CROSS CORRELATIONS BETWEEN MSCI EMERGING MARKETS INDICES AND US STOCK MARKET INDEX: EVIDENCE FROM MODWT

Abstract

MSCI Emerging Market Indices are developed for international investors to evaluate investment opportunities in developing countries and provide the investor with an opportunity for foresight. Due to the rapid globalization and contagion effects in financial markets, studies on MSCI Emerging Market Indices have attracted great interest in recent years. This study aims to investigate the long-memory characteristics of emerging market volatility and to show the existence of cross-correlations between Emerging Markets and the US stock market. For this purpose, Maximum Overlapping Discrete Wavelet Transform (MODWT), which is widely used in estimations in the field of finance, has been applied. MODWT, which can be used with all the features in the time series, is used in all scale dimensions. In addition, MODWT enables to produce asymptotically more efficient wavelet variance estimators. In the study, MSCI indices of seven emerging markets are used by considering the period between 2 May 2014 and 25 October 2018. The findings show that volatility in all emerging markets is stable and short-memory. There is also evidence of high and time-bound correlations between the US and Emerging Markets.

Keywords

• MSCI index
• Emerging Markets
• MODWT
• Cross Correlation
• Long Memory

MSCI GELİŞMEKTE OLAN PİYASALAR VE ABD PİYASA ENDEKSİ ARASINDAKİ ÇAPRAZ KORELASYONLARIN MODWT İLE İNCELENMESİ

Abstract

MSCI Gelişmekte Olan Piyasa Endeksleri, uluslararası yatırımcıların gelişmekte olan ülkelerdeki yatırım fırsatlarını değerlendirmeleri ve yatırımcıya öngörü fırsatı sunması için geliştirilmiştir. Finansal piyasalardaki hızlı küreselleşme ve bulaşma etkileri nedeniyle son yıllarda MSCI Gelişen Piyasa Endeksleri üzerine yapılan çalışmalar büyük ilgi görmektedir. Bu çalışma, yükselen piyasa oynaklığının uzun hafıza özelliklerini araştırmayı ve Gelişmekte Olan Piyasalar ile ABD hisse senedi piyasası arasında çapraz korelasyonların varlığını göstermeyi amaçlamaktadır. Bu amaçla finans alanında tahminlerde yaygın olarak kullanılan Maksimum Örtüşmeli Ayrık Dalgacık Dönüşümü (MODWT) uygulanmıştır. Zaman serisindeki tüm özellikler ile kullanılabilen MODWT, tüm ölçek boyutlarında kullanılmakta ve asimptotik olarak daha verimli dalgacık varyans tahmin edicilerinin üretilmesini sağlamaktadır. Çalışmada 2 Mayıs 2014 ile 25 Ekim 2018 arasındaki dönem dikkate alınarak yedi gelişmekte olan piyasanın MSCI endeksleri kullanılmıştır. Elde edilen bulgular tüm gelişmekte olan piyasalarda oynaklığın istikrarlı ve kısa hafızalı olduğunu göstermektedir. Ek olarak, ABD ve Gelişmekte Olan Piyasalar arasında yüksek ve zamana bağlı korelasyonun olduğu gözlemlenmektedir.

Keywords

• MSCI Endeksi
• Gelişen Piyasa Endeksleri
• MODWT
• Çapraz Korelasyon
• Uzun Hafıza

References

1. Abry, P., Gonçalvés, P., Flaundrin, P., (1995). Wavelet Spectrum Estimation and 1/f processes. Lecture Notes in Statistics, 103, 15-30.
2. Abry, P., Veitch, D., (1996). Wavelet Analysis of Long-Range-Dependent Traffic. IEEE Transactions on Information Theory 44, 2–15.
3. Aggarwal, R., Singh, J.K., Gupta, V.K., Rathore, S., Tiwari, M., Khare, A. (2011). Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold. International Journal of Computer Applications, 20(5), 0975 – 8887.
4. Alhayki, Z.J. (2014). The Dynamic Co-movements between Oil and Stock Market returns in: The Case of GCC Countries. Journal of Applied Finance & Banking, 4(3), 1-6.
5. Bekaert, G., Ehrmann, M., Fratzscher, M., Mehl, A. (2014). The Journal of Finance, 69(6), 2597-2649. doi.org/10.1111/jofi.12203
6. Bruzda, J. (2011). On Some Problems İn Discrete Wavelet Analysis Of Bivariate Spectra With An Application To Business Cycle Synchronization İn The Euro Zone. Economics Discussion Papers, 5.
7. Chakrabarty, A., De, A., Gunasekaran, A., Dubey, R. (2015). Investment Horizon Heterogenity and Wavelet: Overview and Further Research Directions. Physica A, 429,45-61. doi: 10.1016/j.physa.2014.10.097
8. Cohen, Lauren., Frazzını, Andrea. (2008). Economic Links and Predictable Returns. The Journal of Finance, 63(4), 1977-2011. doi:10.1111/j.1540-6261.2008.01379.x

