- Email: [email protected]
Stock price fluctuation and the business cycle in the BRICS countries: A nonparametric quantiles causality approach
Finance Research Letters xxx (xxxx) xxx–xxx
Contents lists available at ScienceDirect
Finance Research Letters journal homepage: www.elsevier.com/locate/frl
Stock price fluctuation and the business cycle in the BRICS countries: A nonparametric quantiles causality approach Shi Guangpinga, , Liu Xiaoxingb ⁎
a b
School of Finance, Henan University of Economics and Law, China School of Economics and Management, Southeast University, China
ARTICLE INFO
ABSTRACT
Keywords: Stock price fluctuations The business cycle Quantile causality Volatility
Using the newly developed nonparametric quantile causality method, we investigate the causal relationships in the mean and variance between stock price fluctuation and the business cycle for the BRICS countries. The empirical results reveal that the causality in the mean between stock price fluctuation and the business cycle is insignificant across all distributions, apart from Russia; however, the bidirectional causality in the variance covers virtually all quantiles, with some exceptions in the tails for all BRICS countries. Therefore, the investors and economic policy makers could consider the variance of stock price fluctuation and the business cycle and pay special attention to the tail quantiles to improve the efficiency of investment and policy.
JEL: G15 E32 C32
1. Introduction The nexus between stock market and economic growth is an important issue which has been discussing among investors, researchers, and policy makers for a long time. However, there has been fierce debate regarding whether the stock market contains predictive ability for the future economy or whether economic growth increases stock market development. One of the explanations, called “supply-leading”, emphasizes that stock prices are indicators for the well-being of the economy. Croux and Reusens (2013) and Tiwari et al. (2015), using the data from G-7 countries and India, respectively, support the supply leading view from the perspective of the frequency domain. Instead, the view called “demand-following” argues that the development of stock market is facilitated by economic growth. Enisan and Olufisayo (2009) gave evidence for the demand-following view using the data of seven sub-Sahara African countries. Moreover, bidirectional causality and no causality have also been found between stock returns and economic growth (Marques et al., 2013; Guo, 2015). More recently, some studies have begun to focus on more detailed issues, such as the relationship between stock price fluctuation and the business cycle; however, the conclusions have still been conflicting. For instance, Zhu and Zhu (2014) find that the business cycle can strongly predict European stock returns, whereas Paetz and Gupta (2016) demonstrate that, for South Africa, part of the volatility of production can be explained by stock price shocks. However, Choudhry et al. (2016) suggest that there is a bidirectional causal relationship between stock market volatility and the business cycle within four representative countries, namely the US, Canada, Japan, and the UK. Only a few papers have focused on the stock price fluctuation- the business cycle relationship for the BRICS countries, none of these studies have employed the quantile causality approach. Moreover, the earlier research has shown that the structure of dependency between stock market and economic growth is characterized by nonlinearity and dynamic (Croux and Reusens, 2013; Guo,
⁎
Corresponding author. E-mail address: [email protected] (G. Shi).
https://doi.org/10.1016/j.frl.2019.06.021 Received 22 January 2019; Received in revised form 5 June 2019; Accepted 29 June 2019 1544-6123/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Shi Guangping and LiuXiaoxing , Finance Research Letters, https://doi.org/10.1016/j.frl.2019.06.021
Finance Research Letters xxx (xxxx) xxx–xxx
G. Shi and X. Liu
2015). Compared with the existing literature, this paper has the following contributions. First, we use a nonparametric quantile causality method to test the nexus of stock price fluctuation and the business cycle for all BRICS countries. Second, the causal relation is tested not only in the mean but also in the variance, which can capture the relationships hidden in mean. Third, we use nonlinearity and structural breaks tests to support the nonlinear causality approach. Fourth, we discuss the linkage between stock price fluctuation and the business cycle for all BRICS countries, and we find that the results are similar. 2. Methodology and data 2.1. Nonparametric quantile causality testing We employ a new methodology, developed by Balcilar et al. (2016a,b), to investigate the nonlinear causality of stock price fluctuation and the business cycle. We denote stock market fluctuation as xt and the business cycle asyt. The null hypothesis that xt does not Granger cause yt atθ of the quantile concerning the lag-vector of {yt-1,…, t-p,xt-1, …, xt-p} implies
Q {yt yt 1 , …, yt
p,
xt 1, …, xt p}=Q {yt yt 1 , …, yt p }
(1)
