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The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
CHAPTER
13
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange Gerasimos G. Rompotis
National and Kapodistrian University of Athens, 25 Ypsilantou Street, Peristeri, Athens 121 31, Greece
13.1 INTRODUCTION Over the last two decades the Exchange Traded Funds (hereafter ETFs) have experienced a tremendous growth in terms of assets under management, number, and variety of products offered covering essentially any asset class and type of investment (i.e., large, mid and small capitalization, domestic sector and broad stock indexes, international country indexes, regional as well as global indexes, equity, fixed income, commodity and currency indexes, passively and actively managed products, non-leveraged and leveraged products, and so on). One of the biggest ETF providers and managers worldwide is the BlackRock’s iShares with hundreds of ETF products listed in the USA, Canada, Australia as well as other major European, and Asian stock exchanges. The focus of the current chapter is on the trading behavior of the iShares listed in the Hong Kong Stock Exchange. Currently nine iShares are listed.1 The literature on Asian ETFs, in general, and iShares in particular is devoted to ETFs tracking Asian indexes but traded in the US stock market as well as the linkages between the US and Asian Stock markets examined with data from the ETF market. Darrat and Zhong (2002) use 15-minute interval trading data to study the relationship between SPDRs, which track the S&P 500 Index, and the iShares Japan Index Fund listed in the USA, which tracks the MSCI Japan Index. They find sufficient comovement between the returns, volatility, and liquidity of these ETFs with the latter being the leading factor of the former’s return, volatility, and liquidity. However, they find no evidence of spillover effect between the two ETFs because their returns are not found to be serially correlated. Hughen (2003) examines the pricing efficiency of Malaysian iShares listed in USA and demonstrates that they trade at a premium to their NAV. According to the author, this premium is arbitrage-able. Jares and Lavin (2004) reveal that the return of Japan and Hong Kong iShares is positively related to 1 In this chapter, eight out of the nine iShares listed in the Hong Kong Stock Exchange are studied. The ninth is excluded due to its limited trading activity, i.e., the majority of daily trading volume observations are nil.
Handbook of Asian Finance, Volume 2 http://dx.doi.org/10.1016/B978-0-12-800986-4.00013-3
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the lagged premium. Furthermore, the contemporaneous premium negatively affects the return of these iShares. According to the authors, the results weaken the efficient capital market hypothesis reflecting profitable opportunities. Gutierrez et al. (2009) study the linkages between the Asian and US stock markets using several Asian iShares traded in the USA and find that there is a significant bidirectional Granger causality between the US and six Asian markets. They further reveal that Asian markets play an important role in determining the performance of Asian ETFs listed in the USA, whereas these returns are highly correlated with the US market returns. Rompotis (2010a) examines the return and risk of the US-based Asian iShares ETFs finding that the Asian ETFs underperform their benchmarks, but they present similar volatility with them. In addition, the author shows that the return of Asian iShares is significantly influenced by the stock returns in the US market. In addition, Shum (2010) examines the linkage between the US and Asian stock markets focusing on four US-listed ETFs covering the major Asian markets (Hong Kong, Korea, Japan, and China). The author finds that these ETFs, whose underlying markets are closed when they actually trade in the US market, behave very much like large-cap US stocks. This implies that their performance and risk can be largely driven by the pricing behavior of the S&P 500 Index. Furthermore, Kim (2011) examines co-integration and spillover effects between US and Asia-Pacific stock markets before and after the global financial crisis using data from nine ETFs over the period from January 7, 2004, to September 30, 2010. The author shows that the majority of Asian markets are co-integrated with the US market both before and after the financial crisis. However, the power of integration is not the same for all the markets and both during the periods before and after the crisis. Additionally, Kim (2011) demonstrates that the US stock markets lead Asia-Pacific stock markets, but Asia-Pacific stock markets do not do so. The results obtained in this chapter show that the iShares traded on the Hong Kong stock market significantly underperform their benchmarks. When it comes to risk, iShares and indexes rather present the same volatility. Furthermore, iShares are found not to adopt full replication strategies as indicated by beta estimates which significantly decline from unity.2 Going further, the underperformance of iShares compared to the tracking indexes is also depicted in the significant tracking error estimates. The last issue examined concerns the pricing efficiency of iShares. Pricing efficiency is assessed by calculating the premium/discount in iShares’ trading prices in comparison with their net assets values (NAV). The results show the iShares trade at a significant premium to their NAV. Per regression analysis, this premium is found to be strongly persistent in the short run.
2 The
iShares examined do not adopt physical replication. Instead, they pursue synthetic replication of the benchmarks’ performance.
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
13.2 DATA AND STATISTICS Our data consists of the historical daily net assets values and trading prices of eight iShares listed in the Hong Kong Stock Exchange. Table 13.1 provides the profiles of the examined ETFs. Profiles concern the names of iShares, their benchmarks, the inception dates in the Hong Kong Stock Exchange, the assets under management as of April 24, 2013 and their age, expressed in Hong Kong dollars (HKD), the average daily volume (number of shares traded) over the examined period (from the inception of each ETF to April 24, 2013), as well as the published management fee ratio. All the details reported have been found on the Web site of BlackRock’s iShares. As far as the market sectors covered by iShares are concerned, Table 13.1 shows that several major sectors of Hong Kong’s economy are pursued such as the Financials and the Energy sectors. In addition, the CSI 300 Index is also used by one ETF. This index is a diversified index consisting of 300 constituent stocks traded on the Shanghai and Shenzhen stock exchanges, representing about the 70% of the two exchanges’ total market capitalization (www.hk.iShares.com). Finally, the FTSE China A50 Index is tracked by one iShares. This index consists of the fifty largest and most liquid companies of China that trade on the Shanghai and Shenzhen stock exchanges. In regard to the iShares’ age, the oldest one is about 8.5 years old (incepted on November 15, 2004), whereas the newest ones (two iShares) are less than 3 years old (incepted on July 15, 2010). The average assets under management are approximately HKD9.3 billion.3 It should be noted, however, that this average does not reflect the actual magnitude of assets under management because it is inflated by the iShares FTSE A50 China Index ETF,4 whose assets are equal to HKD53.9 billion. Without this ETF, the amount of the remaining ETFs’ average assets is $22.25 million. The data on volume is similar to assets. More specifically, the average number of shares traded on a daily basis is about equal to 9.5 million, but when we exclude the aforementioned ETF from the calculations, the average daily volume of the rest ETFs falls to 120.91 thousand shares. Finally, with respect to expenses, Table 13.1 reports an average management fee of 0.99%. This percentage can be considered as quite high given that ETFs pride themselves on their cost efficiency because they charge investors low management fees.5 This average expense ratio is comparable to that of leveraged ETFs traded on the US market.6 Table 13.2 furnishes the descriptive statistics of ETFs vis-à-vis the descriptive statistics of the tracking indexes. The statistics of iShares are presented both in NAV and trading
3 The
exchange rate of HKD and USD as of April 24, 2013, is 0.1288. This means that the average assets of HKD6,927.66 billion is equal to USD892.28 million. 4 This is the oldest iShares listed in the Hong Kong Stock Market. 5 For instance, the expense ratio of SPDRs is less than 0.10%. 6 Rompotis (2011) reports an average expense ratio of US-leveraged ETFs amounting to 0.94%.