9. Connor, j., Rossiter,R. (2005). Wavelet Transforms and Commodity Prices. Studies in Nonlinear. Dynamics & Econometrics, 9 (1), 1558-3708. doi: 10.2202/1558-3708.1170.
10. Dacjman, S., Festic, M., Kavkler, A. (2012). European Stock Market Comovement Dynamics During Some Major Financial Market Turnoils in the Period 1997 to 2010 – A Comparative DCC-GARCH and Wavelet Correlation Analysis. Applied Economics Letters, 19(13), 1249-1256. doi:10.1080/13504851.2011.619481
11. Dickey, D. A., Fuller, W. A. (1979). Distribution of the Estimators For Autoregressive Time Series with A Unit Root. Journal of the American Statistical Association, 74, 427–431. doi:10.1080/01621459.1979.10482531
12. Fernandez, V. (2005). The İnternational CAPM and A Wavelet-Based Decomposition of Value at Risk. Studies in Nonlinear Dynamics & Econometrics, 9 (4), 1-37. doi:10.3386/w12233
13. Gallegati, M. (2008). Wavelet analysis of stock returns and aggregate economic activity. Computational Statistics and Data Analysis, 52 (6), 3061–3074. doi:10.1016/j.csda.2007.07.019.
14. Gallegati, M., Gallegati, M. (2007). Wavelet Variance Analysis of Output in G-7 Countries. Studies in Nonlinear Dynamics & Econometrics, 11 (3), 1435-1435. doi:10.2202/1558-3708.1435
15. Gallegati, M., (2005). A Wavelet Analysis of MENA Stock Markets. Finance 0512027, University Library of Munich, Germany. https://EconPapers.repec.org/RePEc:wpa:wuwpfi:0512027
16. Gencay, R., Selcuk, F., Whitcher B. (2001). Scaling Properties of Foreign Exchange Volatility. Physica A, (1–2), 249–266. doi: 10.1016/S0378-4371(00)00456-8
17. Gencay, R., Selçuk, F., Witcher, B. (2001). Scaling Properties of Foreign Exchange Volatility. Physica A: Statistical Mechanics and its Application, 289, 249-266. doi:10.1016/S0378-4371(00)00456-8
18. Gencay, R., Selçuk, F., Witcher, B. (2005). Multiscale Systematic Risk. Journal of International Money and Finance, 24, 55–70
19. Gençay, R., Selçuk, F., Whitcher, B. (2001). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. United States: Academic Press .
20. Gençay, R., Selçuk, F., Whitcher, B. (2002). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. United States: Academic Press .
21. Goffe, W.L. (1994). Wavelets in Macroeconomics: An Introduction. Computational Techniques for Econometrics and Economic Analysis, 3, 137-149. doi: 10.1007/978-94-015-8372-5_8
22. Hamrita, M.E., Ben Abdallah, N.B., Ammou, S.A.B., (2009). The Multi-Scale Interaction Between Interest Rate, Exchange Rate and Stock Price. SRN Electronic Journal. doi:10.2139/ssrn.1490332
23. Hussin, T.M.T.İ.T., Saiti, B., Alshammri, A.A., Altarturi, B.H.M. (2016). Oil Price and Exchange Rates: A Wavelet Analysis for Organisation of Oil Exporting Countries Members. International Journal of Energy Economics and Policy, 6(3), 421-430.
24. Jammazi, R. (2012). Oil Shock Transmission to Stock Market Returns: Wavelet-Multivariate Markov Switching GARCH Approach. Energy, 37(1):430-454. doi: 10.1016/j.energy.2011.11.011
25. Jammazi, R. (2012). Oil Shock Transmission to Stock Market Returns: Wavelet-Multivariate Markov Switching GARCH Approach. Energy, Elsevier, 37(1), 430-454. doi: 10.1016/j.energy.2011.11.011
26. Jarque, C.M., Bera, A.K., (1980). Efficient Tests For Normality, Homoscedasticity and Serial İndependence of Regression Residuals. Economic Letters, 6(3), 255-259. doi:10.1016/0165-1765(80)90024-5
27. Jena, K.S., Tiwari, A., Roubaud, D. (2017). Comovements of Gold Futures Markets and The Spot Market: A Wavelet Analysis. Finance Research Letters. doi: 10.1016/j.frl.2017.05.006
28. Jensen, M.J., (1999). Using Wavelets to Obtain A Consistent Ordinary Least Squares Estimator of the Long-Memory Parameter. Journal of Forecasting 18, (1), 17–32. doi:10.1002/(SICI)1099-131X(199901)18:1<17::AID-FOR686>3.0.CO;2-M
29. Khalfaouia, R., Boutaharab, M., Boubakerb, H. (2015). Analyzing Volatility Spillovers and Hedging Between Oil and Stock Markets: Evidence From Wavelet Analysis. Energy Economics, 49, 540-549. doi: 10.1016/j.eneco.2015.03.023
30. Kim, S., In, F. (2003). The Relationship Between Financial Variables and Real Economic Activity: Evidence From Spectral and Wavelet Analyses. Studies in Nonlinear Dynamics & Econometrics, 7(4). doi:10.2202/1558-3708.1183
