Performance of Islamic and conventional stock indices: empirical evidence from an emerging economy
 Md Ejaz Rana^{1} and
 Waheed Akhter^{1}Email author
DOI: 10.1186/s4085401500163
© Rana and Akhter. 2015
Received: 5 June 2015
Accepted: 11 November 2015
Published: 2 December 2015
Abstract
Background
This study aims to investigate the extent to which the conditional volatilities of both Shari’ah compliant stock and conventional stock are related to those of interest rate and exchange rate in the emerging economy of Pakistan.
Methods
We used KMI 30 and KSE 100 indices for Islamic and conventional stock for the period of July 2008 to November 2013. We employed Generalized Autoregressive Conditional Heteroskedastic in the mean (GARCHM) model. This framework relaxes constancy assumption of classical linear regression (CLRM) model and allows exchange rate and interest rate volatility to evolve over time. The GARCHM framework also reveals results about riskreturn tradeoff in the context of both Islamic and conventional stock indices.
Results
The findings show positive and statistically significant effect of interest rate volatility on KSE100, whereas KMI30 remains unaffected by the same. Exchange rate volatility is found to be significant for both conventional and Islamic indices. The relationship of risk coefficient (γ) and stocks returns, as expected, is positive and statistically significant for both KMI30 and KSE100. This result is consistent with the theory of riskreturn tradeoff. The results of parametric ttest show significant difference between returns of both indices. This implies that Shari’ah compliant stock index (KMI30) of Pakistan underperforms its conventional counterpart.
Conclusion
By using different performance measures (Sharp ratio, Jensen alpha, Treynor ratio), this study also investigates the hypothesis that Islamic stock index has inferior performance compared with unscreened conventional counterparts due to availability of a smaller investment universe, increased monitoring costs, and limited diversification.
Keywords
KMI30 KSE100 Index Shari’ah Exchange rate volatility Interest rate volatility Stock Performance etcBackground
The Shari’ah screenings criteria applied by the Islamic scholars have enabled Shari’ah Complaint Stock Indices to distinguish themselves from conventional stocks indices. In general, there are two Shari’ah screening criteria – positive and negative. Positive screenings allow an Islamic index to include those companies that meet certain Islamic ethical indicators (both quantitative and qualitative) whilst negative screenings delete stocks which are unable to meet such requirements. The common stock guidelines, accepted by Shari’ah scholars, have become a key factor in the growth of Islamic funds all over the world. Majority of the Shari’ah scholars are agreed that buying and selling of stocks &shares adhere to Shari’ah laws because shares & stocks represent real assets. In addition, the dividend payments are also in accordance with Shari’ah indicators since receipt/payment of interest (Riba) is unlawful (Haraam) in Islam. Hence, equities, mutual funds, and government bonds are considered more compatible with Shari’ah screenings criteria of profit and risk sharing than fixed income assets.
The researches in Islamic finance have always been interested in investigating the question whether returns earned by the investors of Islamic funds/indices are different from conventional funds investors. In addition, the researchers are also examining whether adhering to Shari’ah law creates any impact on the performance of Islamic funds /indices because such funds suffer from restricted assets selection, limited investment practices, and smaller investment universe, Hassan (2001). Fundamentally, there are essentially two opposing views regarding Shari’ah screening effects on returns earned by Islamic indices. Opponents of Islamic ethical investing argue that implementing Islamic screening may result in limited investment universe due to potential increase in volatility, reduced diversification, and monitoring costs. In particular, due to Islamic screening, stable blue chip and larger firms may be excluded (due to high leverage) from Islamic index and as a result, remaining firms tend to be smaller and prevent investors to have other attractive investment opportunities from further consideration, Guyot (2011).
On the other hand, advocates of Islamic investing argue that financial and Islamic ethical screens propose a good economic and social sense to evaluate potential investments. Islamic ethical investors can align their potential investments with their religious & ethical beliefs that will not only give them peace of mind but also a lawful (Halaal) monetary reward. In addition, the empirical findings of Myers (1993), Fama and French (1998 and 2002), ShyamSunder and Myers (1999) argue that most profitable firms borrow least, therefore there exist a negative relationship between profitability and leverage. Hence, Islamic index can outperform its conventional counterpart because all firms included in the any Islamic Index have low leverage ratios.