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Table 13.1 The Profiles of iShares Name Benchmark
iShares CSI 300 A-Share Index ETF iShares FTSE A50 China Index ETF iShares CSI A-Share Consumer Discretionary Index ETF iShares CSI A-Share Consumer Staples Index ETF iShares CSI A-Share Energy Index ETF iShares CSI A-Share Financials Index ETF iShares CSI A-Share Infrastructure Index ETF iShares CSI A-Share Materials Index ETF Average
Inception Date
Age (Years)
Assets (HKD 000,000)
Volume
Expense Ratio
CSI 300 Index
11/11/2009
3.452
601.88
3161
0.99
FTSE China A50 Index
11/15/2004
8.444
53,865.55
74,936,845
0.99
CSI 300 Consumer Discretionary Index
07/15/2010
2.778
159.61
62,819
0.99
CSI 30 Consumer Staples Index
07/15/2010
2.778
189.57
74,805
0.99
CSI 300 Energy Index CSI 300 Financials Index
11/12/2009
3.449
100.81
81,424
0.99
11/12/2009
3.449
264.47
16,564
0.99
CSI 300 Infrastructure Index
11/12/2009
3.449
132.54
49,566
0.99
CSI 300 Materials Index
11/12/2009
3.449
106.86
96,003
0.99
3.906
6.927.66
9,472,900
0.99
This table presents the profiles of iShares examined which are the names, benchmark indices, inception dates, age, assets as of 24 April 2013, volume and expense ratios of iShares.
prices terms. The average NAV return of iShares is negative and equal to −0.02%. The corresponding return of indexes is essentially equal to zero. The difference in returns is statistically significant at the 1% level of acceptance. However, the median NAV return of iShares does not statistically differ from that of the indexes. When the trading prices are taken into consideration, the iShares are found once again to underperform their
Index
1.674 1.471 2.283 1.920 1.624 1.559 1.483 1.509 1.894 1.925 1.590 1.619 1.313 1.287 1.881 1.871 1.718 1.645 1.460
1.435 1.471 1.907 1.920 1.553 1.559 1.506 1.509 1.913 1.925 1.602 1.619 1.292 1.287 1.861 1.871 1.634 1.645 −2.827**
ETF
Risk Index
−5.303 −6.368 −15.185 −9.241 −4.533 −6.670 −5.520 −5.605 −5.859 −6.863 −5.307 −6.494 −4.595 −5.734 −5.992 −7.438 −6.537 −6.802 0.291
−4.855 −6.368 −9.283 −9.241 −6.568 −6.670 −5.683 −5.605 −6.892 −6.863 −6.592 −6.494 −5.653 −5.734 −7.330 −7.438 −6.607 −6.802 1.022
ETF
Minimum Index
5.934 5.408 19.626 9.500 5.210 5.114 4.508 4.408 9.365 8.047 5.281 6.541 3.811 4.010 7.609 8.412 7.668 6.430 0.953
5.254 5.408 9.558 9.500 5.056 5.114 4.358 4.408 7.997 8.047 5.163 6.541 4.256 4.010 8.475 8.412 6.265 6.430 −0.926
ETF
Maximum
793 1978 611 615 783 786 745 777 886
793 1978 611 615 783 786 745 777 886
Obs.
at the 1% level. ⁎⁎Significant at the 5% level. This table presents the descriptive statistics of iShares and their benchmarks as of April 24, 2013. Descriptive statistics include the average daily return, the median return, the standard deviation, and the extreme scores (minimum and maximum). The statistics of iShares are presented both in NAV and closing trading prices. T-test assesses whether the difference in descriptive between iShares and indices is statistically significant.
⁎Significant
0.000 0.005 0.000 0.051 0.000 −0.046 0.000 0.078 −0.142 −0.091 0.000 −0.010 0.000 0.013 0.000 −0.013 −0.018 −0.002 −1.107
Index
Panel B: Closing Trading Prices ETF1 −0.037 −0.018 ETF2 0.068 0.070 ETF3 −0.014 0.019 ETF4 0.005 0.045 ETF5 −0.061 −0.038 ETF6 −0.032 −0.008 ETF7 −0.059 −0.038 ETF8 −0.057 −0.038 Average −0.023 −0.001 T-test −5.802*
ETF
−0.018 0.005 0.064 0.051 −0.018 −0.046 0.043 0.078 −0.082 −0.091 −0.009 −0.010 −0.012 0.013 −0.032 −0.013 −0.008 −0.002 −0.790
Index
Panel A: Net Assets Values ETF1 −0.038 −0.018 ETF2 0.059 0.070 ETF3 −0.001 0.019 ETF4 0.021 0.045 ETF5 −0.059 −0.038 ETF6 −0.030 −0.008 ETF7 −0.058 −0.038 ETF8 −0.055 −0.038 Average −0.020 −0.001 T-test −14.474*
ETF
Table 13.2 Descriptive Statistics of Returns Average Return Median Return
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
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benchmarks. Once again, there is no statistically and economically significant difference between the median trading price returns of iShares and indexes. When it comes to volatility, the average NAV risk estimate of iShares is equal to 1.634. The corresponding figure of the indexes is 1.645. In trading price terms, the risk of ETFs is equal to 1.718. In any case, there is no statistically significant difference in risk between ETFs and benchmarks. This also is the case for the extreme scores, namely the minimum and maximum return estimations of iShares and indexes. However, both the standard deviation estimates and the range of extreme scores, i.e., maximum minus minimum scores, which range from 12.872 in absolute terms when the NAV returns of iShares are considered to 14.205 when the trading prices of ETFs are taken into consideration (not clearly shown in Table 13.2), show that the Hong Kong ETF market is quite volatile.
13.3 PERFORMANCE ASSESSMENT In this section, the performance of iShares versus the performance of the tracking indexes is assessed. Performance assessment is performed in two ways; first, performance is assessed via a single-index regression analysis in which the daily return of ETFs is regressed on the daily return of the indexes. The regression is performed both for the NAV returns and for the closing day prices of iShares.The second performance appraisal regards the tracking error, which indicates the difference in returns between ETFs and indexes. Again, tracking error is computed with the usage of both NAV and closing day price. Table 13.3 provides the results of the single-index performance regression. Presented in the table are the alpha and beta coefficients, the respective t-tests on the statistical significance of the estimates along with the R-squares, which assess the explanatory power of the model. The average alpha in Panel A is equal to −0.020 being significant at the 1% level. All but one single alpha is statistically significant at the 1% level, too.7 The corresponding alpha obtained from the closing day price return is equal to −0.023 being significant at the 1% level. Interestingly, the single alphas are now statistically insignificant. In any case, iShares do not produce any alpha. This finding is not surprising given the passive nature of the ETFs examined in this chapter. On the question of systematic risk and the replication strategy of iShares, the average NAV beta of 0.982, which is significantly different than the unity, leads to the following two main inferences. The first one is that the iShares are slightly more conservative than 7 We could assume that the negative alpha is due to the impact of expenses. However, the short number of sample’s iShares does not allow the performance of a relevant cross-sectional regression so as to verify this assumption.
