Hisse Senedi Piyasası Oynaklığı Konjonktür Dalgalanmalarını Nasıl Etkiler? Türkiye’den Asimetrik Kanıtlar

Yıl 2023, , 467 - 481, 30.09.2023

Veysel Karagöl

https://doi.org/10.30784/epfad.1303396

Öz

Bu çalışmanın amacı, 1998-2022 çeyreklik dönemi için, Türkiye’de hisse senedi piyasası oynaklığının konjonktür dalgalanmalarını nasıl etkilediğini araştırmaktır. Hisse senedi piyasası oynaklığını ve konjonktür dalgalanmalarını temsilen, sırasıyla, Borsa İstanbul 100 endeksi getiri serisi (RBIST) ve trendden arındırılmış reel Gayri Safi Yurtiçi Hasıla (BC) değişkenleri kullanılmıştır. Bu oynaklığın, konjonktür dalgalanmalarının genişleme ve daralma aşamalarındaki etkilerini ayrıştırabilmek adına asimetrik yöntemlerden yararlanılmıştır. İlk olarak Markov rejim değişim GARCH (MS-GARCH) yöntemiyle konjonktür dalgalanmaları, genişleme ve daralma rejimleri altında incelenmiştir. MS-GARCH yönteminin bulgularına göre, RBIST, BC’yi her iki aşamada da negatif etkilemektedir. Ancak 2000’li yılların başında yaşanan Bankacılık Krizi, Küresel Finansal Kriz, 2016 ve 2018 Döviz krizleri ile son yaşanan Covid-19 Pandemisi gibi daralma aşamalarında söz konusu etki nispeten daha büyüktür. İkinci olarak ise bulguları daha güçlü kılmak adına, doğrusal olmayan ARDL (NARDL) yöntemi uygulanmıştır. NARDL yönteminin bulguları, kısa dönemde, MS-GARCH yönteminden elde edilen bulguları desteklemektedir. Kısa dönemde RBIST’in pozitif ve negatif bileşenlerinin, BC üzerindeki etkisi negatif, ancak uzun dönemde pozitif ve simetriktir. Genel olarak çalışmanın bulguları, politika yapıcıların, hisse senedi piyasasındaki oynaklık ile konjonktür dalgalanmaları arasındaki ilişkinin doğrusal olmayan özelliklerini dikkate alması gerektiğini göstermektedir.

Anahtar Kelimeler

Hisse Senedi Piyasası Oynaklığı, Konjonktür Dalgalanmaları, MS-GARCH, NARDL, Türkiye

How Does Stock Market Volatility Affect Business Cycles? Asymmetric Evidence from Türkiye

Öz

This study aims to investigate how stock market volatility affects business cycles in Turkey for the quarterly period 1998-2022. Borsa Istanbul 100 index return series (RBIST) and detrended real Gross Domestic Product (BC) are employed to proxy stock market volatility and business cycles, respectively. Asymmetric methods are used to decompose the effects of this volatility in the expansion and contraction phases of business cycles. First, business cycles are examined under the expansion and contraction regimes by the Markov regime-switching GARCH (MS-GARCH) method. According to the findings of the MS-GARCH method, RBIST negatively affects BC in both phases. However, this effect is relatively greater during contraction phases such as the Banking Crisis in the early 2000s, the Global Financial Crisis, the 2016 and 2018 Currency Crises, and the recent Covid-19 Pandemic. Second, the non-linear ARDL (NARDL) method is applied to robust the findings. The findings of the NARDL method in the short run corroborate the findings obtained from the MS-GARCH method. The positive and negative components of RBIST have a negative effect on BC in the short run, but it is positive and symmetric in the long run. Overall, the findings of the study suggest that policymakers should consider the non-linear characteristics of the relationship between stock market volatility and business cycles.