where Qθ{yt|•} is the θ th quantile of yt, depending on t and 0 < θ < 1. Rejecting the null hypothesis implies that the lagged values of xt affect yt in the θ th quantile. In light of Balcilar et al. (2016a,b), we can test the existence of causality between stock price fluctuation and the business cycle not only in the mean but also in the variance. The causality testing across different quantiles can be implemented successively by the following steps. First, two choices regarding the lag order p and the bandwidth h should be solved before nonparametric quantile causality test. Based on a VAR model including stock price fluctuation and the business cycle, we use the Schwarz Information Criterion (SIC) to select the lag order. The bandwidth value is determined by the least squares crossvalidation. Second, the kernel type for K(•) and L(•) should be specified. The Gaussian-type is used as the kernel in our study. Third, empirical analysis are realized by employing the approach of nonparametric quantile causality (Nishiyama et al., 2011).1 2.2. Data description We focus on quarterly data from 1996:1 to 2016:4 for the BRICS countries. For each country, a national stock price index is selected,2 and stock price fluctuation (STOFLU) is extracted by subtracting the HP-filtered trend from the stock price index. The seasonally adjusted real GDP is chosen as a measurement of the economic activity for each country. We obtained the business cycle (BUSCYC) using the HP filters method on the real GDP. All data come from the Wind database, both stock price index and GDP for each country are expressed in the domestic currency prices. The summary statistics of the series imply that stock price fluctuation and the business cycle have “thick tail distribution” feature. Furthermore, according to the Jarque–Bera test, the null hypothesises that the two variables of each country are normal distribution for are rejected.3 This indicates that the causality is expected to test throughout the whole condition distribution, rather than to focus solely on the condition mean. 3. Empirical results 3.1. Linear causality The ADF unite root tests indicate that all series used in our paper are strong stationary.4 For comparison, we first carry on the linear Granger causality test for stock price fluctuation and the business cycle of each country. Table 1 shows that there is a unidirectional causality from stock price fluctuation to the business cycle for Brazil, Russia, India, China, and South Africa. However, the one-way Granger causality shows the influence of the business cycle on stock price fluctuation only in China. Our results are inconsistent with those of Marques et al. (2013) who indicated there is a bidirectional causality between stock market and economic growth and those of Guo (2015) who found no causality between stock returns and real economic growth in China for the period before the subprime crisis. Therefore, the suitability of predictability analysis based on linear VAR models would be questioned. In other words, the effectiveness of the results from the standard causality test should be test further. 3.2. Nonlinearity test and parameter stability test The BDS tests with respect to the residuals from the VAR model of each country are applied to detect the existence of nonlinearity, and the results about the p-values of the BDS test are shown in Table 2. From Table 2 we can see that the series we employed and their relationships are characteristic of nonlinearity, apart from the business cycle equation for South Africa. To assess the stability of standard Granger causality test, we apply the parameter stability testing developed by Andrews (1993) and Andrews and 1
The details of the empirical implementation are omitted here, see Balcilar et al. (2016 a, b). Brazil: IBOVESPA index; Russia: Russian index; India: SENSEX30; China: Shanghai stock exchange composite index; South Africa: TOP40. 3 To save space, the results about summary statistics and the Jarque-Bera test are not reported here. 4 Details are not reported here. 2
2
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Table 1 Linear Granger causality test. Country
STOFLU ≠ >BUSCYC Statistics
p-value
GDPGAP ≠ >BUSCYC Statistics
p-value
Brazil Russia India China South Africa
9.3356*** 5.0778*** 3.9380*** 1.2841 7.4433***
0.0002 0.0085 0.0061 0.2810 0.0011
2.0808 0.3386 0.2296 2.0780* 0.1815
0.1318 0.7138 0.9209 0.0788 0.8344
Notes: * and *** denote significance at the 10% and 1% levels, respectively. Table 2 BDS test.
Brazil BUSCYC Residual STOFLU Residual Russia BUSCYC Residual STOFLU Residual India BUSCYC Residual STOFLU Residual China BUSCYC Residual STOFLU Residual South Africa BUSCYC Residual STOFLU Residual
Dimension 2
3
4
5
6
1.7581* (0.0787) 2.8269*** (0.0047)
0.9867 (0.3238) 3.5307*** (0.0004)
1.4663 (0.1426) 3.6046*** (0.0003)
1.6610* (0.0967) 3.9198*** (0.0001)
2.0187** (0.0435) 4.2362*** (0.0000)
5.0388*** (0.0000) 3.3686*** (0.0008)
5.6376*** (0.0000) 3.5684*** (0.0004)
5.9856*** (0.0000) 4.1625*** (0.0000)
6.4095*** (0.0000) 4.7328*** (0.0000)
6.6466*** (0.0000) 4.9210*** (0.0000)
2.9295*** (0.0034) 1.5831 (0.1134)
2.3293** (0.0198) 2.9382*** (0.0033)
1.9321* (0.0533) 3.5285*** (0.0004)
1.9114* (0.0599) 3.6572*** (0.0003)
2.0387** (0.0415) 4.7904*** (0.0000)
1.3256 (0.1850) 4.8538*** (0.0000)
1.6266 (0.1038) 5.7755*** (0.0000)
1.8685* (0.0617) 6.0336*** (0.0000)
2.1886** (0.0286) 5.9545*** (0.0000)
1.9250* (0.0542) 5.7365*** (0.0000)
0.5217 (0.6019) 2.1186** (0.0341)
0.0880 (0.9299) 2.4082** (0.0160)
−0.1700 (0.8650) 2.2550** (0.0241)
−0.3171 (0.7512) 2.2574** (0.0240)
0.1777 (0.8590) 1.5981 (0.1100)
Notes: Residual series derived from the VAR(2), VAR(2), VAR(4), VAR(5), and VAR(7) comprising stock price fluctuation and the business cycle for Brazil, Russia, India, China, and South Africa, respectively. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. Table 3 Parameter stability testing. Test statistic Brazil Sup-F Mean-F Exp-F Lc Russia Sup-F Mean-F Exp-F Lc India Sup-F Mean-F Exp-F Lc China Sup-F Mean-F Exp-F Lc South Africa Sup-F Mean-F Exp-F Lc
BUSCYC equation Statistics
p-value
STOFLU equation Statistics
p-value
63.0170*** 18.8897*** 27.4353*** 0.8599
0.0000 0.0000 0.0000 0.33601
17.8603* 6.3027 5.1449 0.8652
0.0557 0.2269 0.1053 0.3562
33.6679*** 19.7161*** 13.2575*** 1.4627**
0.0000 0.0000 0.0000 0.0500
15.6076 5.7361 4.8147 0.4666
0.1181 0.3027 0.1363 0.6528
35.9114*** 8.0279* 15.1724*** 0.4913
0.0000 0.0866 0.0000 0.6344