31. Kim, S., In, F. (2005). The Relationship Between Stock Returns and Inflation: New Evidence from Wavelet Analysis. Journal of Empirical Finance, 12(3), 435-444. doi: 10.1016/j.jempfin.2004.04.008
32. Kim, H.O., Lee, B. K., Lee, N. (2007). Wavelet-Based Audio Watermarking Techniques: Robustness and Fast Synchronization. Koutmoss, G.G. (2006). Modeling the dynamic interdependence of major European stock markets. Journal of Business Finance & Accounting, 23(7),975 – 988. doi: 10.1111/j.1468-5957.1996.tb01035.x
33. Kumar, A., Joshi, L.K., Pal, A.K., Shukla, A.K. (2011). MODWT Based Time Scale Decomposition Analysis of BSE and NSE Indexes Financial Time Series. International Journal of Mathematical Analysis, 5, 1343 – 1352.
34. Kumar, A., Pant, S., Joshi, L.K. (2016). Wavelet Variance, Covariance and Correlation Analysis of BSE and NSE Indexes Financial Time Series. International Journal of Mathematical, Engineering and Management Sciences, 1(1), 26-33.
35. Kumar, A.S., B Kamaiah, B. (2017). Returns and Volatılıty Spıllover between Asıan Equıty Markets: A Wavelet Approach. Economic Annals, 62(212), 63-84.
36. Lee, J., Kim, T.S., Lee, H.K. (2008). Risk-Return Relationship in High Frequency Data: Multiscale Analysis and Long Memory Effect. KAIST Business School Working Paper. doi:10.2139/ssrn.1137533
37. Lin, J., Ki, W-H., Edwards, T., Shamma, S. (1994). Analog VLSI implementations of auditory wavelet transforms using switched-capacitor circuits. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 41(9), 572 – 583. doi: 10.1109/81.317956
38. Macho, J.F. (2012). Wavelet Multiple Correlation and Cross-Correlation: A Multiscale Analysis of Eurozone Stock Markets. Physica A: Statistical Mechanics and its Applications, 391(4), 1097-1104. doi: 10.1016/j.physa.2011.11.002.
39. Madaleno, M., Pinho, C. (2012). International Stock Market Indices Comovements: A New Look. Internaional Journal of Finance and Economics,17(1), 89-102. doi:10.1002/ijfe.448
40. Martinez, J.S.P., Abadie, L.M. (2016). Analyzing Crude Oil Spot Price Dynamics Versus Long Term Future Prices: A Wavelet Analysis Approach. Energies, 9(12). doi: 10.3390/en9121089
41. Moshiri, S., Pakizeh, Kamran. , Dabirian, Manouchehr., (2010). Stock Returns and Inflation: A Wavelet Analysis in Tehran Stock Exchange. Quarterly Iranian Economic Research, 43.
42. Naccache, T. ( 2011). Oil price cycles and wavelets. Energy Economics, Elsevier, 33(2), 338-352.
43. Nikkinen, J., Pynnonen, S., Ranta, Mikko., Vähämaa, S., (2011). Cross-Dynamics of Exchange Rate Expectations: A Wavelet Analysis. International Journal of Finance & Economics, 16(3). doi: 10.1002/ijfe.423
44. Percival, D. B. and H. O. Mofjeld (1997). Analysis of subtidal coastal sea level fluctuations using wavelets. Journal of the American Statistical Association 92 (439), 868–880.
45. Percival, D.B. (1995). On Estimation of the Wavelet Variance. Biometrica, 82, 619-631.
46. Percival, D.B., Walden, A.T. (2000). Wavelet Methods for- Times Series Analysis. Cambridge: Cambridge University Press.
47. Ramsey, J.B., Lampart, Camille. (1998) Decomposıtıon of Economıc Relatıonshıps By Tımescale Usıng Wavelets. Macroeconomic Dynamics,2(1), 49-71.
48. R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
49. Reboredo, J. (2013). Is Gold A Safe Haven or A Hedge For The US Dollar? Implications For Risk Management. Journal of Banking & Finance, , 37(8), 2665-2676.
50. Rua, A., Nunes L.C. (2009). International Comovement of Stock Market Returns: A Wavelet Analysis. Journal of Empirical Finance, (16- 4), 632–639. doi:10.1016/j.jempfin.2009.02.002
51. Sharkasi, A., Ruskin, H.,Crane, M. (2005). Interrelationships Among İnternational Stock Market İndices: Europe, Asia and the Americas. International Journal of Theoretical and Applied Finance, 8 (5) 603–622.
52. Strauss, J., Zhou, G., Rapach, D.E., (2013). International Stock Return Predictability: What Is the Role of the United States? The Journal of Finance, 68(4). doi: 10.2139/ssrn.1508484
53. Whitcher, B. (2020). waveslim: Basic Wavelet Routines for One-,Two-, and Three-Dimensional Signal Processing. R package version