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Journals / Trakya Üniversitesi Sosyal Bilimler Dergisi / 2025 / Cilt: 27 Sayı: IERFM 2025 Özel Sayı Özel Sayı

FINANCIAL MARKET AND ASYMMETRIC STRUCTURE: AN EMPIRICAL APPLICATION ON TURKISH STOCK MARKET

Finansal Piyasalar ve Asimetrik Yapı: Türk Hisse Senedi Piyasası Üzerine Ampirik Bir Uygulama

Emre Kılıç(NİŞANTAŞI ÜNİVERSİTESİ)0000-0003-2900-5123
Şevket Pazarcı(NİŞANTAŞI ÜNİVERSİTESİ)0000-0002-3675-909X
Elif Hilal Nazlıoğlu(PAMUKKALE ÜNİVERSİTESİ)0000-0002-4425-7479
Asim Kar(PAMUKKALE ÜNİVERSİTESİ)0000-0001-5763-1434
Pages
123–148
Publication Date
2025-03-14
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings make it possible for researchers who will forecast the BIST100 to make inferences about the future movements of the index by looking at macroeconomic variables.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, the relationship between Borsa Istanbul benchmark index (BIST100) and selected macroeconomic variables for the period 2014:01-2024:01 is analysed in non-linear form.
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Key Findings & Data:In the model, BIST100 is taken as the dependent variable and deposit interest rate (INT), industrial production index (IPI), M2 money supply (M2) and volatility index (VIX) are considered as explanatory variables.
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Conclusion & Impact:These findings make it possible for researchers who will forecast the BIST100 to make inferences about the future movements of the index by looking at macroeconomic variables.
🔬Design:Modeling & Simulation
📍Scope:2014 Verileri
⚙️Variables:Hisse senedi piyasası · Makroekonomik göstergeler · Asimetrik ilişki

Abstract

In this study, the relationship between Borsa Istanbul benchmark index (BIST100) and selected macroeconomic variables for the period 2014:01-2024:01 is analysed in non-linear form. In the model, BIST100 is taken as the dependent variable and deposit interest rate (INT), industrial production index (IPI), M2 money supply (M2) and volatility index (VIX) are considered as explanatory variables. The relationship between BIST100 and the explanatory variables is analysed with the non-linear ARDL (NARDL) approach, which allows the asymmetric structure to be taken into account. According to the empirical findings, there is a long-run cointegration relationship between BIST100 index and explanatory variables (INT, IPI, M2 and VIX). When the coefficient results are analysed, increases in M2 positively affect the BIST100 index. Increases in INT lead to a decrease in BIST100 index and this effect is statistically significant. Positive shocks in IPI, which shows an asymmetric effect, have a statistically significant effect on BIST100, while negative shocks have a downward effect on BIST100 but are statistically insignificant. The effect of negative shocks in VIX, another asymmetric variable, on BIST100 is statistically insignificant. Positive shocks, on the other hand, have a downward effect on BIST100 as expected. These findings make it possible for researchers who will forecast the BIST100 to make inferences about the future movements of the index by looking at macroeconomic variables.

Özet

Bu çalışmada 2014:01-2024:01 dönemi için Borsa İstanbul gösterge endeksi (BIST100) ile seçili makroekonomik arasındaki ilişki doğrusal olmayan formda incelenmektedir. Modelde bağımlı değişken olarak BIST100, açıklayıcı değişkenler olarak da mevduat faiz oranı (INT), sanayi üretim endeksi (IPI), M2 para arzı (M2) ve oynaklık endeksi (VIX) dikkate alınmaktadır. BIST100 ve açıklayıcı değişkenler arasındaki ilişki, asimetrik yapının dikkate alınmasına olanak sağlayan doğrusal olmayan ARDL (NARDL) yaklaşımı ile analiz edilmektedir. Elde edilen ampirik bulgulara göre BIST100 endeksi ile açıklayıcı değişkenler (INT, IPI, M2 ve VIX) arasında uzun dönemde eşbütünleşme ilişkisi bulunmaktadır. Katsayı sonuçları incelendiğinde M2’de meydana gelen artışlar BIST100 endeksini pozitif etkilemektedir. INT’de meydana gelen artışlar ise BIST100 endeksinde azalışa yol açmakta ve bu etki istatistiksel olarak anlamlı olmaktadır. Asimetrik etki gösteren IPI’da meydana gelen pozitif şoklar BIST100 üzerinde istatistiksel olarak anlamlı bir etkiye sahipken negatif şoklar ise BIST100’ü düşürücü etki doğurmakla birlikte istatistiksel olarak anlamsız bulunmaktadır. Asimetrik etki gösteren bir başka değişken olan VIX’deki negatif şokların BIST100 üzerindeki etkisi istatistiksel olarak anlamsız bulunmaktadır. Pozitif şoklar ise beklentiler dahilinde BIST100’ü düşürücü etkide bulunmaktadır. Bu bulgular, BIST100’ü tahmin edecek araştırmacılar için makroekonomik değişkenlere bakarak, endeksin ileri dönemdeki hareketleri hakkında çıkarsamalar yapmasını mümkün kılmaktadır.