23.9044*** 8.9035* 8.0541*** 0.4960
0.0057 0.0510 0.0087 0.6309
41.1723*** 14.6727*** 17.0124*** 0.7387
0.0000 0.0010 0.0000 0.4502
63.2282*** 11.4627*** 27.5434*** 0.5732
0.0000 0.0096 0.0000 0.5734
123.1268*** 10.4654** 57.4859*** 0.6888
0.0000 0.0187 0.0000 0.2566
39.9057*** 9.7271** 15.8780*** 0.4326
0.0000 0.0302 0.0000 0.6781
Notes: Parameter stability tests by Andrews (1993) with the null of parameter stability. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 3
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Ploberger (1994) for both stock price fluctuation equation and business cycle equation of each country. From the Lc statistics in Table 3 we can see that the long-run parameters of VAR models for all BRICS countries are stable. However, the Mean-LR, Sup-LR and Exp-LR statistics in Table 3 shows that the VAR models for each country have unstable short-run parameters. This suggests that there are structural changes. Considering the strong evidence of nonlinearity and structural mutations, we further performed with the nonparametric quantile causality testing. 3.3. Quantile causality test between stock price fluctuation and the business cycle Figs. 1 and 2 show the results of the quantiles causality test for stock price fluctuation and the business cycle and their volatilities. In Fig. 1 we observe that, the results about the causal relationship from stock price fluctuation to the business cycle are quite similar for each country, the no causality hypothesis is accepted across the various quantiles. This is the opposite of the results of the linear Granger test, but in line with the work of Guo (2015). As for the causality from stock price fluctuation to the volatility of the business cycle, the situation is quite similar again except for the case of China. For Brazil, Russia, India and South Africa, the null hypothesis of no causality is rejected below quantiles of 0.75, 0.70, 0.75 and 0.75, respectively. However, for China, the strength of the evidence for causality from stock price fluctuation to the volatility of business cycle presents a hump-shaped pattern from low to high quantile, i.e., the causality is not significant below 0.05 and above 0.80. These results support the supply leading view (see Croux and Reusens, 2013; Tiwari et al., 2015) from the perspective of causality in variance. Examining the quantile causality for the BRICS countries allows us to make a more robust conclusion. Contrary to the Linear Granger test, stock price fluctuation fails to predict business cycle over all quantiles across BRICS. However, we find that stock price fluctuation have a predictive information content for the volatility of business cycle, with some exception in the high quantiles. In fact, in situations of extreme fluctuation, the stock price may be driven mainly by the investor sentiment, which could create some extraordinary price bubbles. Thus, the distorted markets lose the ability to forecast the business cycle. The exception that stock price fluctuation is unable to predict the volatility of the business cycle in China when the former is extremely low may be due to the characteristics of the Chinese stock market, which is still government regulated. Our findings with respect to the causality from stock price fluctuation to the business cycle have important guiding significance for macro policy makers. Just paying attention to the causal relationship at the mean level can no longer fully use the stock market's forecasting effect on the economy. Economic policy makers should pay more attention to the ability of the stock market to predict the variance of the economic cycle in different quantiles. Specifically, given the predictive power of stock price fluctuation on the volatility of the business cycle in the lower and middle quantiles (below 0.75) for Brazil, Russia, India and South Africa, policy makers of the above four countries can make early responses based on stock market conditions when the volatility of business cycle is not very high. However, when the business cycle in Brazil, Russia, India, and South Africa have extremely high fluctuations, policy makers should focus not only on the stock market situation but also on other factors such as investor sentiment, herd behavior, etc. In China, policy makers should pay more attention to factors other than the stock market during the trough and peak period of the business cycle. In summary, the importance of the stock market as a reference factor for policy makers in formulating policies change with the magnitude of the volatility of the business cycle. Fig. 2 reveals that, there is no causality running from the business cycle to stock price fluctuation across entire quantiles in Brazil, India, China, and South Africa. However, as far as Russia, the one-way causality running from business cycle to stock price fluctuation at certain quantiles of the distribution of stock price fluctuation such as 0.05, 0.1 and 0.55–0.65. Moreover, similar patterns are found regarding the causality from the business cycle to the variance of stock price fluctuation for the case of Brazil, Russia, and South Africa, with the causality being significant in quantiles below 0.75, 0.75, and 0.8, respectively. Moving to the case of India and China, however, we find a hump-shaped pattern with the causality in variance being insignificant in the extreme low and high quantiles. Specifically, we find the statistics of causality test in variance for India and China reach the top for quantiles from 0.3 to 0.6, and weakens again after that. A feasible explanation for this is that the stock markets of China and India are the two largest in the BRICS countries and that weakens the influence of the business cycle when the stock price fluctuation is extremely low. Our findings illustrate how the evidence of