Anahtar Kelimeler

Stock Market Volatility, Business Cycles, MS-GARCH, NARDL, Türkiye

Kaynakça

• Adam, K. and Merkel, S. (2019). Stock price cycles and business cycles (SSRN Working Paper No. 3455237). http://dx.doi.org/10.2139/ssrn.3455237
• Al-Rjoub, S.A. (2009). Business cycles, financial crises, and stock volatility in Jordan stock exchange (SSRN Working Paper No. 1461819). http://dx.doi.org/10.2139/ssrn.1461819
• Altaf, N. (2021). Stock market volatility and business cycle: Exploring cross-country spillovers. DLSU Business and Economics Review, 30(2), 43-50. Retrieved from https://www.dlsu.edu.ph/
• Ang, A. and Timmermann, A. (2011). Regime changes and financial markets (Netspar Discussion Paper No. 068). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1919497
• Bauwens, L., Preminger, A. and Rombouts, J.V. (2010). Theory and inference for a Markov switching GARCH model. The Econometrics Journal, 13(2), 218-244. https://doi.org/10.1111/j.1368-423X.2009.00307.x
• Beck, T. and Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking and Finance, 28(3), 423-442. https://doi.org/10.1016/S0378-4266(02)00408-9
• Bernanke, B. (1983). Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics, 98(1), 85-106. https://doi.org/10.2307/1885568
• Candelon, B. and Metiu, N. (2011), Linkages between stock market fluctuations and business cycles in Asia. In Y.-W. Cheung, V. Kakkar and G. Ma (Eds.), The evolving role of Asia in global finance (pp. 23-51). Bingley: Emerald Group Publishing Limited.
• Caporale, G.M., Howells, P.G. and Soliman, A.M. (2004). Stock market development and economic growth: The causal linkage. Journal of Economic Development, 29(1), 33-50. Retrieved from https://www.dbpia.co.kr/
• Casarin, R. and Trecroci, C. (2006). Business cycle and stock market volatility: A particle filter approach (Centre de Recherches en Mathématiques de la Decision Working Paper No 34219). Retrieved from https://iris.unive.it/bitstream/10278/34219/1/BusCycle07.pdf
• Chauvet, M. (1999). Stock market fluctuations and the business cycle. Journal of Economic and Social Measurement, 25(3-4), 235-257. https://doi.org/10.3233/JEM-1999-0166
• Choudhry, T., Papadimitriou, F.I. and Shabi, S. (2016). Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests. Journal of Banking and Finance, 66, 89-101. https://doi.org/10.1016/j.jbankfin.2016.02.005
• Diebold, F.X. and Yilmaz, K. (2010). Macroeconomic volatility and stock market volatility, world‐wide. In T.Bollerslev, J. Russell and M. Watson (Eds.), Volatility and time series econometrics: Essays in honor of Robert Engle (pp. 97-116). England: Oxford University Press.
• Döpke, J. and Pierdzioch, C. (2001). Brokers and business cycles: Does financial market volatility cause real fluctuations? Credit and Capital Markets-Kredit und Kapital, 34(3), 327-355. Retrieved from https://elibrary.duncker-humblot.com/
• Erdoğan, L., Ceylan, R. and Abdul-Rahman, M. (2022). The impact of domestic and global risk factors on Turkish stock market: Evidence from the NARDL approach. Emerging Markets Finance and Trade, 58(7), 1961-1974. https://doi.org/10.1080/1540496X.2021.1949282
• Federer, J. (1993). The impact of uncertainty on aggregate investment spending. Journal of Money, Credit and Banking, 25(1), 30-48. https://doi.org/10.2307/2077818
• Fornari, F. and Mele, A. (2013). Financial volatility and economic activity. Journal of Financial Management, Markets and Institutions, 1(2), 155-198. doi:10.12831/75569
• Gómez‐Cram, R. (2022). Late to recessions: Stocks and the business cycle. The Journal of Finance, 77(2), 923-966. https://doi.org/10.1111/jofi.13100
• Greenwald, B.C. and Stiglitz, J.E. (1993). Financial market imperfections and business cycles. The Quarterly Journal of Economics, 108(1), 77-114. https://doi.org/10.2307/2118496
• Hamilton, J.D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 57(2) 357-384. https://doi.org/10.2307/1912559
• Hamilton, J.D. and Lin, G. (1996). Stock market volatility and the business cycle. Journal of Applied Econometrics, 11(5), 573-593. https://doi.org/10.1002/(SICI)1099-1255(199609)11:5<573::AID-JAE413>3.0.CO;2-T
• Hodrick, R.J. and Prescott, E.C. (1997). Postwar US business cycles: An empirical investigation. Journal of Money, Credit, and Banking, 29(1), 1-16. https://doi.org/10.2307/2953682
• Kim, Y. and Nelson, C.R. (2013). Pricing stock market volatility: Does it matter whether the volatility is related to the business cycle? Journal of Financial Econometrics, 12(2), 307-328. https://doi.org/10.1093/jjfinec/nbt014
• Lee, J. and Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. The Review of Economics and Statistics, 85(4), 1082-1089. https://doi.org/10.1162/003465303772815961
• Levine, R. and Zervos, S.J. (1993). What we have learned about policy and growth from cross-country regressions? American Economic Review, 83(2), 426-430. Retrieved from https://www.jstor.org/
• López-Salido, D., Stein, J.C. and Zakrajšek, E. (2017). Credit-market sentiment and the business cycle. The Quarterly Journal of Economics, 132(3), 1373-1426. https://doi.org/10.1093/qje/qjx014
• Mirman, L.J. (1971). Uncertainty and optimal consumption decisions. Econometrica: Journal of the Econometric Society, 39(1), 179-185. https://doi.org/10.2307/1909149
• Pesaran, M.H., Shin, Y. and Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
• Raunig, B. and Scharler, J. (2010). Stock market volatility and the business cycle. Monetary Policy and the Economy, 2(10), 54-63. Retrieved from https://www.oenb.at/
• Schwert, G.W. (1989). Business cycles, financial crises, and stock volatility. In B.T. McCallum (Ed.), Carnegie-Rochester conference series on public policy (83-125). North-Holland: Elsevier.
• Shin, Y., Yu, B. and Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R.C. Sickles, and W.C. Horrace, (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281-314). New York: Springer.
• Vu, N.T. (2015). Stock market volatility and international business cycle dynamics: Evidence from OECD economies. Journal of International Money and Finance, 50, 1-15. https://doi.org/10.1016/j.jimonfin.2014.08.003