Although Islamic funds have shown a massive growth over the past few decades, the empirical literature on the performance analysis of such funds is still at its initial stage. The limited literature provides somewhat mixed results regarding performance of Shari’ah screened funds/Indices compared to their unscreened counterparts. For example, Hakim and Rashidian (2002) analyzed the performance of Dow Jones Islamic Index (DJIM) against its conventional counterparts; Dow Jones world index (DJW) and Dow Jones Sustainability World index (DJS). They applied capital asset pricing model (CAPM) and reported that DJIM index has outperformed DJW but has underperformed DJS index. The same results are reported by Hussein and Omran (2005). This study documents that during entire bull period DJIM index has outperformed its counterpart, but has underperformed during the bear market period. On the other hand, Hoepner et al. (2011) examine performance differences of 62 Islamic equity funds collected from 20 different countries. They report that Islamic funds from 8 western nations are unable to outperform their equity benchmarks, whereas only 3 funds have, somehow, performed relatively well against their market benchmarks. In addition, Dharani and Natarajan (2011) find no significant differences between the performance of Indian Shari’ah compliant stocks and conventional stocks indices during the period of 2007 to 2011. They report that average return earned by Shari’ah compliant stocks is similar to conventional stocks returns.
To the best of our knowledge, our study is the first attempt towards analyzing the performance of Islamic index with its conventional counterpart in an emerging economy which is struggling to Islamize its monetary and financial system of the country Pakistan is one of those few emerging economies who started Islamization of monetary and financial system of the country in early 80s. As a result of Islamization efforts, Supreme Court of Pakistan, on 14 November 1991, declared bank interest as “Riba” and prohibited all types of interest prevailed in monetary and financial system of Pakistan. In July 2008, an Islamic Index (KMI 30) is launched with a view to provide a platform for ethical investors who seek to align their financial objectives with their religious beliefs and value systems. Beside increasing investor trust and enhancing their participation, KMI30 index also serves as research tool for measuring performance of Shari’ah compliant stocks and strategic assets allocation procedure. In this regard, it is really need of the hour to assess the performance of Islamic index. This performance analysis will tell us whether Islamization of stocks index rewards its investors or not. In addition, it will also help us to know whether objectives of establishing Islamic index are fulfilled or not. This will also give us insight whether an emerging economy like Pakistan is better at offering ethical investment universe for its local and international investors as well. Therefore, our study is the first attempt of not only analyzing the performance of Islamic stock index of Pakistan but also its response to volatility effects caused by other macroeconomic variables such as interest rate and exchange rate.
Therefore, the primary objective of this study is twofold: first, by using different riskadjusted performance measures such as Jensen’s Alpha (1968), Sharp ratio (1966), Treynor Ratio (1965), and MM (1977), this study investigates the potential impact of Shari’ah screening on the performance of Karachi Meezan Index (KMI30), traded at Karachi Stock Exchange (Pakistan), against its conventional counterparts Karachi Stock Exchange index (KSE100). The study examines whether returns earned by ethical investors who trade Shari’ah compliant stocks (KMI30) are different from conventional investors. Second, to examine the effects of volatilities of interest rate and exchange rate on KMI30 index and its counterparts, this study employs Generalized Autoregressive Conditional Heteroskedastic in the mean (GARCHM) model. This framework relaxes constancy assumption of classical linear regression (CLRM) model and allows exchange rate volatility and interest rate volatility to evolve over time. The GARCHM framework also reveals results about riskreturn tradeoff in the context of returns earned by Islamic and conventional investors.
Literature review
Contrary to the literature of Islamic banks and Islamic mutual funds, Islamic indices have not received high level of empirical research due to their shorter histories (El Khamlichi and Laaradh 2012). In addition, the performance comparison of Islamic indices against conventional counterparts is also complicated owing to different factors such as differences in size and industryweighting (Fowler and Hope 2007). Therefore, earlier studies such as Naughton and Naughton (2000) use qualitative approach to discuss the initial stage of Islamic stock indices in terms of regulations, financial principles, and market framework.
