such as political events and external disturbances. Since this parameter was statistically insignificant, these factors thus have no influence on the GEM. Nonetheless, the AR(1) coefficient (β_{1}) at final state was significantly different from 0 at 1 per cent, implying weakform inefficiency in the GEM. The time path of β_{1} is depicted in Figure 3 and discussed in later paragraphs. While the risk premium parameter (β_{2}) was insignificant, ARCH and GARCH effects were significant at 1 per cent, suggesting that the GEM return volatility are highly sensitivity to past shocks. Moreover, the measure of volatility persistence represented by (α_{1}+α_{2}) is very close to unity (0.97), implying that undesirable shocks will persist in the long run. Additionally, postestimation diagnostic statistics provided evidence of no serial correlation and heteroscedasticity in the standardised residuals, suggesting that the model specification is adequate.
Figure 3 portrays the evolution of market efficiency in GEM by showing the time path of AR(1) coefficient (β_{1t}) (red line) together with 95 per cent confidence interval (black lines), obtained from Kalman filter estimation. When the time path approaches zero, a tendency towards efficiency is implied and vice versa. As SMEs are growing rapidly and market participants and regulatory environment becomes more sophisticated over time, the GEM has been developing robustly in terms of market size (market capitalisation) and liquidity provision (trading turnover) since launch, see Figures 1–2. Increasing market capitalisation implies positive sentiments about the future prospects of the listed companies since market capitalisation indicates how much the public is willing to pay for the companies’ shares. This encourages investors to make further investments in the market and thus more transactions are executed, which in turn increase the trading turnover. Increasing trading turnover indicates more opportunities for market prices to adjust and reflect new information, thereby the degree of market efficiency will gradually improve. Additionally, institutional improvements such as improvements in system infrastructure for trading and settlement also facilitate investors’ trade in the market by making the trading process more effective in terms of speed and accuracy. This in turn boosts the trading turnover and later enhances the degree of market efficiency. Therefore, the evolution of market efficiency in the GEM is now justified based on the growth of market capitalisation and trading turnover, and institutional improvements that were implemented in the GEM during the study period.
As can be seen, the GEM is still inefficient in the weak form, and the recent GFC imposed a further deviation from efficiency in the market from 2008 to 2011. However, other than this turmoil period, the GEM shows a tendency towards efficiency, which seems to align with a gradual increase in its market capitalisation and trading turnover since market launch. Growing trading turnover means that more transactions are executed, thus offering more chances for market prices to adjust and incorporate new information. This is a requisite for a stock market to be weakform efficient. Furthermore, the tendency towards efficiency in the GEM can be supported by the efforts of the HKEX authorities to improve the operational efficiency of the market during the pre and postGFC period. These efforts are also referred to as institutional reforms and mainly relate to improvements in system infrastructure for trading, settlement and information dissemination, reduction in transaction fees, and measures to manage risks and market volatility (see Appendix). Specifically, HKEX upgraded the third generation automatic order matching and execution system to version 3.8, increasing the processing capacity from 3,000 orders per second to 30,000 and reducing the response time to 2 ms from 0.15 s. A major investment of US$400m in a next generation market data platform, Orion Market Data Platform, enables the HKEX to establish points of presence for market data distribution outside of Hong Kong, such as in Mainland China. Progressive technology investments thus have boosted the HKEX’s competitive appeal as a global leading fundraising platform, which may position HKEX in the same fundraising league as New York or London. Moreover, a recent introduction of volatility control mechanism for the securities and derivatives markets is to assure market integrity by preventing extreme price volatility stemming from significant trading errors or other unusual incidents. This initiative also offers a window allowing investors to review their strategies and the market to reestablish equilibrium point during volatile market conditions, thereby enhancing HKEX’s overall competitiveness. Accordingly, the institutional reforms undertaken by the exchange authorities so far seem to be effective in driving the GEM towards weakform market efficiency (Figure 3).
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 longmemory pattern in both return and volatility series of the GEM is desirable. To model long memory in return and volatility, longmemory 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 (r_{t}) and adjusted returns (
Table V reports the estimation results of the three indicated longmemory models. In the ARFIMA(2, d_{m}, 2)–FIGARCH(1, d_{v}, 1) model estimation, the d_{m} parameters using raw returns (r_{t}) and dethinned returns (
Similarly, the estimations of ARFIMA(2, d_{m}, 2)–FIAPARCH(1, γ, δ, d_{v}, 1) model revealed that the magnitude and level of significance of d_{m} and d_{v} parameters reduced steadily once the joint effects of thin trading, structural breaks and inflation were accounted for. In particular, the d_{m} 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 d_{v} 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 d_{m} and d_{v} 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, d_{m}, 2)–FIGARCH(1, d_{v}, 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, d_{m}, 2)–HYGARCH(1, d_{v}, 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 d_{m} and d_{v} parameters weakened from 1 to 5 per cent and 10 per cent after the factors adjustments. The degree of d_{m}(d_{v}) 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 postestimation 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 longmemory models, the estimates of d_{m} and d_{v} parameters using the returns adjusted for the three factors (
6. Conclusion and future research
The target of this paper is to explore the evolution of weakform 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, statespace GARCHM model with the Kalman filter estimation to depict the evolution of weakform 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 postGFC 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 weakform efficiency.
The results also revealed the presence of stationary long memory in return and volatility series of the GEM. However, these dual longmemory 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 longrange 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 proofofconcept 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
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)  Q^{2}(10)  Q^{2}(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
Structural breakpoints
Breakpoint  Corresponding events  Regime period 