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
Table 13.3 Performance Regression Results t-test#0 α
β
t-test#1
R2
Obs.
Panel A: Net Assets Values ETF1 −0.022 ETF2 −0.011⁎ ETF3 −0.019⁎ ETF4 −0.024⁎ ETF5 −0.021⁎ ETF6 −0.023⁎ ETF7 −0.020⁎ ETF8 −0.018⁎ Average −0.020 T-test −13.421*
−1.236 −3.080 −4.419 −4.900 −3.978 −4.442 −4.865 −5.322 −4.030
0.917⁎ 0.988⁎ 0.992 0.995 0.991⁎⁎ 0.984⁎⁎⁎ 0.995 0.995⁎⁎⁎ 0.982 −1.903***
−5.458 −3.942 −1.495 −1.463 −2.107 −1.842 −0.767 −1.970 −2.381
0.882 0.990 0.992 0.992 0.993 0.989 0.982 0.996 0.977
793 1978 611 615 783 786 745 777 886
Panel B: Closing Trading Prices ETF1 −0.020 ETF2 0.000 ETF3 −0.033 ETF4 −0.033 ETF5 −0.026 ETF6 −0.025 ETF7 −0.027 ETF8 −0.022 Average −0.023 T-test −6.300*
−0.595 −0.017 −1.273 −1.086 −1.299 −0.952 −1.155 −0.792 −0.896
0.899⁎ 0.973 0.865⁎ 0.808⁎ 0.885⁎ 0.851⁎ 0.835⁎ 0.903⁎ 0.877 −6.910*
−3.334 −1.154 −5.105 −8.339 −6.400 −7.304 −7.466 −5.054 −5.519
0.609 0.696 0.685 0.656 0.786 0.730 0.652 0.784 0.700
793 1978 611 615 783 786 745 777 886
⁎Significant
at the 1% level. ⁎⁎Significant at the 5% level. ⁎⁎⁎Significant at the 10% level. This table presents the results of the performance regression between iShares and benchmarks. The iShares’ daily return is regressed on the corresponding return of benchmarks. Alpha coefficient reflects the return that can be achieved by an iShares independently to the index return. Beta counts for the systematic risk of iShares and indicates the replication efficiency of iShares. T-tests on alphas estimate the statistical significance of the difference of these coefficients from zero. T-tests on betas indicate the significance of the difference of estimates from the unity. R-square assesses the explanatory power of the regression.
the benchmarks (average beta lower than unity). The second inference is that iShares do not pursue a full replication strategy. This statistical finding is in line with the statement on the Web site of iShares about the synthetic replication techniques adopted by the iShares listed in the Hong Kong Stock Exchange. The results on systematic risk and benchmarks’ replication obtained with the usage of trading data are qualitatively equal to these of NAV returns. The average beta is equal to 0.877 being inferior to the respective average NAV beta. This average beta is significant at the 1% level. Moreover, all but one single beta is different than unity. In the case of NAV returns, five out of eight betas are significantly different than zero. Therefore, the conservativeness and the deviation from the full replication methods are also confirmed by the trading data of iShares.
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Table 13.4 Tracking Error TE1
Panel A: Net Assets Values ETF1 −0.020 ETF2 −0.011 ETF3 −0.019 ETF4 −0.024 ETF5 −0.020 ETF6 −0.022 ETF7 −0.020 ETF8 −0.017 Average −0.019 T-test −14.474* Panel B: Closing Trading ETF1 ETF2 ETF3 ETF4 ETF5 ETF6 ETF7 ETF8 Average T-test ⁎Significant
Prices −0.019 −0.002 −0.033 −0.040 −0.003 −0.024 −0.021 −0.019 −0.020 −4.343*
TE2
TE3
Obs.
0.507 0.191 0.149 0.138 0.161 0.175 0.184 0.128 0.204 4.647*
0.492 0.188 0.144 0.137 0.159 0.171 0.175 0.124 0.199 4.662*
793 1978 611 615 783 786 745 777 886
1.066 1.314 0.962 0.936 0.918 0.874 0.819 0.908 0.974 17.841*
1.048 1.266 0.915 0.872 0.880 0.827 0.777 0.875 0.933 16.942*
793 1978 611 615 783 786 745 777 886
at the 1% level. Note: TE1 refers to the average return daily return difference between each iShares and its benchmark.TE2 is the standard deviation of daily return differences. TE3 is the standard errors of the single-index performance regression. This table presents the estimations of tracking error as of 24 April 2013, which reflects the deviation between the return of iShares and benchmarks.Tracking error is calculated via three distinct methods labeled as TE1,TE2, and TE3.The t-test assesses whether the average tracking errors are statistically different from zero.
Table 13.4 provides the tracking error computations with three alternative tracking errors used.The first one concerns the raw average difference in returns between iShares and indexes. The second method deals with the standard deviation in return differences between ETFs and indexes, which is a method that is standard in the literature.8 The last tracking error measurement considers the standard error of the performance regression described above. The average tracking error estimation obtained via the first method is equal to −0.019 being quite similar to the average NAV alpha (i.e., −0.020). This estimate is statistically significant at the 1% level. The average of TE2 and TE3 is equal to 0.204 and 0.199, respectively, both being significant at the 1% level. When the trading prices 8
E.g., refer to Frino and Gallagher (2001).
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
returns of iShares are considered, the tracking errors are worse, i.e., they show that the divergence in returns between iShares and indexes is greater in trading data terms. The average TE1 is significant and equal to −0.020 (once approaching the average alpha reported in Panel B of Table 13.3). However, the average of TE2 and TE3 is equal to 0.974 and 0.933, respectively, both being much higher than the TE2 and TE3 derived from the NAV returns. Overall, the results on tracking error calculations indicate that the performance of iShares listed in the Hong Kong Stock Exchange substantially deviates from the return of the tracking index portfolios. This finding is line with the analysis of raw returns in Table 13.2 and the regression analysis of performance in Table 13.3.