However, previous studies on the performance analysis of Islamic indices provide somewhat mixed results. This difference among the results can be attributed to different performance measures, sample data, and different benchmarks used by these studies. For example, Atta (2000) analyzed the performance of Dow Jones Islamic Index (DJIM) against market index and riskfree rate. He reports that DJIM has not only outperformed its conventional counterparts, but also offered more returns than riskfree rate. Same results are reported by Hassan (2001) where he investigated the performance of 6 Dow Jones Islamic indices. His results also confirm superior efficiency of Islamic indices against counterparts. On the contrary, Girard and Hassan (2005) do not find any significant performance differences between of Dow Jones Islamic indices and 7 Morgan and Stanley conventional indices. They use different performance measures to check the robustness of the results. From single factor CAMP to four factor conditional CAMP; they report that results remain same for the period of 1996–2005. They also report that growth and small stocks are core drivers of the positive performance for Islamic indices. The same results are reported by Dabeerru (2006) about the performance of Saudi Arabian Islamic indices. He reports that Shari’ah screenings do not lead to good performance of Saudi Islamic indices.
The family of Dow Jones Islamic Market Index got attention of most earlier empirical studies and include the work of Atta (2000), Hassan (2001), Tilva and Tuli (2002), Hakim and Rashidian (2002; 2004). These studies compared the performance of Dow Jones Islamic market index (DJIM) against a conventional benchmark. However, the choice of performance measures and benchmark remain different from one researcher to another. Atta (2000) used market conventional index and 3 month riskfree rate as benchmark against DJIM and concluded superior performance of DJIM than riskfree rate and conventional index. His results are further supported by Hassan (2001) who used same benchmark with different data set (1996–2000). He documents that 6 DJIM indices are more efficient than conventional index. On the other hand, Tilva and Tuli (2002) used different conventional benchmark (S&P 500) with different performance measure of Fama and French 3 factor model. His results show that Islamic and conventional indices are highly correlated and have no significant performance difference. Hakim and Rashidian (2000) analyzed the performance of DJIM with Wilshire 5000 and 3 moths TBill with weekly data set. They conclude less performance of Islamic index. Hakim and Rashidian (2004) again analyze the performance of DJIM but with different benchmark (Green Index, DJ World, Libor) with different data set (2000–2004). Islamic index earned inferior returns as compared to Green index (socially responsible index).
On the other hand, family of Financial Times Stock Exchange Islamic Index (FTSE Islamic Index) analyzed by Hussein (2004), Miglietta and Forte (2007), Girard and Hassan (2008) who also employed different empirical models, benchmarks and performance measures to examine the FTSE Islamic Index. Hussein (2004) uses FTSE all world and FTSE4good as benchmark against FTSE Islamic Index to compare the performance difference. He comes up with somewhat complicated results. He analyzed performance of these indices over three different intervals; bullish, bearish and entire time horizon over the period of 1996–2003. During bullish period, Islamic index outperformed its conventional counterpart, whereas during whole time period both indices perform same. Since Islamic investing is a part of socially responsible investing (SRI), Elgari (1993) and Miglietta and Forte (2007) compared FTSE Global Islamic index to FTSE socially responsible index by employing Sharpe’s analysis (Sharpe, 1946) and cointegration techniques. They report that although Islamic index looks similar to SRI, however both are quite unique in terms of both assets allocation and econometric profile. SRI indices are exposed financial sectors whereas Islamic indices are invested in Oil & Gas sectors. Also, there exists cointegration between 3 months Euribor and FTSE Islamic index. Another study (Hashim, 2008) compares FTSE Islamic index and SRI (FTSE 4 Good). By employing CAMP and other traditional performance measures such as Jensen’s Alpha, Sharpe, and Treynor, he concludes that FTSE Islamic index is more efficient, even though more riskier than the market, and yields positive abnormal returns as compared to SRI index.
The study of Girard and Hassan (2008) is considered as a gateway into the empirical literature of Islamic indices. By employing sharp Ratio, Treynor Ratio, and Jensen’s Alpha, they compared 5 FTSE Islamic indices and 5 conventional benchmarks MSCI. They also employ Fama’s selectivity, net selectivity, and diversification to examine the style and timing ability of fund managers. In addition, Charhart (1997) fourfactor model is used to examine the performance persistence of Islamic indices. They report insignificant performance differences between FTSE Islamic indices and their counterparts due to style and timing ability of fund managers. The results remain same even after controlling for other factors like market risk, size, booktomarket, momentum, and local and global factors. Also, there exists cointegration between Islamic and nonIslamic indices for overall period.