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Cite This Article

Kılıç, E., Pazarcı, Ş., Nazlıoğlu, E. H., Kar, A. (2025). FINANCIAL MARKET AND ASYMMETRIC STRUCTURE: AN EMPIRICAL APPLICATION ON TURKISH STOCK MARKET. Trakya Üniversitesi Sosyal Bilimler Dergisi, Cilt: 27 Sayı: IERFM 2025 Özel Sayı Özel Sayı, 123–148. https://doi.org/10.26468/trakyasobed.1515386

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References (49)

  1. Akdag S. (2019). Effect of VIX fear index on financial indicators: Turkey case. Hitit University Journal of Social Sciences Institute, 12(1), 235-256. https://doi.org/10.17218/hititsosbil.522619
  2. Alper, D., & Kara, E. (2017). Macroeconomic Factors That Affecting Stock Returns in Borsa Istanbul: A Research on Bist Industrial Index. Suleyman Demirel University the Journal of Faculty of Economics and Administrative Sciences, 22(3), 713-730.
  3. Anoruo, E. (2011). Testing for linear and nonlinear causality between crude oil price changes and stock market returns. International Journal of Economic Sciences and Applied Research, 4(3), 75-92. https://hdl.handle.net/10419/66595
  4. Asravor, R. K., & Fonu, P. D. D. (2021). Dynamic relation between macroeconomic variable, stock market returns and stock market development in Ghana. International Journal of Finance & Economics, 26(2), 2637-2646. https://doi.org/10.1002/ijfe.1925
  5. Bagchi, D. (2012). Cross‐sectional analysis of emerging market volatility index (India VIX) with portfolio returns. International Journal of Emerging Markets. https://doi.org/10.1108/17468801211264306
  6. Basher, S. A., & Sadorsky, P. (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics. 54, 235-247. https://doi.org/10.1016/j.eneco.2015.11.022
  7. Bayrakdaroglu, A., & Kaya, B.T. (2021). Testing The Relationship Between the Stock Market Index and Volatility Index in BRICS-T Countries by Panel Data Analysis. Electronic Journal of Social Sciences, 20(77), 313-328. https://doi.org/10.17755/esosder.711955
  8. Bhuiyan, E. M., & Chowdhury, M. (2020). Macroeconomic variables and stock market indices: Asymmetric dynamics in the US and Canada. The Quarterly Review of Economics and Finance, 77, 62-74. https://doi.org/10.1016/j.qref.2019.10.005
  9. Bildirici, M. E., Salman, M., & Ersin, Ö. Ö. (2022). Nonlinear contagion and causality nexus between oil, gold, VIX investor sentiment, exchange rate and stock market returns: The MS-GARCH copula causality method. Mathematics, 10(21), 4035. https://doi.org/10.3390/math10214035
  10. Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197-235. https://doi.org/10.1080/07474939608800353
  11. Caglar, A.E., Yavuz, E., Mert, M., and Kilic, E. (2022). The ecological footprint facing asymmetric natural resources challenges: evidence from the USA. Environmental Science and Pollution Research, 1-14. https://doi.org/10.1007/s11356-021-16406-9
  12. Chandra, A., & Thenmozhi, M. (2015). On Asymmetric Relationship of India Volatility Index (India VIX) With Stock Market Return and Risk Management. Decision, 42, 33-55. https://doi.org/10.1007/s40622-014-0070-0
  13. 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(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
  14. Duvar, N.C., & Eygu, H. (2022). Analysis of The Relationship Between the Stock Market Index and Selected Variables in Turkey. Academy Social Sciences Journal, 9(25), 102-122. DOİ: 10.34189/asbd.9.25.007
  15. Fareed, Z., Meo, M. S., Zulfiqar, B., Shahzad, F., & Wang, N. (2018). Nexus of tourism, terrorism, and economic growth in Thailand: new evidence from asymmetric ARDL cointegration approach. Asia Pacific Journal of Tourism Research, 23(12), 1129-1141. https://doi.org/10.1080/10941665.2018.1528289
  16. Granger, C. W., & Yoon, G. (2002). Hidden cointegration. U of California, Economics Working Paper, (2002-02). http://dx.doi.org/10.2139/ssrn.313831
  17. Gulhan, U. (2020). Relationship Between Gold Prices and VIX Index, BIST 100 Index, Exchange Rate and Oil Price: An Econometric Analysis. Gümüşhane University Journal of Social Sciences, 11(2), 576-591. https://doi.org/10.36362/gumus.710836