13.4 PRICING EFFICIENCY ASSESSMENT In this last section of the chapter, the pricing efficiency of iShares is examined.The pricing efficiency is assessed via the computation of the premium/discount in the trading price returns of iShares. The premium is calculated in percentage terms as the fraction of day’s t closing price minus the day’s t NAV for each ETF divided by the day’s t NAV. In addition, the persistence of premium is investigated by running a simple time series regression model of premium values on its one-day lagged values. This approach in assessing the persistence of premium has been found in Elton et al. (2002). Table 13.5 reports the descriptive statistics of ETFs’ premium. In particular, the average and median premium of each ETF are presented along with the standard deviation of daily premiums (used to assess the volatility in premium) and the extreme scores, namely the lowest premium (i.e., discount) and the maximum premium observed for each ETF. The results in Table 13.5 show that the average iShares trade at a significant premium to its NAV of 4.167%. This figure is quite higher than the average premiums of US-listed ETFs reported in the literature.9 The median premium is slightly better yet, but still high (it is equal to 3.849). The average standard deviation of premiums is equal to 3.792. This number is extremely high and indicates that there are a lot of fluctuations in terms of pricing behavior of Hong Kong iShares, whereby the latter inference is confirmed by the extreme scores. In particular, the average minimum discount of the sample is equal to 4.623%, whereas the average maximum premium equals the 14.715%. The main inference drawn via the analysis of iShares’ premiums is that the specific ETFs deviate from what is the case for the majority of US-listed ETFs, whose premiums are usually low and very short-lived. The large premiums found in the pricing of Hong Kong iShares raise questions about the applicability of the efficient market hypothesis in the case of Hong Kong ETFs. 9
E.g., Rompotis (2010b) reports an average premium of US-listed iShares of 0.059%.
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Table 13.5 Descriptive Statistics of Premium Average Median Standard Deviation
Minimum
Maximum
Obs.
ETF1 ETF2 ETF3 ETF4 ETF5 ETF6 ETF7 ETF8 Average
−3.840 −16.640 −2.100 −4.950 −3.170 −2.720 −1.610 −1.950 −4.623
9.160 23.430 13.740 14.820 13.780 14.940 13.710 14.140 14.715
793 1978 611 615 783 786 745 777 886
1.963 3.655 5.056 4.353 4.379 4.708 4.656 4.565 4.167
1.590 2.930 4.960 3.870 4.200 3.985 4.730 4.530 3.849
2.224 5.052 3.523 3.996 3.951 4.122 3.734 3.736 3.792
Note: Negative premiums are named discounts. This table presents the descriptive statistics of iShares’ premium as of 24 April 2013. Descriptive statistics include the average daily premium, the median premium, the standard deviation of premium’s values, and the extreme scores (minimum and maximum).
Table 13.6 Premium Regression Results t-test α
β
t-test
R2
Obs.
ETF1 ETF2 ETF3 ETF4 ETF5 ETF6 ETF7 ETF8 Average
0.980⁎ 0.976⁎ 0.975⁎ 0.986⁎ 0.983⁎ 0.984⁎ 0.977⁎ 0.976
77.478 96.695 113.163 101.208 163.542 145.201 150.262 123.856 121.426
0.854 0.935 0.922 0.939 0.943 0.953 0.946 0.937 0.929
793 1978 611 615 783 786 745 777 886
⁎Significant
0.106⁎
0.044 0.104⁎⁎⁎ 0.090⁎⁎⁎ 0.060⁎⁎⁎ 0.081⁎⁎⁎ 0.074⁎⁎ 0.105⁎⁎ 0.083
2.999 1.083 1.997 1.722 1.833 1.931 2.081 2.584 2.029
0.945⁎
at the 1% level. ⁎⁎Significant at the 5% level. ⁎⁎⁎Significant at the 10% level. This table presents the results of the premium’s regression on its one-day lagged values. Beta counts for the persistence in iShares’ premium. T-tests on alphas estimate the statistical significance of the difference of these coefficients from zero. T-tests assess the significance of the difference of estimates from zero. R-square assesses the explanatory power of the regression.
The regression results on premiums are provided in Table 13.6. The average beta estimate is equal to 0.976. All the single betas approximate to unity being statistically significant at the 1% level. This finding is indicative of high persistence in Hong Kong iShares’ premium from the day t − 1 to the day t. This finding contrasts the literature’s findings on US-listed ETFs.10 Persistence in premium may be related to statutory and other frictions that can make arbitrage execution less efficient. 10 E.g., Engle and Sarkar (2006) show that the pricing errors for domestic US ETFs are small and quickly reversed. However, in the case international ETFs listed in the USA, the premiums/discounts are greater and long-lasting.
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
13.5 CONCLUSION Given the increasing popularity of ETFs worldwide, they have generally been of great interest to researchers investing ETFs listed in the US market but examining tracking indexes from Asian stock markets.The current chapter examines several issues surrounding the trading behavior of eight iShares listed in the Hong Kong Stock Exchange. In particular, the performance and volatility of these ETFs are examined along with their tracking and pricing efficiency. The main findings of the chapter are summarized as follows: At first, the iShares are found to significantly underperform their tracking index portfolios. This finding is consistent both when NAV and trading prices returns are assessed. The risk of iShares is comparable to that of the indexes, but there are no significant differences between ETFs and indexes when volatility is in question. Going further, the performance regression results confirm that the iShares cannot produce any meaningful alpha given that they are passively managed investment vehicles suitable to investors wishing to access beta exposure without expecting any excess market return. In addition, Hong Kong iShares are found not to adopt full replication techniques in order to outperform the underlying indexes. This finding is in line with information found on the Web site of Hong Kong iShares, stating that the specific products seek to deliver the return of the tracking benchmarks in a synthetic way. When it comes to the ability of iShares to efficiently replicate their underlying assets, the results demonstrate that they basically fail to do so. More specifically, the tracking error calculations show that there is a significant difference in returns between iShares and underlying index portfolios. This finding reconfirms the return discrepancy already revealed via the analysis of raw returns of iShares and indexes as well as through the performance regression analysis. Finally, when the pricing efficiency of iShares is investigated, the results show that the average Hong Kong iShares trade at a substantial premium to their NAV. The magnitude of the average premium is quite unusual when compared to the respective premium estimates of US-listed ETFs. In addition, the premium is highly persistent on a daily basis, which implies that a significant premium on one day is followed by an equally significant premium the next day. Both the magnitude and persistence in premium may indicate the existence of serious statutory and other obstacles in the application of efficient arbitrage which would contribute to the narrowness of the gaps between iShares’ trading and net assets vales in a very efficient way.
REFERENCES Darrat, A., Zhong, M., 2002. Permanent and transitory driving forces in the Asian-Pacific stock markets. The Financial Review 37 (1), 35–52. Elton, J.E., Gruber, M., Comer, G., Li, K., 2002. Spiders: where are the bugs? Journal of Business 75 (3), 453–473.