In addition, some studies have analyzed the performance of Islamic indices of particular countries instead of examining the performance of world’s famous Islamic indices. These studies include (Ahmad and Ibrahim 2002; Nishat 2004; Dabeerru 2006; Yusof and Majid 2007; Albaity and Ahmad 2008; and Fahmi et al. 2009). For example, in Malaysia, the performance of Kuala Lampur Shari’ah Index (KLSI) have been analyzed by Ahmad and Ibrahim (2002), Yusof and Majid (2007), and Albaity and Ahmad (2008). No significant performance differences between Islamic and NonIslamic indices have been reported by Ahmad and Ibrahim (2002). Also, during the bull market period, Islamic index is less performing against its conventional counterparts. Albaity and Ahmad (2008) report similar results for Kuala Lampur Shari’ah Index (KLSI) and Kuala Lampur Composite index (KLCI). They also examined causality between both indices and find bidirectional causality.
Recent studies on performance analysis of global Islamic indices include Guyot (2011), El Khamlichi and Sarkar (2012), JouaberSnoussi et al. (2012), and Arouri et al. (2013). Guyot (2011) analyzes nine Dow Jones Islamic indices (DJIM) and finds no cointegration between Islamic and nonIslamic indices. He also reports that both indices have no performance difference and efficiency & liquidity of both indices is similar during the study period. Another study that examines efficiency of 4 Dow Jones Islamic indices is El Khamlichi and Sarkar (2012). These results about efficiency level of Islamic and nonIslamic indices are similar to those of Guyot (2011). They document that Islamic indices are as efficient as conventional MSCI and FTSE indices are. In addition, Dow Jones Islamic index and S&P Islamic index are not cointegrated with their conventional counterparts.
Arouri et al. (2013) examines the impact of current global financial crisis on 3 Dow Jones Islamic indices to see whether Islamic finance constitute a potential solution in reassuring investors and stabilizing financial systems to escape from financial downturns. They employ Multivariate Vector Autoregressive (VAR) and Granger Causality test to test the interaction between Islamic and conventional financial products and specify the dependence orientation of feedback between screened and unscreened stock prices, respectively. Moreover, to ensure the best resource allocation, they develop portfolio simulation and optimal portfolio strategies (Proportional investment for both Islamic and conventional funds). They find that inverting in Islamic financial products yields higher returns and systemic risk of such portfolios, which includes Islamic financial products, is reduced significantly.
Methods

H_{o}: The return of KMI30 index is not significantly different from its conventional counterpart (KSE100 index)
For this purpose, four different riskadjusted performance measures (explained below) have been used to analyze the performance differences between KMI30 index (Shari’ah compliant index) and its conventional computer partKSE100,for examining volatility effects of interest rate and exchange rate on these two indices, we have used GARCHM model (explained below). In addition, long run performance of both Islamic and nonIslamic indices has also been analyzed.
Daily closing values of KMI30 and KSE100 have been collected from database of Karachi Stock Exchange for the period of July 2008 to November 2013. Daily closing value of interest rate and weighted average exchange rate is also taken from July 2008 to November 2013. The daily yield of 3 months TBills is used as proxy of short term interest rate and is taken from web site of State Bank of Pakistan (SBP). The daily closing value of weighted average exchange rate, measured as simple basket of equally weighted currencies (US $, GBP, EURO), is obtained from State Bank of Pakistan (SBP). Conditional variance of interest rate series and exchange rate series represent the volatility of both series.
Riskadjusted performance measures
 a.
Jensen’s Alpha
The first performance measure used in this study is Jensen’s Alpha, known as absolute riskadjusted measure of returns. Based upon Sharpe (1964), and linter (1965) CAPM, Michael Jensen used Jensen’s Alpha in 1970 to estimate excess returns earned by a security or a fund. The basic advantage of Jensen’s alpha is that it explains whether the null hypothesis of neutral performance of an Islamic index, i.e. no screening effect or alpha is equal to zero, is statistically significant by employing tstatistics. A positive or negative value of alpha reflects superior or inferior performance of an index, respectively.
 b.