causality change with quantiles, further showing the advantage of our quantile causality test (Balcilar et al., 2016a,b). As mentioned above, the business cycle and stock price fluctuation have close relations. Furthermore, stock market price fluctuation has an important impact on investment strategies. Therefore, our results regarding the causality from the business cycle to stock price fluctuation have important implications for the international portfolio investors wanting to invest in the BRICS asset markets. Most investors consider the impact of the economic cycle on the average level of stock price fluctuations when making decisions based on the economic cycle. According to our conclusion, investors can pay attention to the predictive ability of the economic cycle on the variance of stock price fluctuations to improve investment efficiency. For instance, considering the predictability of the business cycle on the volatility of stock price fluctuation in the lower and middle quantiles (below 0.75) for Brazil, Russia, and South Africa, investors who want to invest in stock markets of the above three countries can take strategy according to the business cycle in addition to the peak period of the economic cycle. This is in line with our intuition. During the peak period of the economic cycle, stock prices are risker due to the possibility of bubbles. Therefore, investors are not recommended to adopt a positive strategy. Moreover, according to results from China and India, strategies based on the business cycle could be taken when the stock price fluctuates are not extremely low and high. Strategies based on the business cycle are not advocated when the stock price fluctuates extremely low and high in China and India. This may caused by the government's macro-regulation. Therefore, investors who want to invest in the stock markets of China and India should pay more attention to macroeconomic policies in extreme situations of stock price fluctuation. 4
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Fig. 1. Quantiles causality in mean and variance for the H0: Stock price fluctuation does not Granger cause the business cycle.
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Fig. 2. Quantiles causality in mean and variance for the H0: The business cycle does not Granger cause stock price fluctuation.
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4. Conclusion This article proposes new insights into the nexus between stock price fluctuation and the business cycle in the BRICS economic bloc, employing nonparametric quantiles causality testing developed by Balcilar et al. (2016a,b). Preliminary analysis with respect to the nonlinearity and parameter stability reveal evidence of nonlinearity and structural breaks in the nexus between stock price fluctuation and business cycle, which provides a strong foundation for the use of nonparametric quantiles causality test. The results demonstrate that: (1) For the business cycle, the stock price fluctuation fails to present predictability over the entire distribution for all BRICS countries; (2) For stock price fluctuation, there is no evidence of predictability from the business cycle across all the quantiles, except for Russia; (3) The predictability from stock price fluctuation to the volatility of the business cycle or from the business cycle to the volatility of stock price fluctuation covers virtually all the quantiles, with some exceptions in the right tails for Brazil, Russia, and South Africa and in the two tails for China and India. Acknowledgments The authors are very grateful for the financial support from the Chinese National Social Science Major Project [grant number 18VSJ035] and the Chinese National Science Foundation [grant number 71673043]. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.frl.2019.06.021. References Andrews, D.W.K., Ploberger, W., 1994. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 1383–1414. Andrews, D.W.K., 1993. Tests for parameter instability and structural change with unknown change point. Econometrica 821–856. Balcilar, M., Bekiros, S., Gupta, R., 2016a. The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method. Empir. Econ. 1–11. Balcilar, M., Gupta, R., Pierdzioch, C., 2016b. Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resour. Policy 49, 74–80. Choudhry, T., Papadimitriou, F.I., Shabi, S., 2016. Stock market volatility and business cycle: evidence from linear and nonlinear causality tests. J. Bank. Financ. 66, 89–101. Croux, C., Reusens, P., 2013. Do stock prices contain predictive power for the future economic activity? A granger causality analysis in the frequency domain. J. Macroecon. 35, 93–103. Enisan, A.A., Olufisayo, A.O., 2009. Stock market development and economic growth: evidence from seven sub-Sahara African countries. J. Econ. Bus. 61 (2), 162–171. Guo, J., 2015. Causal relationship between stock returns and real economic growth in the pre-and post-crisis period: evidence from China. Appl. Econ. 47 (1), 12–31. Marques, L.M., Fuinhas, J.A., Marques, A.C., 2013. Does the stock market cause economic growth? Portuguese evidence of economic regime change. Econ. Model 32, 316–324. Nishiyama, Y., Hitomi, K., Kawasaki, Y., et al., 2011. . A consistent nonparametric test for nonlinear causality-Specification in time series regression. J. Econ. 165 (1), 112–127. Paetz, M., Gupta, R., 2016. Stock price dynamics and the business cycle in an estimated DSGE model for South Africa. J. Int. Financ. Mak. Inst. Money 44, 166–182. Tiwari, A.K., Mutascu, M.I., Albulescu, C.T., et al., 2015. Frequency domain causality analysis of stock market and economic activity in India. Int. Rev. Econ. Financ. 39, 224–238. Zhu, Y., Zhu, X., 2014. European business cycles and stock return predictability. Financ. Res. Lett. 11 (4), 446–453.