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Engle, R.F., Sarkar, D., 2006. Premiums-discounts and exchange-traded funds. Journal of Derivatives 1 (1), 35–53. Frino, A., Gallagher, D.R., 2001. Tracking S&P 500 index funds. Journal of Portfolio Management 28 (1), 44–55. Gutierrez, J.A., Martinez,V., Tse,Y., 2009. Where does Return and Volatility Come from? The Case of Asian ETFs. Working Paper, University of Texas at San Antonio, San Antonio, United States and Fairfield University, Fairfield, United States. Hughen, J.C., 2003. How effective is arbitrage of foreign stocks? The case of the Malaysia exchange traded fund. Multinational Business Review 11 (1), 17–27. Jares, E.T., Lavin, A.M., 2004. Japan and Hong Kong exchange-traded funds (ETFs): discounts, returns, and trading strategies. Journal of Financial Service Research 25 (1), 57–69. Kim, B.S., 2011. Linkages between the US and Asia-Pacific exchange traded funds (ETF) markets: evidence from the 2007–2008 global financial crisis. Asian Academy of Management Journal of Accounting and Finance 7 (1), 53–72. Rompotis, G.G., 2010a. Investing overseas from home: the case of Asian iShares. Journal of Asset Management 11 (1), 1–18. Rompotis, G.G., 2010b. Does premium impact exchange traded funds’ returns? Evidence from iShares. Journal of Asset Management 11 (4), 298–308. Rompotis, G.G., 2011. A study on the 3G exchange traded funds: the case of short ETFs. Journal of Index Investing 2 (1), 25–43. Shum, P.M., 2010. How Passive are International ETFs? A Study of their Intraday Behavior. Working Paper, Schulich School of Business, York University.
13
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange Gerasimos G. Rompotis
National and Kapodistrian University of Athens, 25 Ypsilantou Street, Peristeri, Athens 121 31, Greece
13.1 INTRODUCTION Over the last two decades the Exchange Traded Funds (hereafter ETFs) have experienced a tremendous growth in terms of assets under management, number, and variety of products offered covering essentially any asset class and type of investment (i.e., large, mid and small capitalization, domestic sector and broad stock indexes, international country indexes, regional as well as global indexes, equity, fixed income, commodity and currency indexes, passively and actively managed products, non-leveraged and leveraged products, and so on). One of the biggest ETF providers and managers worldwide is the BlackRock’s iShares with hundreds of ETF products listed in the USA, Canada, Australia as well as other major European, and Asian stock exchanges. The focus of the current chapter is on the trading behavior of the iShares listed in the Hong Kong Stock Exchange. Currently nine iShares are listed.1 The literature on Asian ETFs, in general, and iShares in particular is devoted to ETFs tracking Asian indexes but traded in the US stock market as well as the linkages between the US and Asian Stock markets examined with data from the ETF market. Darrat and Zhong (2002) use 15-minute interval trading data to study the relationship between SPDRs, which track the S&P 500 Index, and the iShares Japan Index Fund listed in the USA, which tracks the MSCI Japan Index. They find sufficient comovement between the returns, volatility, and liquidity of these ETFs with the latter being the leading factor of the former’s return, volatility, and liquidity. However, they find no evidence of spillover effect between the two ETFs because their returns are not found to be serially correlated. Hughen (2003) examines the pricing efficiency of Malaysian iShares listed in USA and demonstrates that they trade at a premium to their NAV. According to the author, this premium is arbitrage-able. Jares and Lavin (2004) reveal that the return of Japan and Hong Kong iShares is positively related to 1 In this chapter, eight out of the nine iShares listed in the Hong Kong Stock Exchange are studied. The ninth is excluded due to its limited trading activity, i.e., the majority of daily trading volume observations are nil.
Handbook of Asian Finance, Volume 2 http://dx.doi.org/10.1016/B978-0-12-800986-4.00013-3
© 2014 Elsevier Inc. All rights reserved.
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the lagged premium. Furthermore, the contemporaneous premium negatively affects the return of these iShares. According to the authors, the results weaken the efficient capital market hypothesis reflecting profitable opportunities. Gutierrez et al. (2009) study the linkages between the Asian and US stock markets using several Asian iShares traded in the USA and find that there is a significant bidirectional Granger causality between the US and six Asian markets. They further reveal that Asian markets play an important role in determining the performance of Asian ETFs listed in the USA, whereas these returns are highly correlated with the US market returns. Rompotis (2010a) examines the return and risk of the US-based Asian iShares ETFs finding that the Asian ETFs underperform their benchmarks, but they present similar volatility with them. In addition, the author shows that the return of Asian iShares is significantly influenced by the stock returns in the US market. In addition, Shum (2010) examines the linkage between the US and Asian stock markets focusing on four US-listed ETFs covering the major Asian markets (Hong Kong, Korea, Japan, and China). The author finds that these ETFs, whose underlying markets are closed when they actually trade in the US market, behave very much like large-cap US stocks. This implies that their performance and risk can be largely driven by the pricing behavior of the S&P 500 Index. Furthermore, Kim (2011) examines co-integration and spillover effects between US and Asia-Pacific stock markets before and after the global financial crisis using data from nine ETFs over the period from January 7, 2004, to September 30, 2010. The author shows that the majority of Asian markets are co-integrated with the US market both before and after the financial crisis. However, the power of integration is not the same for all the markets and both during the periods before and after the crisis. Additionally, Kim (2011) demonstrates that the US stock markets lead Asia-Pacific stock markets, but Asia-Pacific stock markets do not do so. The results obtained in this chapter show that the iShares traded on the Hong Kong stock market significantly underperform their benchmarks. When it comes to risk, iShares and indexes rather present the same volatility. Furthermore, iShares are found not to adopt full replication strategies as indicated by beta estimates which significantly decline from unity.2 Going further, the underperformance of iShares compared to the tracking indexes is also depicted in the significant tracking error estimates. The last issue examined concerns the pricing efficiency of iShares. Pricing efficiency is assessed by calculating the premium/discount in iShares’ trading prices in comparison with their net assets values (NAV). The results show the iShares trade at a significant premium to their NAV. Per regression analysis, this premium is found to be strongly persistent in the short run.
2 The
iShares examined do not adopt physical replication. Instead, they pursue synthetic replication of the benchmarks’ performance.
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
13.2 DATA AND STATISTICS Our data consists of the historical daily net assets values and trading prices of eight iShares listed in the Hong Kong Stock Exchange. Table 13.1 provides the profiles of the examined ETFs. Profiles concern the names of iShares, their benchmarks, the inception dates in the Hong Kong Stock Exchange, the assets under management as of April 24, 2013 and their age, expressed in Hong Kong dollars (HKD), the average daily volume (number of shares traded) over the examined period (from the inception of each ETF to April 24, 2013), as well as the published management fee ratio. All the details reported have been found on the Web site of BlackRock’s iShares. As far as the market sectors covered by iShares are concerned, Table 13.1 shows that several major sectors of Hong Kong’s economy are pursued such as the Financials and the Energy sectors. In addition, the CSI 300 Index is also used by one ETF. This index is a diversified index consisting of 300 constituent stocks traded on the Shanghai and Shenzhen stock exchanges, representing about the 70% of the two exchanges’ total market capitalization (www.hk.iShares.com). Finally, the FTSE China A50 Index is tracked by one iShares. This index consists of the fifty largest and most liquid companies of China that trade on the Shanghai and Shenzhen stock exchanges. In regard to the iShares’ age, the oldest one is about 8.5 years old (incepted on November 15, 2004), whereas the newest ones (two iShares) are less than 3 years old (incepted on July 15, 2010). The average assets under management are approximately HKD9.3 billion.3 It should be noted, however, that this average does not reflect the actual magnitude of assets under management because it is inflated by the iShares FTSE A50 China Index ETF,4 whose assets are equal to HKD53.9 billion. Without this ETF, the amount of the remaining ETFs’ average assets is $22.25 million. The data on volume is similar to assets. More specifically, the average number of shares traded on a daily basis is about equal to 9.5 million, but when we exclude the aforementioned ETF from the calculations, the average daily volume of the rest ETFs falls to 120.91 thousand shares. Finally, with respect to expenses, Table 13.1 reports an average management fee of 0.99%. This percentage can be considered as quite high given that ETFs pride themselves on their cost efficiency because they charge investors low management fees.5 This average expense ratio is comparable to that of leveraged ETFs traded on the US market.6 Table 13.2 furnishes the descriptive statistics of ETFs vis-à-vis the descriptive statistics of the tracking indexes. The statistics of iShares are presented both in NAV and trading
3 The
exchange rate of HKD and USD as of April 24, 2013, is 0.1288. This means that the average assets of HKD6,927.66 billion is equal to USD892.28 million. 4 This is the oldest iShares listed in the Hong Kong Stock Market. 5 For instance, the expense ratio of SPDRs is less than 0.10%. 6 Rompotis (2011) reports an average expense ratio of US-leveraged ETFs amounting to 0.94%.