Sharpe Ratio
The second performance measure is Sharpe Ratio, also known as relative riskadjusted measure of returns, developed by Sharpe in 1966 and derived from Capital Market Line. The basic advantage of Sharpe measure is that it provides additional returns per unit of total risk (both systematic and unsystematic) for a security/index. Since risk is measured by standard deviation of the index, this measure gives us tradeoff between risk and return. Therefore, this ratio explains how well an investor is compensated for assuming additional risk. Higher Sharpe ratio reflects superior performance of an index.
 c.
Treynor Ratio
The Treynor ratio (TR) also measures the additional returns per unit of risk, but contrary to Sharpe ratio, TR Considers only systematic risk instead of both systematic and nonsystematic risk. A benchmark is required for computing this relative riskadjusted measure. TR is considered better performance measure as compared to SR since TR provided better picture of a large diversified portfolio’s beta that is computed from CAPM equation.
MM performance measure
MM is an extension to Sharpe Ratio and developed by Modigliani and Modigliani in 1977. This relative risk adjusted performance measure provides an index’s performance to the market in percentage terms by taking same standard deviation. Moreover, to investigate the longrun performance of all indices, this study uses two most commonly used methods; Cumulative Returns (CRs) and Buyand –Hold Returns (BHRs), since literature shows no agreement on the appropriate methodology for computing long run returns (i.e. Brav and Gompers, 1997, Barber and lyon, 1977). The Jensen’s riskadjusted return model is used to compute CRs and BHRs.
Volatility measure (GARCHM) for Shari’ah screened index and conventional indices
Where R _{ m,t } is the stocks returns of m ^{th} Index (KMI30, KSE100), ΔFX _{ t } is the changes in foreign exchange rate, ΔINT _{ t } is the changes in 3 months TBills yield and subscript tis time index for all variables. The index volatility (risk) is measured by variable (h _{ m,t }), INT _{ t − 1} is short term interest rate volatility, FX _{ t − 1} is foreign exchange volatility, and π _{0}, π _{ i }, θ _{1}, θ _{2}, θ _{3}, γ, α _{0}, α _{1}, β, δ _{1}, and δ _{2} are parameters. Representation of volatility (h _{ t }) in logarithmic form is consistent with Elyasiani et al. (1995) and Lloyd and Shick (1977).
The above model is more practical than basic ARCH and GARCH models. First, it examines the impact of volatility on risk premium. Second, the inclusion of (h _{ t }) will examine the fundamental relationship of risk and return. If (h _{ t }) is significant, then there exists a relationship between risk (volatility) and returns. The sign and magnitude of this relationship, measured by γ, may be positive, negative, and zero.
Results and discussion
Descriptive Statistics
Index Name  Mean  Median  Maximum  minimum  S.D.  Skewness  Kurtosis  JarqiueBera 

Conventional index (KSE100)  67.004  62.01  134.7  20.88  22.01  −0.882  3.29  3322.32* 
Islamic Index (KMI30)  35.17  28.71  77.33  8.76  20.55  −0.547  1.974  3886.09* 
INT (Interest Rate)  13.12  10.32  15.3  1.45  1.94  0.752  2.87  4721.12* 
FX (Foreign Exchange)  69.813  62.71  86  59.71  10.48  0.391  1.31  1573.05* 
The average returns (mean) of both indices are also shown in Table 1. It is clearly evident from mean values that KMI30 earns less return (35.17) than KSE100 return (67.004), which suggests that that we cannot reject our null hypothesis of lower returns earned by Islamic index. The lower returns earned by KMI30 is also supported by its standard deviation (0.36754), a measurement of risk, which shows that KMI30is less risky. Moreover, KSE100 also shows superior longterm returns (measured by sum of all returns). Table 1 also shows correlation coefficients for both series that suggest a positive relationship between both indices. The correlation coefficient is 86 %, which is as strong as reported by Ahmad and Ibrahim (2002). One possible explanation of such strong correlation between both indices is that most of the stocks listed under KMI30 are also listed under KSE100. Therefore, both indices move together as also depicted in Fig. 1.