7
Contents lists available at ScienceDirect
Finance Research Letters journal homepage: www.elsevier.com/locate/frl
Stock price fluctuation and the business cycle in the BRICS countries: A nonparametric quantiles causality approach Shi Guangpinga, , Liu Xiaoxingb ⁎
a b
School of Finance, Henan University of Economics and Law, China School of Economics and Management, Southeast University, China
ARTICLE INFO
ABSTRACT
Keywords: Stock price fluctuations The business cycle Quantile causality Volatility
Using the newly developed nonparametric quantile causality method, we investigate the causal relationships in the mean and variance between stock price fluctuation and the business cycle for the BRICS countries. The empirical results reveal that the causality in the mean between stock price fluctuation and the business cycle is insignificant across all distributions, apart from Russia; however, the bidirectional causality in the variance covers virtually all quantiles, with some exceptions in the tails for all BRICS countries. Therefore, the investors and economic policy makers could consider the variance of stock price fluctuation and the business cycle and pay special attention to the tail quantiles to improve the efficiency of investment and policy.
JEL: G15 E32 C32
1. Introduction The nexus between stock market and economic growth is an important issue which has been discussing among investors, researchers, and policy makers for a long time. However, there has been fierce debate regarding whether the stock market contains predictive ability for the future economy or whether economic growth increases stock market development. One of the explanations, called “supply-leading”, emphasizes that stock prices are indicators for the well-being of the economy. Croux and Reusens (2013) and Tiwari et al. (2015), using the data from G-7 countries and India, respectively, support the supply leading view from the perspective of the frequency domain. Instead, the view called “demand-following” argues that the development of stock market is facilitated by economic growth. Enisan and Olufisayo (2009) gave evidence for the demand-following view using the data of seven sub-Sahara African countries. Moreover, bidirectional causality and no causality have also been found between stock returns and economic growth (Marques et al., 2013; Guo, 2015). More recently, some studies have begun to focus on more detailed issues, such as the relationship between stock price fluctuation and the business cycle; however, the conclusions have still been conflicting. For instance, Zhu and Zhu (2014) find that the business cycle can strongly predict European stock returns, whereas Paetz and Gupta (2016) demonstrate that, for South Africa, part of the volatility of production can be explained by stock price shocks. However, Choudhry et al. (2016) suggest that there is a bidirectional causal relationship between stock market volatility and the business cycle within four representative countries, namely the US, Canada, Japan, and the UK. Only a few papers have focused on the stock price fluctuation- the business cycle relationship for the BRICS countries, none of these studies have employed the quantile causality approach. Moreover, the earlier research has shown that the structure of dependency between stock market and economic growth is characterized by nonlinearity and dynamic (Croux and Reusens, 2013; Guo,
⁎
Corresponding author. E-mail address: [email protected] (G. Shi).
https://doi.org/10.1016/j.frl.2019.06.021 Received 22 January 2019; Received in revised form 5 June 2019; Accepted 29 June 2019 1544-6123/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Shi Guangping and LiuXiaoxing , Finance Research Letters, https://doi.org/10.1016/j.frl.2019.06.021
Finance Research Letters xxx (xxxx) xxx–xxx
G. Shi and X. Liu
2015). Compared with the existing literature, this paper has the following contributions. First, we use a nonparametric quantile causality method to test the nexus of stock price fluctuation and the business cycle for all BRICS countries. Second, the causal relation is tested not only in the mean but also in the variance, which can capture the relationships hidden in mean. Third, we use nonlinearity and structural breaks tests to support the nonlinear causality approach. Fourth, we discuss the linkage between stock price fluctuation and the business cycle for all BRICS countries, and we find that the results are similar. 2. Methodology and data 2.1. Nonparametric quantile causality testing We employ a new methodology, developed by Balcilar et al. (2016a,b), to investigate the nonlinear causality of stock price fluctuation and the business cycle. We denote stock market fluctuation as xt and the business cycle asyt. The null hypothesis that xt does not Granger cause yt atθ of the quantile concerning the lag-vector of {yt-1,…, t-p,xt-1, …, xt-p} implies
Q {yt yt 1 , …, yt
p,
xt 1, …, xt p}=Q {yt yt 1 , …, yt p }
(1)