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Table 13.1 The Profiles of iShares Name Benchmark
iShares CSI 300 A-Share Index ETF iShares FTSE A50 China Index ETF iShares CSI A-Share Consumer Discretionary Index ETF iShares CSI A-Share Consumer Staples Index ETF iShares CSI A-Share Energy Index ETF iShares CSI A-Share Financials Index ETF iShares CSI A-Share Infrastructure Index ETF iShares CSI A-Share Materials Index ETF Average
Inception Date
Age (Years)
Assets (HKD 000,000)
Volume
Expense Ratio
CSI 300 Index
11/11/2009
3.452
601.88
3161
0.99
FTSE China A50 Index
11/15/2004
8.444
53,865.55
74,936,845
0.99
CSI 300 Consumer Discretionary Index
07/15/2010
2.778
159.61
62,819
0.99
CSI 30 Consumer Staples Index
07/15/2010
2.778
189.57
74,805
0.99
CSI 300 Energy Index CSI 300 Financials Index
11/12/2009
3.449
100.81
81,424
0.99
11/12/2009
3.449
264.47
16,564
0.99
CSI 300 Infrastructure Index
11/12/2009
3.449
132.54
49,566
0.99
CSI 300 Materials Index
11/12/2009
3.449
106.86
96,003
0.99
3.906
6.927.66
9,472,900
0.99
This table presents the profiles of iShares examined which are the names, benchmark indices, inception dates, age, assets as of 24 April 2013, volume and expense ratios of iShares.
prices terms. The average NAV return of iShares is negative and equal to −0.02%. The corresponding return of indexes is essentially equal to zero. The difference in returns is statistically significant at the 1% level of acceptance. However, the median NAV return of iShares does not statistically differ from that of the indexes. When the trading prices are taken into consideration, the iShares are found once again to underperform their
Index
1.674 1.471 2.283 1.920 1.624 1.559 1.483 1.509 1.894 1.925 1.590 1.619 1.313 1.287 1.881 1.871 1.718 1.645 1.460
1.435 1.471 1.907 1.920 1.553 1.559 1.506 1.509 1.913 1.925 1.602 1.619 1.292 1.287 1.861 1.871 1.634 1.645 −2.827**
ETF
Risk Index
−5.303 −6.368 −15.185 −9.241 −4.533 −6.670 −5.520 −5.605 −5.859 −6.863 −5.307 −6.494 −4.595 −5.734 −5.992 −7.438 −6.537 −6.802 0.291
−4.855 −6.368 −9.283 −9.241 −6.568 −6.670 −5.683 −5.605 −6.892 −6.863 −6.592 −6.494 −5.653 −5.734 −7.330 −7.438 −6.607 −6.802 1.022
ETF
Minimum Index
5.934 5.408 19.626 9.500 5.210 5.114 4.508 4.408 9.365 8.047 5.281 6.541 3.811 4.010 7.609 8.412 7.668 6.430 0.953
5.254 5.408 9.558 9.500 5.056 5.114 4.358 4.408 7.997 8.047 5.163 6.541 4.256 4.010 8.475 8.412 6.265 6.430 −0.926
ETF
Maximum
793 1978 611 615 783 786 745 777 886
793 1978 611 615 783 786 745 777 886
Obs.
at the 1% level. ⁎⁎Significant at the 5% level. This table presents the descriptive statistics of iShares and their benchmarks as of April 24, 2013. Descriptive statistics include the average daily return, the median return, the standard deviation, and the extreme scores (minimum and maximum). The statistics of iShares are presented both in NAV and closing trading prices. T-test assesses whether the difference in descriptive between iShares and indices is statistically significant.
⁎Significant
0.000 0.005 0.000 0.051 0.000 −0.046 0.000 0.078 −0.142 −0.091 0.000 −0.010 0.000 0.013 0.000 −0.013 −0.018 −0.002 −1.107
Index
Panel B: Closing Trading Prices ETF1 −0.037 −0.018 ETF2 0.068 0.070 ETF3 −0.014 0.019 ETF4 0.005 0.045 ETF5 −0.061 −0.038 ETF6 −0.032 −0.008 ETF7 −0.059 −0.038 ETF8 −0.057 −0.038 Average −0.023 −0.001 T-test −5.802*
ETF
−0.018 0.005 0.064 0.051 −0.018 −0.046 0.043 0.078 −0.082 −0.091 −0.009 −0.010 −0.012 0.013 −0.032 −0.013 −0.008 −0.002 −0.790
Index
Panel A: Net Assets Values ETF1 −0.038 −0.018 ETF2 0.059 0.070 ETF3 −0.001 0.019 ETF4 0.021 0.045 ETF5 −0.059 −0.038 ETF6 −0.030 −0.008 ETF7 −0.058 −0.038 ETF8 −0.055 −0.038 Average −0.020 −0.001 T-test −14.474*
ETF
Table 13.2 Descriptive Statistics of Returns Average Return Median Return
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benchmarks. Once again, there is no statistically and economically significant difference between the median trading price returns of iShares and indexes. When it comes to volatility, the average NAV risk estimate of iShares is equal to 1.634. The corresponding figure of the indexes is 1.645. In trading price terms, the risk of ETFs is equal to 1.718. In any case, there is no statistically significant difference in risk between ETFs and benchmarks. This also is the case for the extreme scores, namely the minimum and maximum return estimations of iShares and indexes. However, both the standard deviation estimates and the range of extreme scores, i.e., maximum minus minimum scores, which range from 12.872 in absolute terms when the NAV returns of iShares are considered to 14.205 when the trading prices of ETFs are taken into consideration (not clearly shown in Table 13.2), show that the Hong Kong ETF market is quite volatile.