OLS Estimation (CAPM)
CAPM Regression (tvalues in parenthesis)
Index Name  Alpha  Beta  R^{2} 

Conventional index (KSE100)  0.04325 (0.0325)**  0.9837 (0.0000)*  0.89 
Islamic Index (KMI30)  0.03911 (0.1321)  0.9235 (0.0000)*  0.82 
Mean difference
Differences in Mean between KMI30 and KSE100 (ttest)
Mean Difference  tvalue  Pvalue for ttest 

0.0001  5.58  (0.0423)** 
Riskadjusted performance evaluation
RiskAdjusted Performance Evaluation
Index Name  Sharpe Ratio  Treynor Ratio  Jensen’s Alpha  MM 

Conventional index (KSE100)  0.00694  0.00076  0.0156  0.0387 
Islamic Index (KMI30)  0.00401  0.00002  0.0032  0.0532 
LongRun Performance of KMI30 and KSE100
Index Name  Cumulative Returns  BuyandHold Returns 

Conventional index (KSE100)  0.4886 (2.53)**  0.6985 (3.93)** 
Islamic Index (KMI30)  0.2641 (0.036)  0.5543 (0.041) 
Estimated conditional returns with GARCH (1, 1) Model
UnitRoot Analysis
Augmented Dickey Fuller  PhillipsPeron  

Index Name  At Level  At Difference  At Level  At Difference 
Conventional index (KSE100)  −2.132 (0.2319)  −48.251 (0.0001)  −2.120 (0.2366)  −49.54 (0.0001) 
Islamic Index (KMI30)  −0.6051 (0.8670)  −47.1839 (0.0001)  −0.6437 (0.858)  −47.187 (0.0001) 
FX (Foreign Exchange)  0.3045 (0.9785)  −11.4329 (0.0000)  −0.5389 (0.9880)  −32.8513 (0.0000) 
INT (Interest Rate)  −1.3531 (0.6065)  −22.2255 (0.0000)  −3.2041 (0.0199)  −133.068 (0.0000) 
Estimated Conditional Returns with GARCH (1, 1)
\( {R}_{m,t}={\pi}_0+{\displaystyle \sum_{i=1}^n{\pi}_i{R}_{m,ti}}+{\theta}_1\varDelta F{X}_t+{\theta}_2\varDelta IN{T}_t+\gamma log\left({\mathrm{h}}_{\mathrm{m},\mathrm{t}}\right)+{\varepsilon}_{\mathrm{m},\mathrm{t}} \)  

Panel A (Conditional Mean Equation)  
Index Name  ARCH (1)  π _{0}  π _{ i }  θ _{1}  θ _{2}  γ  Adjusted R^{2} 
Conventional index (KSE100)  63.2085 (2.342)*  9.2404 (12.765)***  0.082 (23.983)*  0.659 (21.265)**  0.0033 (24.76)***  1.682 (21.87)**  0.2973 
Islamic Index (KMI30)  34.8262 (12.84)*)  4.2196 (1.543)**  0.0049 (41.874)*  0.6619 (17.65)**  0.6619 (2.543)  2.4321 (1.042)*  0.2234 
Volatility estimates
\( {h}_{m,t}={\alpha}_0+{\alpha}_1\kern0.5em {\varepsilon}_{m,t1}^2+\beta {h}_{m,t1}+{\delta}_1F{X}_{t1}+{\delta}_2 IN{T}_{t1} \)  

Panel B (Conditional variance Equation)  
Index Name  α _{0}  α _{1}  β  α _{1} + β  δ _{1}  δ _{2} 
Conventional index (KSE100)  39.7251  0.93999  0.0044  0.2499  0.51419  0.36302 
(1.322)*  (9.412)*  (23.74)*  (19.52)  (64.921)*  (28.32)*  
Islamic Index (KMI30)  53.9856  0.7996  0.2247  0.0486  6.3333  0.5941 
(1.180)*  (1.850)*  (13.854)*  (0.520)  (18.95)*  (0.2391) 
It can be seen from Panel “A” of Tables 7 and 8 that conventional index (KSE100) is significantly affected by the changes in interest rate and exchange rate. The estimated parameters for exchange rate and interest rate are 0.659 and 0.0033, respectively. This result fits well with stock valuation model that argues that discounted present values of a firm’s future cash flows are represented in stock prices. The stock prices usually reduce with an increase in interest rate and eventually the returns. Therefore, it can be safely said that changes in interest rate is a major factor behind the instability of conventional stock index and, accordingly, investors are also more sensitive towards fluctuations in interest rate. Perhaps, the conventional stock market must be stabilized by the government by controlling interest rate. This finding is consistent with the study Yusof and Majid (2007) who document that Malaysian conventional market is affected by higher interest rate.