where Qθ{yt|•} is the θ th quantile of yt, depending on t and 0 < θ < 1. Rejecting the null hypothesis implies that the lagged values of xt affect yt in the θ th quantile. In light of Balcilar et al. (2016a,b), we can test the existence of causality between stock price fluctuation and the business cycle not only in the mean but also in the variance. The causality testing across different quantiles can be implemented successively by the following steps. First, two choices regarding the lag order p and the bandwidth h should be solved before nonparametric quantile causality test. Based on a VAR model including stock price fluctuation and the business cycle, we use the Schwarz Information Criterion (SIC) to select the lag order. The bandwidth value is determined by the least squares crossvalidation. Second, the kernel type for K(•) and L(•) should be specified. The Gaussian-type is used as the kernel in our study. Third, empirical analysis are realized by employing the approach of nonparametric quantile causality (Nishiyama et al., 2011).1 2.2. Data description We focus on quarterly data from 1996:1 to 2016:4 for the BRICS countries. For each country, a national stock price index is selected,2 and stock price fluctuation (STOFLU) is extracted by subtracting the HP-filtered trend from the stock price index. The seasonally adjusted real GDP is chosen as a measurement of the economic activity for each country. We obtained the business cycle (BUSCYC) using the HP filters method on the real GDP. All data come from the Wind database, both stock price index and GDP for each country are expressed in the domestic currency prices. The summary statistics of the series imply that stock price fluctuation and the business cycle have “thick tail distribution” feature. Furthermore, according to the Jarque–Bera test, the null hypothesises that the two variables of each country are normal distribution for are rejected.3 This indicates that the causality is expected to test throughout the whole condition distribution, rather than to focus solely on the condition mean. 3. Empirical results 3.1. Linear causality The ADF unite root tests indicate that all series used in our paper are strong stationary.4 For comparison, we first carry on the linear Granger causality test for stock price fluctuation and the business cycle of each country. Table 1 shows that there is a unidirectional causality from stock price fluctuation to the business cycle for Brazil, Russia, India, China, and South Africa. However, the one-way Granger causality shows the influence of the business cycle on stock price fluctuation only in China. Our results are inconsistent with those of Marques et al. (2013) who indicated there is a bidirectional causality between stock market and economic growth and those of Guo (2015) who found no causality between stock returns and real economic growth in China for the period before the subprime crisis. Therefore, the suitability of predictability analysis based on linear VAR models would be questioned. In other words, the effectiveness of the results from the standard causality test should be test further. 3.2. Nonlinearity test and parameter stability test The BDS tests with respect to the residuals from the VAR model of each country are applied to detect the existence of nonlinearity, and the results about the p-values of the BDS test are shown in Table 2. From Table 2 we can see that the series we employed and their relationships are characteristic of nonlinearity, apart from the business cycle equation for South Africa. To assess the stability of standard Granger causality test, we apply the parameter stability testing developed by Andrews (1993) and Andrews and 1
The details of the empirical implementation are omitted here, see Balcilar et al. (2016 a, b). Brazil: IBOVESPA index; Russia: Russian index; India: SENSEX30; China: Shanghai stock exchange composite index; South Africa: TOP40. 3 To save space, the results about summary statistics and the Jarque-Bera test are not reported here. 4 Details are not reported here. 2
2
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Table 1 Linear Granger causality test. Country
STOFLU ≠ >BUSCYC Statistics
p-value
GDPGAP ≠ >BUSCYC Statistics
p-value
Brazil Russia India China South Africa
9.3356*** 5.0778*** 3.9380*** 1.2841 7.4433***
0.0002 0.0085 0.0061 0.2810 0.0011
2.0808 0.3386 0.2296 2.0780* 0.1815
0.1318 0.7138 0.9209 0.0788 0.8344
Notes: * and *** denote significance at the 10% and 1% levels, respectively. Table 2 BDS test.
Brazil BUSCYC Residual STOFLU Residual Russia BUSCYC Residual STOFLU Residual India BUSCYC Residual STOFLU Residual China BUSCYC Residual STOFLU Residual South Africa BUSCYC Residual STOFLU Residual
Dimension 2
3
4
5
6
1.7581* (0.0787) 2.8269*** (0.0047)
0.9867 (0.3238) 3.5307*** (0.0004)
1.4663 (0.1426) 3.6046*** (0.0003)
1.6610* (0.0967) 3.9198*** (0.0001)
2.0187** (0.0435) 4.2362*** (0.0000)
5.0388*** (0.0000) 3.3686*** (0.0008)
5.6376*** (0.0000) 3.5684*** (0.0004)
5.9856*** (0.0000) 4.1625*** (0.0000)
6.4095*** (0.0000) 4.7328*** (0.0000)
6.6466*** (0.0000) 4.9210*** (0.0000)
2.9295*** (0.0034) 1.5831 (0.1134)
2.3293** (0.0198) 2.9382*** (0.0033)
1.9321* (0.0533) 3.5285*** (0.0004)
1.9114* (0.0599) 3.6572*** (0.0003)
2.0387** (0.0415) 4.7904*** (0.0000)
1.3256 (0.1850) 4.8538*** (0.0000)
1.6266 (0.1038) 5.7755*** (0.0000)
1.8685* (0.0617) 6.0336*** (0.0000)
2.1886** (0.0286) 5.9545*** (0.0000)
1.9250* (0.0542) 5.7365*** (0.0000)
0.5217 (0.6019) 2.1186** (0.0341)
0.0880 (0.9299) 2.4082** (0.0160)
−0.1700 (0.8650) 2.2550** (0.0241)
−0.3171 (0.7512) 2.2574** (0.0240)
0.1777 (0.8590) 1.5981 (0.1100)
Notes: Residual series derived from the VAR(2), VAR(2), VAR(4), VAR(5), and VAR(7) comprising stock price fluctuation and the business cycle for Brazil, Russia, India, China, and South Africa, respectively. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. Table 3 Parameter stability testing. Test statistic Brazil Sup-F Mean-F Exp-F Lc Russia Sup-F Mean-F Exp-F Lc India Sup-F Mean-F Exp-F Lc China Sup-F Mean-F Exp-F Lc South Africa Sup-F Mean-F Exp-F Lc
BUSCYC equation Statistics
p-value
STOFLU equation Statistics
p-value
63.0170*** 18.8897*** 27.4353*** 0.8599
0.0000 0.0000 0.0000 0.33601
17.8603* 6.3027 5.1449 0.8652
0.0557 0.2269 0.1053 0.3562
33.6679*** 19.7161*** 13.2575*** 1.4627**
0.0000 0.0000 0.0000 0.0500
15.6076 5.7361 4.8147 0.4666
0.1181 0.3027 0.1363 0.6528
35.9114*** 8.0279* 15.1724*** 0.4913
0.0000 0.0866 0.0000 0.6344
23.9044*** 8.9035* 8.0541*** 0.4960
0.0057 0.0510 0.0087 0.6309
41.1723*** 14.6727*** 17.0124*** 0.7387
0.0000 0.0010 0.0000 0.4502