13.3 PERFORMANCE ASSESSMENT In this section, the performance of iShares versus the performance of the tracking indexes is assessed. Performance assessment is performed in two ways; first, performance is assessed via a single-index regression analysis in which the daily return of ETFs is regressed on the daily return of the indexes. The regression is performed both for the NAV returns and for the closing day prices of iShares.The second performance appraisal regards the tracking error, which indicates the difference in returns between ETFs and indexes. Again, tracking error is computed with the usage of both NAV and closing day price. Table 13.3 provides the results of the single-index performance regression. Presented in the table are the alpha and beta coefficients, the respective t-tests on the statistical significance of the estimates along with the R-squares, which assess the explanatory power of the model. The average alpha in Panel A is equal to −0.020 being significant at the 1% level. All but one single alpha is statistically significant at the 1% level, too.7 The corresponding alpha obtained from the closing day price return is equal to −0.023 being significant at the 1% level. Interestingly, the single alphas are now statistically insignificant. In any case, iShares do not produce any alpha. This finding is not surprising given the passive nature of the ETFs examined in this chapter. On the question of systematic risk and the replication strategy of iShares, the average NAV beta of 0.982, which is significantly different than the unity, leads to the following two main inferences. The first one is that the iShares are slightly more conservative than 7 We could assume that the negative alpha is due to the impact of expenses. However, the short number of sample’s iShares does not allow the performance of a relevant cross-sectional regression so as to verify this assumption.
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
Table 13.3 Performance Regression Results t-test#0 α
β
t-test#1
R2
Obs.
Panel A: Net Assets Values ETF1 −0.022 ETF2 −0.011⁎ ETF3 −0.019⁎ ETF4 −0.024⁎ ETF5 −0.021⁎ ETF6 −0.023⁎ ETF7 −0.020⁎ ETF8 −0.018⁎ Average −0.020 T-test −13.421*
−1.236 −3.080 −4.419 −4.900 −3.978 −4.442 −4.865 −5.322 −4.030
0.917⁎ 0.988⁎ 0.992 0.995 0.991⁎⁎ 0.984⁎⁎⁎ 0.995 0.995⁎⁎⁎ 0.982 −1.903***
−5.458 −3.942 −1.495 −1.463 −2.107 −1.842 −0.767 −1.970 −2.381
0.882 0.990 0.992 0.992 0.993 0.989 0.982 0.996 0.977
793 1978 611 615 783 786 745 777 886
Panel B: Closing Trading Prices ETF1 −0.020 ETF2 0.000 ETF3 −0.033 ETF4 −0.033 ETF5 −0.026 ETF6 −0.025 ETF7 −0.027 ETF8 −0.022 Average −0.023 T-test −6.300*
−0.595 −0.017 −1.273 −1.086 −1.299 −0.952 −1.155 −0.792 −0.896
0.899⁎ 0.973 0.865⁎ 0.808⁎ 0.885⁎ 0.851⁎ 0.835⁎ 0.903⁎ 0.877 −6.910*
−3.334 −1.154 −5.105 −8.339 −6.400 −7.304 −7.466 −5.054 −5.519
0.609 0.696 0.685 0.656 0.786 0.730 0.652 0.784 0.700
793 1978 611 615 783 786 745 777 886
⁎Significant
at the 1% level. ⁎⁎Significant at the 5% level. ⁎⁎⁎Significant at the 10% level. This table presents the results of the performance regression between iShares and benchmarks. The iShares’ daily return is regressed on the corresponding return of benchmarks. Alpha coefficient reflects the return that can be achieved by an iShares independently to the index return. Beta counts for the systematic risk of iShares and indicates the replication efficiency of iShares. T-tests on alphas estimate the statistical significance of the difference of these coefficients from zero. T-tests on betas indicate the significance of the difference of estimates from the unity. R-square assesses the explanatory power of the regression.
the benchmarks (average beta lower than unity). The second inference is that iShares do not pursue a full replication strategy. This statistical finding is in line with the statement on the Web site of iShares about the synthetic replication techniques adopted by the iShares listed in the Hong Kong Stock Exchange. The results on systematic risk and benchmarks’ replication obtained with the usage of trading data are qualitatively equal to these of NAV returns. The average beta is equal to 0.877 being inferior to the respective average NAV beta. This average beta is significant at the 1% level. Moreover, all but one single beta is different than unity. In the case of NAV returns, five out of eight betas are significantly different than zero. Therefore, the conservativeness and the deviation from the full replication methods are also confirmed by the trading data of iShares.
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Table 13.4 Tracking Error TE1
Panel A: Net Assets Values ETF1 −0.020 ETF2 −0.011 ETF3 −0.019 ETF4 −0.024 ETF5 −0.020 ETF6 −0.022 ETF7 −0.020 ETF8 −0.017 Average −0.019 T-test −14.474* Panel B: Closing Trading ETF1 ETF2 ETF3 ETF4 ETF5 ETF6 ETF7 ETF8 Average T-test ⁎Significant
Prices −0.019 −0.002 −0.033 −0.040 −0.003 −0.024 −0.021 −0.019 −0.020 −4.343*
TE2
TE3
Obs.
0.507 0.191 0.149 0.138 0.161 0.175 0.184 0.128 0.204 4.647*
0.492 0.188 0.144 0.137 0.159 0.171 0.175 0.124 0.199 4.662*
793 1978 611 615 783 786 745 777 886
1.066 1.314 0.962 0.936 0.918 0.874 0.819 0.908 0.974 17.841*
1.048 1.266 0.915 0.872 0.880 0.827 0.777 0.875 0.933 16.942*
793 1978 611 615 783 786 745 777 886
at the 1% level. Note: TE1 refers to the average return daily return difference between each iShares and its benchmark.TE2 is the standard deviation of daily return differences. TE3 is the standard errors of the single-index performance regression. This table presents the estimations of tracking error as of 24 April 2013, which reflects the deviation between the return of iShares and benchmarks.Tracking error is calculated via three distinct methods labeled as TE1,TE2, and TE3.The t-test assesses whether the average tracking errors are statistically different from zero.
Table 13.4 provides the tracking error computations with three alternative tracking errors used.The first one concerns the raw average difference in returns between iShares and indexes. The second method deals with the standard deviation in return differences between ETFs and indexes, which is a method that is standard in the literature.8 The last tracking error measurement considers the standard error of the performance regression described above. The average tracking error estimation obtained via the first method is equal to −0.019 being quite similar to the average NAV alpha (i.e., −0.020). This estimate is statistically significant at the 1% level. The average of TE2 and TE3 is equal to 0.204 and 0.199, respectively, both being significant at the 1% level. When the trading prices 8
E.g., refer to Frino and Gallagher (2001).
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
returns of iShares are considered, the tracking errors are worse, i.e., they show that the divergence in returns between iShares and indexes is greater in trading data terms. The average TE1 is significant and equal to −0.020 (once approaching the average alpha reported in Panel B of Table 13.3). However, the average of TE2 and TE3 is equal to 0.974 and 0.933, respectively, both being much higher than the TE2 and TE3 derived from the NAV returns. Overall, the results on tracking error calculations indicate that the performance of iShares listed in the Hong Kong Stock Exchange substantially deviates from the return of the tracking index portfolios. This finding is line with the analysis of raw returns in Table 13.2 and the regression analysis of performance in Table 13.3.