On the other hand, as shown in Panel A, for Islamic index (KMI30), interest rate is not a significant factor to predict the excess returns. The tenet of Islamic principles is highlighted by the findings that interest rate is not a determining variable in explaining KMI30’s volatility. Whereas, KIM30 is significantly affected by the changes in exchange rate whose estimated parameter is 0.6619. On the whole, it is found that 22 % of the volatilities in exchange rate and interest rate can predict the volatility of KMI30 with volatility in exchange rate remain the most significant. Whereas, for KSE100, the predictive power of both interest rate and exchange rate volatility is increased from 22 to 29 %. Muslim investors around the globe do not want to maximize their profits but are also concern whether stocks are Shari’ah compliant.
Over the last decade, there has been immense growth in literature that investors go beyond maximizing results and that they are concern with ethical dimensions of their investments. Therefore, interest rate is not a decisive factor in the context of Pakistani investors who seek to invest in KMI30. Moreover, the exchange rate is found to be a determining factor for volatility of KMI30. Hence changes in volatility of returns of KMI30is significantly produced by the changes in macroeconomic conditions like exchange rate. In addition, interest rate alone is not responsible for stock market volatility but also the exchange rate. Both conventional and Islamic stock indices must be stabilized by the government agencies by designing and implementing suitable policies.
The last column of Panel A shows the result for theory of riskreturn tradeoff. The relationship of riskreturn tradeoff is measured by the coefficient Gamma (γ). The relationship of risk γ and stocks returns, as expected, is positive and statistically significant for both KMI30 and KSE100. This result is in line with the theory of riskreturn tradeoff and is consistent with previous results of Yusof and Majid (2007). In simple words, whenever there is higher risk assumed by the investors, there is higher return. The implication of the positive relationship is that investors do consider risk associated with individual stock index and expect to be compensated with higher returns when higher risk is assumed. Although every investor has different risk preferences, some are riskaverse and others are riskseekers, however, every investor expects higher return when he assumes higher risk.
Panel B of Tables 7 and 8 reports the results about conditional variance equation in which impact of exchange rate volatility & interest rate volatility on both indices’ stock returns volatility is examined. In conditional variance equation, α _{0} is intercept term. The timeinvariant component in the stock returns of both conventional and Islamic index volatility is shown by the result of intercept term (α _{0}). The positive and statistically significant value of α _{0}, in both cases, show that stocks returns of KMI30 and KSE100 are highly volatile in nature and contain timeinvariant component. This implication further strengthens the choice of using ARCH type models to analyze volatility of both indices’ returns. In conditional variance equation, both α _{1} and β represents ARCH and GARCH terms, respectively. Both the ARCH and GARCH parameters are positive, which satisfies the nonnegativity condition, and are statistically significant for KMI30 and KSE100. The ARCH parameter α _{1} describes the impact of last period’s shock on volatility, whereas GARCH parameter β shows the impact of previous period’s variance on both indices’ stock return’s volatility. Although both the parameters, ARCH α _{1} and GARCH β, are statistically significant for both indices, however, in magnitude, ARCH parameter a _{1} is smaller than the GARCH parameter β. This implication shows that volatility of both indices are more sensitive to its own lagged value than it is to new surprises. In other words, the impact of previous period’s forecast variance is more persistence on the stock return’s volatility of both indices. The volatility persistence is measured by the sum of ARCH and GARCH parameters (a _{1} + β). The sum (a _{1} + β) is less than unity, in all cases, which implies the stationarity of the models. The larger value of the sum (a _{1} + β) shows that shocks to stock returns of both indices persist for a longer time period and its effects remain highly persistent in the following periods.