63.2282*** 11.4627*** 27.5434*** 0.5732
0.0000 0.0096 0.0000 0.5734
123.1268*** 10.4654** 57.4859*** 0.6888
0.0000 0.0187 0.0000 0.2566
39.9057*** 9.7271** 15.8780*** 0.4326
0.0000 0.0302 0.0000 0.6781
Notes: Parameter stability tests by Andrews (1993) with the null of parameter stability. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 3
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Ploberger (1994) for both stock price fluctuation equation and business cycle equation of each country. From the Lc statistics in Table 3 we can see that the long-run parameters of VAR models for all BRICS countries are stable. However, the Mean-LR, Sup-LR and Exp-LR statistics in Table 3 shows that the VAR models for each country have unstable short-run parameters. This suggests that there are structural changes. Considering the strong evidence of nonlinearity and structural mutations, we further performed with the nonparametric quantile causality testing. 3.3. Quantile causality test between stock price fluctuation and the business cycle Figs. 1 and 2 show the results of the quantiles causality test for stock price fluctuation and the business cycle and their volatilities. In Fig. 1 we observe that, the results about the causal relationship from stock price fluctuation to the business cycle are quite similar for each country, the no causality hypothesis is accepted across the various quantiles. This is the opposite of the results of the linear Granger test, but in line with the work of Guo (2015). As for the causality from stock price fluctuation to the volatility of the business cycle, the situation is quite similar again except for the case of China. For Brazil, Russia, India and South Africa, the null hypothesis of no causality is rejected below quantiles of 0.75, 0.70, 0.75 and 0.75, respectively. However, for China, the strength of the evidence for causality from stock price fluctuation to the volatility of business cycle presents a hump-shaped pattern from low to high quantile, i.e., the causality is not significant below 0.05 and above 0.80. These results support the supply leading view (see Croux and Reusens, 2013; Tiwari et al., 2015) from the perspective of causality in variance. Examining the quantile causality for the BRICS countries allows us to make a more robust conclusion. Contrary to the Linear Granger test, stock price fluctuation fails to predict business cycle over all quantiles across BRICS. However, we find that stock price fluctuation have a predictive information content for the volatility of business cycle, with some exception in the high quantiles. In fact, in situations of extreme fluctuation, the stock price may be driven mainly by the investor sentiment, which could create some extraordinary price bubbles. Thus, the distorted markets lose the ability to forecast the business cycle. The exception that stock price fluctuation is unable to predict the volatility of the business cycle in China when the former is extremely low may be due to the characteristics of the Chinese stock market, which is still government regulated. Our findings with respect to the causality from stock price fluctuation to the business cycle have important guiding significance for macro policy makers. Just paying attention to the causal relationship at the mean level can no longer fully use the stock market's forecasting effect on the economy. Economic policy makers should pay more attention to the ability of the stock market to predict the variance of the economic cycle in different quantiles. Specifically, given the predictive power of stock price fluctuation on the volatility of the business cycle in the lower and middle quantiles (below 0.75) for Brazil, Russia, India and South Africa, policy makers of the above four countries can make early responses based on stock market conditions when the volatility of business cycle is not very high. However, when the business cycle in Brazil, Russia, India, and South Africa have extremely high fluctuations, policy makers should focus not only on the stock market situation but also on other factors such as investor sentiment, herd behavior, etc. In China, policy makers should pay more attention to factors other than the stock market during the trough and peak period of the business cycle. In summary, the importance of the stock market as a reference factor for policy makers in formulating policies change with the magnitude of the volatility of the business cycle. Fig. 2 reveals that, there is no causality running from the business cycle to stock price fluctuation across entire quantiles in Brazil, India, China, and South Africa. However, as far as Russia, the one-way causality running from business cycle to stock price fluctuation at certain quantiles of the distribution of stock price fluctuation such as 0.05, 0.1 and 0.55–0.65. Moreover, similar patterns are found regarding the causality from the business cycle to the variance of stock price fluctuation for the case of Brazil, Russia, and South Africa, with the causality being significant in quantiles below 0.75, 0.75, and 0.8, respectively. Moving to the case of India and China, however, we find a hump-shaped pattern with the causality in variance being insignificant in the extreme low and high quantiles. Specifically, we find the statistics of causality test in variance for India and China reach the top for quantiles from 0.3 to 0.6, and weakens again after that. A feasible explanation for this is that the stock markets of China and India are the two largest in the BRICS countries and that weakens the influence of the business cycle when the stock price fluctuation is extremely low. Our findings illustrate how the evidence of causality change with quantiles, further showing the advantage of our quantile causality