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:
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 

−59.18*  −71.04*  −203.03*  −276.29*  −262.41*  −10.82** 

−5.38*  −5.94*  −10.07*  −11.75*  −11.45*  −2.26**  
MSB^{d}  0.09*  0.08*  0.05*  0.04*  0.04*  0.21**  

0.55*  0.38*  0.13*  0.09*  0.09*  2.53**  
NP  C&T 

−265.45*  −86.47*  −266.04*  −276.72*  −262.49*  −37.93* 

−11.48*  −6.55*  −11.52*  −11.76*  −11.46*  −4.35*  
MSB^{d}  0.04*  0.08*  0.04*  0.04*  0.04*  0.11*  

0.44*  1.15*  0.37*  0.34*  0.35*  2.41* 
Notes: C denotes as constant; C&T denotes as constant and trend;
Statespace GARCHM(1, 1) model estimation
Coefficient  SE  RobustSE  tvalue  

β_{0}  0.00  0.00  0.00  −0.67 
β_{1} (final state)  0.11  0.02  0.02  5.86* 
β_{2}  1.97  9.78  9.75  0.20 
α_{0}  0.00  0.00  0.00  2.28** 
α_{1}(ARCH)  0.17  0.02  0.04  4.88* 
α_{2}(GARCH)  0.79  0.02  0.04  18.00* 
α_{1}+α_{2}  0.97  
Loglikelihood  11,039.77  
AIC  −6.13  
Diagnostic statistics  
Q(10)  37.69  Q(20)  53.64  
Q^{2}(10)  3.84  Q^{2}(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
Longmemory model estimations
ARFIMA(2, d_{m}, 2)–FIGARCH(1, d_{v}, 1)  ARFIMA(2, d_{m}, 2)–FIAPARCH(1, γ, δ, d_{v}, 1)  ARFIMA(2, d_{m}, 2)–HYGARCH(1, d_{v}, 1)  

Panel A: mean equation  
μ  0.00  0.00  0.00 
d_{m}(r_{t})  0.138**  0.187*  0.124* 

0.112***  0.153**  0.117** 

0.103***  0.112***  0.103** 

0.100***  0.110***  0.100*** 
Φ_{1}  −0.860***  −1.630***  −0.859*** 
Φ_{2}  −0.969***  −0.989***  −0.968*** 
Θ_{1}  0.871***  1.632***  0.869*** 
Θ_{2}  0.976***  0.991***  0.976*** 
Panel B: variance equation  
ω  0.132*  5.379  0.212* 
d_{v}(r_{t})  0.517**  0.456*  0.580* 

0.481**  0.442**  0.472** 

0.468***  0.437**  0.466*** 

0.457***  0.418***  0.454*** 
α_{1}  −0.184  –  −0.090 
β_{1}  0.086  0.215  0.240 
ϕ_{1}  –  −0.039  – 
γ  –  0.287**  – 
δ  –  1.300***  – 
Logλ  –  –  −0.115* 
Panel C: diagnostics  
Loglikelihood  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 
Q^{2}(10)  2.86  3.10  2.68 
Q^{2}(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; d_{m}(r_{t}),
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 realtime 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
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 Chinabased firms that are listed on the two Chinese stock exchanges, the Shanghai Stock Exchange and the Shenzhen Stock Exchange.
Appendix
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