Consistent with Elyasiani et al. (1995) and Kasman et al. (2011) this finding implies that results regarding volatility effects on returns of both indices. The impact of exchange rate volatility on the stock returns is measured by the coefficient δ _{1}. The results show that parameter of exchange rate volatility δ _{1} is positive and statistically significant for both KMI30 and KSE100. This implies that, whenever exchange rate volatility increases, stock returns of both indices become more volatile in following periods. Pakistan is an importoriented country and always exposed to the risk of foreign exchange fluctuations due to the globalization of banking sector. Therefore, higher exchange rate volatility leads towards higher fluctuations in the stocks returns of both indices. Another possible reason for higher fluctuations in the stock returns, due to exchange rate volatility, is the change in Pakistani political setup in 2008 (from dictatorship to democracy), after which exchange rate increased rapidly Hence, overall, KMI30 and KSE100 stock return’s volatility is increased in response to exchange rate volatility. The impact of interest rate volatility on stock return’s volatility is measured by the coefficient δ _{2}. Consistent with Kasman et al. (2011) the parameter of interest rate volatility δ _{2} is positive and statistically significant only for KSE100. This implication shows that, in response to increased interest rate volatility, the stock return’s volatility of KSE100 becomes more volatile in the subsequent periods. Pakistani financial markets lack financial derivatives instruments that can prevent stock returns becoming more volatile in response to interest rate volatility. This result further support our previous result of conditional mean equation which shows that, for Islamic index (KMI30), interest rate is not a determining factor behind conditional volatilities of KMI30.
Conclusion
Some of the financial and academic experts around the globe still hold a question mark on the economic viability of the ethical investing. Prime arguments given by the opponents of ethical investing include restricted diversification, availability of smaller investment universe, and additional screening and monitoring cots are. Hence, ethical investing criteria may adversely impact the performance of an investment. On the other hand, advocates of socially responsible investment come up with an argument that a competitive advantage can be achieved with good corporate responsibility practices, which can offer firms a range of opportunities. Accordingly, there is scarcity in the empirical literature on ethical investing and somewhat inconsistent results are provided by the available empirical papers.
The prime objective of this study was to investigate the extent to which the conditional volatilities of both Shari’ah compliant stock index (KMI30) and conventional stock index (KSE100) in Pakistan are related to the conditional volatility of interest rate and exchange rate. We employed Generalized Autoregressive Conditional Heteroskedastic in the mean (GARCHM) model. This framework relaxes constancy assumption of classical linear regression (CLRM) model and allows exchange rate and interest rate volatility to evolve over time. The GARCHM framework also reveals results about riskreturn tradeoff in the context of both Islamic and conventional stock indices. The findings show positive and statistically significant effect of interest rate volatility on KSE100, whereas KMI30 remains unaffected by the same. The relationship of risk coefficient (γ), measured in conditional mean equation (GARCHM), and stocks returns is positive and statistically significant for both KMI30 and KSE100, as expected. This result is consistent with the theory of riskreturn tradeoff.
In addition, this study also aims at investigating performance ofKMI30 and KSE100 using popular riskadjusted performance measures. KMI30 is marginally underperforming KSE100 as indicated by our statistical results on risk and returns, measured by mean and standard deviation, respectively. KMI includes 30 Shari’ah compliant stocks, while, KSE100 includes 100 securities that represent large market capitalization. One possible reason of marginal underperformance of KMI30 might be because of its relative newness (since it was launched in 2008) and other reason might be because in less developed countries, size and returns are positively related. Therefore, Islamic investors are not substantially worseoff than conventional investors who seek to invest in unscreened stocks. Moreover, KSE100 has higher returns and higher beta (systematic risk) as shown by the results of riskadjusted returns for four performance measures. Opposite is true for KMI30that confirms the theory of finance where higher risk assumed by investors will yield higher returns and vise verse. Muslim investors might have lower returns in the short run; however, such investments yield some other rewards in the world hereafter. Shari’ah investors want maximize their investment returns but they also want peace of mind by aligning their investments with their religious beliefs. On the whole, this study finds no significant performance differences and movements of both indices. Both indices are behaving in a similar direction for short and long run as well.
The empirical findings of this study reveal important information& policy implications for individual &institutional investors, regulatory authorities, academic community, and particularly for those who wish to make alignment between their investments and religious ðical beliefs through ethically responsible investments. Expected or unexpected movements in exchange rate and interest rate must be analyzed closely, by the portfolio managers and other stakeholders, for developing risk management strategies. Further research must be initiated by examining impact of other macroeconomic factors, such as inflation and GDP, on the riskreturn characteristics of both KMI30 and KSE100.
Declarations
Acknowledgements
The authors are grateful to the officials of state bank of Pakistan (SBP) for helping in accessing the relevant data regarding Islamic and conventional stock indices. We are grateful to the staff of COMSATS institute of information technology for secretarial assistance.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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