13.4 PRICING EFFICIENCY ASSESSMENT In this last section of the chapter, the pricing efficiency of iShares is examined.The pricing efficiency is assessed via the computation of the premium/discount in the trading price returns of iShares. The premium is calculated in percentage terms as the fraction of day’s t closing price minus the day’s t NAV for each ETF divided by the day’s t NAV. In addition, the persistence of premium is investigated by running a simple time series regression model of premium values on its one-day lagged values. This approach in assessing the persistence of premium has been found in Elton et al. (2002). Table 13.5 reports the descriptive statistics of ETFs’ premium. In particular, the average and median premium of each ETF are presented along with the standard deviation of daily premiums (used to assess the volatility in premium) and the extreme scores, namely the lowest premium (i.e., discount) and the maximum premium observed for each ETF. The results in Table 13.5 show that the average iShares trade at a significant premium to its NAV of 4.167%. This figure is quite higher than the average premiums of US-listed ETFs reported in the literature.9 The median premium is slightly better yet, but still high (it is equal to 3.849). The average standard deviation of premiums is equal to 3.792. This number is extremely high and indicates that there are a lot of fluctuations in terms of pricing behavior of Hong Kong iShares, whereby the latter inference is confirmed by the extreme scores. In particular, the average minimum discount of the sample is equal to 4.623%, whereas the average maximum premium equals the 14.715%. The main inference drawn via the analysis of iShares’ premiums is that the specific ETFs deviate from what is the case for the majority of US-listed ETFs, whose premiums are usually low and very short-lived. The large premiums found in the pricing of Hong Kong iShares raise questions about the applicability of the efficient market hypothesis in the case of Hong Kong ETFs. 9
E.g., Rompotis (2010b) reports an average premium of US-listed iShares of 0.059%.
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Table 13.5 Descriptive Statistics of Premium Average Median Standard Deviation
Minimum
Maximum
Obs.
ETF1 ETF2 ETF3 ETF4 ETF5 ETF6 ETF7 ETF8 Average
−3.840 −16.640 −2.100 −4.950 −3.170 −2.720 −1.610 −1.950 −4.623
9.160 23.430 13.740 14.820 13.780 14.940 13.710 14.140 14.715
793 1978 611 615 783 786 745 777 886
1.963 3.655 5.056 4.353 4.379 4.708 4.656 4.565 4.167
1.590 2.930 4.960 3.870 4.200 3.985 4.730 4.530 3.849
2.224 5.052 3.523 3.996 3.951 4.122 3.734 3.736 3.792
Note: Negative premiums are named discounts. This table presents the descriptive statistics of iShares’ premium as of 24 April 2013. Descriptive statistics include the average daily premium, the median premium, the standard deviation of premium’s values, and the extreme scores (minimum and maximum).
Table 13.6 Premium Regression Results t-test α
β
t-test
R2
Obs.
ETF1 ETF2 ETF3 ETF4 ETF5 ETF6 ETF7 ETF8 Average
0.980⁎ 0.976⁎ 0.975⁎ 0.986⁎ 0.983⁎ 0.984⁎ 0.977⁎ 0.976
77.478 96.695 113.163 101.208 163.542 145.201 150.262 123.856 121.426
0.854 0.935 0.922 0.939 0.943 0.953 0.946 0.937 0.929
793 1978 611 615 783 786 745 777 886
⁎Significant
0.106⁎
0.044 0.104⁎⁎⁎ 0.090⁎⁎⁎ 0.060⁎⁎⁎ 0.081⁎⁎⁎ 0.074⁎⁎ 0.105⁎⁎ 0.083
2.999 1.083 1.997 1.722 1.833 1.931 2.081 2.584 2.029
0.945⁎
at the 1% level. ⁎⁎Significant at the 5% level. ⁎⁎⁎Significant at the 10% level. This table presents the results of the premium’s regression on its one-day lagged values. Beta counts for the persistence in iShares’ premium. T-tests on alphas estimate the statistical significance of the difference of these coefficients from zero. T-tests assess the significance of the difference of estimates from zero. R-square assesses the explanatory power of the regression.
The regression results on premiums are provided in Table 13.6. The average beta estimate is equal to 0.976. All the single betas approximate to unity being statistically significant at the 1% level. This finding is indicative of high persistence in Hong Kong iShares’ premium from the day t − 1 to the day t. This finding contrasts the literature’s findings on US-listed ETFs.10 Persistence in premium may be related to statutory and other frictions that can make arbitrage execution less efficient. 10 E.g., Engle and Sarkar (2006) show that the pricing errors for domestic US ETFs are small and quickly reversed. However, in the case international ETFs listed in the USA, the premiums/discounts are greater and long-lasting.
The Trading Behavior of iShares Listed on the Honk Kong Stock Exchange
13.5 CONCLUSION Given the increasing popularity of ETFs worldwide, they have generally been of great interest to researchers investing ETFs listed in the US market but examining tracking indexes from Asian stock markets.The current chapter examines several issues surrounding the trading behavior of eight iShares listed in the Hong Kong Stock Exchange. In particular, the performance and volatility of these ETFs are examined along with their tracking and pricing efficiency. The main findings of the chapter are summarized as follows: At first, the iShares are found to significantly underperform their tracking index portfolios. This finding is consistent both when NAV and trading prices returns are assessed. The risk of iShares is comparable to that of the indexes, but there are no significant differences between ETFs and indexes when volatility is in question. Going further, the performance regression results confirm that the iShares cannot produce any meaningful alpha given that they are passively managed investment vehicles suitable to investors wishing to access beta exposure without expecting any excess market return. In addition, Hong Kong iShares are found not to adopt full replication techniques in order to outperform the underlying indexes. This finding is in line with information found on the Web site of Hong Kong iShares, stating that the specific products seek to deliver the return of the tracking benchmarks in a synthetic way. When it comes to the ability of iShares to efficiently replicate their underlying assets, the results demonstrate that they basically fail to do so. More specifically, the tracking error calculations show that there is a significant difference in returns between iShares and underlying index portfolios. This finding reconfirms the return discrepancy already revealed via the analysis of raw returns of iShares and indexes as well as through the performance regression analysis. Finally, when the pricing efficiency of iShares is investigated, the results show that the average Hong Kong iShares trade at a substantial premium to their NAV. The magnitude of the average premium is quite unusual when compared to the respective premium estimates of US-listed ETFs. In addition, the premium is highly persistent on a daily basis, which implies that a significant premium on one day is followed by an equally significant premium the next day. Both the magnitude and persistence in premium may indicate the existence of serious statutory and other obstacles in the application of efficient arbitrage which would contribute to the narrowness of the gaps between iShares’ trading and net assets vales in a very efficient way.
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