test (Balcilar et al., 2016a,b). As mentioned above, the business cycle and stock price fluctuation have close relations. Furthermore, stock market price fluctuation has an important impact on investment strategies. Therefore, our results regarding the causality from the business cycle to stock price fluctuation have important implications for the international portfolio investors wanting to invest in the BRICS asset markets. Most investors consider the impact of the economic cycle on the average level of stock price fluctuations when making decisions based on the economic cycle. According to our conclusion, investors can pay attention to the predictive ability of the economic cycle on the variance of stock price fluctuations to improve investment efficiency. For instance, considering the predictability of the business cycle on the volatility of stock price fluctuation in the lower and middle quantiles (below 0.75) for Brazil, Russia, and South Africa, investors who want to invest in stock markets of the above three countries can take strategy according to the business cycle in addition to the peak period of the economic cycle. This is in line with our intuition. During the peak period of the economic cycle, stock prices are risker due to the possibility of bubbles. Therefore, investors are not recommended to adopt a positive strategy. Moreover, according to results from China and India, strategies based on the business cycle could be taken when the stock price fluctuates are not extremely low and high. Strategies based on the business cycle are not advocated when the stock price fluctuates extremely low and high in China and India. This may caused by the government's macro-regulation. Therefore, investors who want to invest in the stock markets of China and India should pay more attention to macroeconomic policies in extreme situations of stock price fluctuation. 4
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Fig. 1. Quantiles causality in mean and variance for the H0: Stock price fluctuation does not Granger cause the business cycle.
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Fig. 2. Quantiles causality in mean and variance for the H0: The business cycle does not Granger cause stock price fluctuation.
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4. Conclusion This article proposes new insights into the nexus between stock price fluctuation and the business cycle in the BRICS economic bloc, employing nonparametric quantiles causality testing developed by Balcilar et al. (2016a,b). Preliminary analysis with respect to the nonlinearity and parameter stability reveal evidence of nonlinearity and structural breaks in the nexus between stock price fluctuation and business cycle, which provides a strong foundation for the use of nonparametric quantiles causality test. The results demonstrate that: (1) For the business cycle, the stock price fluctuation fails to present predictability over the entire distribution for all BRICS countries; (2) For stock price fluctuation, there is no evidence of predictability from the business cycle across all the quantiles, except for Russia; (3) The predictability from stock price fluctuation to the volatility of the business cycle or from the business cycle to the volatility of stock price fluctuation covers virtually all the quantiles, with some exceptions in the right tails for Brazil, Russia, and South Africa and in the two tails for China and India. Acknowledgments The authors are very grateful for the financial support from the Chinese National Social Science Major Project [grant number 18VSJ035] and the Chinese National Science Foundation [grant number 71673043]. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.frl.2019.06.021. References Andrews, D.W.K., Ploberger, W., 1994. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 1383–1414. Andrews, D.W.K., 1993. Tests for parameter instability and structural change with unknown change point. Econometrica 821–856. Balcilar, M., Bekiros, S., Gupta, R., 2016a. The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method. Empir. Econ. 1–11. Balcilar, M., Gupta, R., Pierdzioch, C., 2016b. Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resour. Policy 49, 74–80. Choudhry, T., Papadimitriou, F.I., Shabi, S., 2016. Stock market volatility and business cycle: evidence from linear and nonlinear causality tests. J. Bank. Financ. 66, 89–101. Croux, C., Reusens, P., 2013. Do stock prices contain predictive power for the future economic activity? A granger causality analysis in the frequency domain. J. Macroecon. 35, 93–103. Enisan, A.A., Olufisayo, A.O., 2009. Stock market development and economic growth: evidence from seven sub-Sahara African countries. J. Econ. Bus. 61 (2), 162–171. Guo, J., 2015. Causal relationship between stock returns and real economic growth in the pre-and post-crisis period: evidence from China. Appl. Econ. 47 (1), 12–31. Marques, L.M., Fuinhas, J.A., Marques, A.C., 2013. Does the stock market cause economic growth? Portuguese evidence of economic regime change. Econ. Model 32, 316–324. Nishiyama, Y., Hitomi, K., Kawasaki, Y., et al., 2011. . A consistent nonparametric test for nonlinear causality-Specification in time series regression. J. Econ. 165 (1), 112–127. Paetz, M., Gupta, R., 2016. Stock price dynamics and the business cycle in an estimated DSGE model for South Africa. J. Int. Financ. Mak. Inst. Money 44, 166–182. Tiwari, A.K., Mutascu, M.I., Albulescu, C.T., et al., 2015. Frequency domain causality analysis of stock market and economic activity in India. Int. Rev. Econ. Financ. 39, 224–238. Zhu, Y., Zhu, X., 2014. European business cycles and stock return predictability. Financ. Res. Lett. 11 (4), 446–453.
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