  18. Gungor, M. (2021). The Interaction of the Exchange Rate, the VIX Fear Index and Foreign Portfolio Investments. European Journal of Science and Technology Special Issue, (32), 1034-1042. https://doi.org/10.31590/ejosat.1044711
  19. Hatipoglu, M., & Tekin, B. (2017). The effects of VIX index, exchange rate & oil prices on the BIST 100 index: A quantile regression approach. Ordu University Journal of Social Science Research, 7(3) 627-634. https://papers.ssrn.com/Sol3/papers.cfm?abstract_id=2946398
  20. Imegi, J.C. (2014). Impact of Financial Liberalization on Stock Market Volatility in Nigeria. Journal of Business and Retail Management Research, 8(2), 80-87. http://dx.doi.org/10.21511/imfi.14(3-1).2017.13
  21. Iskenderoglu, O., & Akdag, S. (2020). Comparison of the effect of VIX fear index on stock exchange indices of developed and developing countries: The G20 case. The South East European Journal of Economics and Business, 15(1), 105-121. doi: 10.2478/jeb-2020-0009
  22. Jarque, C. M., & Bera, A. K. (1987). A test for normality of observations and regression residuals. International Statistical Review, 163-172. https://doi.org/10.2307/1403192
  23. Kazak, H. (2023). The Relationship Between Islamic and Conventional Equity Market Indices under the Fear Index Effect: The Case of Türkiye. JOEEP: Journal of Emerging Economies and Policy, 8(2), 196-208.
  24. Koy, A., Gungor, M. Y., & Simsek, O. (2022). Analysis of Intraday Non-Linear Asymmetrical Relationship in US Stock Exchanges with Momentum Threshold Models. Journal of Finance Letters, (117), 63-76. https://doi.org/10.33203/mfy.1038136
  25. Munyas, T. (2022). An Empirical Analysis of The Volatility Index (VIX) And Stock Markets in Developing Countries. Istanbul Commerce University Journal of Social Sciences, 21(43), 1-19. https://doi.org/10.46928/iticusbe.796019
  26. Munyas, T., & Bektur, C. (2021). Evaluation of the Relationship Between Volatility Index (VIX) and Credit Default Swap (CDS), Dollar Rate, EURO Rate, BIST 100 and Gold: The Case of Turkey. Journal of TESAM Academy, 8(2), 555-571. https://doi.org/10.30626/tesamakademi.959051
  27. Nazlioglu, E.H. (2024). The Relationships between the Turkish Stock Market and Macroeconomic Variables. Journal of Research in Economics, Politics & Finance, 9(1), 140-158. https://doi.org/10.30784/epfad.1424089
  28. Neffelli, M., & Resta, M. R. (2018). Is VIX still the investor fear gauge? Evidence for the US and BRIC markets. Evidence for the US and BRIC Markets (March 23, 2018). https://doi.org/10.48550/arXiv.1806.07556
  29. Pazarci, S., Kar, A., Kilic, E., & Umut, A. (2022). Empirical Analysis of the Relationship of Stock Market, Exchange Rate, CDS Spreads and VIX Index in Turkey. Afyon Kocatepe University Journal of Social Science, 24(3), 1090-1103. https://doi.org/10.32709/akusosbil.1084718
  30. Pesaran, M. H., Shin, Y., & 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
  31. Prasad, A., Bakhshi, P., & Seetharaman, A. (2022). The impact of the US macroeconomic variables on the CBOE VIX Index. Journal of Risk and Financial Management, 15(3), 126. https://doi.org/10.3390/jrfm15030126
  32. Ruan, L. (2018). Research on sustainable development of the stock market based on VIX index. Sustainability, 10(11), 4113. https://doi.org/10.3390/su10114113
  33. Sadeghzadeh, K. (2018). The Stock Market's Sensitivity to Psychological Factors: Relationship Between Volatility Index (VIX), Consumer Confidence Index (TGE) And Bist 100 Index. Cumhuriyet University Journal of Economics and Administrative Sciences, 19(2), 238-253.
  34. Saritas, H., & Nazlioglu, E. H. (2019). Fear Index, Stock Market and Exchange Rates Nexus: An Empirical Analysis for Turkey. Academic Review of Economics and Administrative Sciences, 12(4), 542-551. https://doi.org/10.25287/ohuiibf.538592
  35. Saritas, H., Kilic, E., & Nazlioglu, E. H. (2021). Analysis of the Relationship Between Credit Default Swaps (CDS), Credit Ratings and Stock Markets: The Case of Turkey. Journal of Finance Letters, (116). https://doi.org/10.33203/mfy.854876
  36. Sarwar, G. (2012). Is VIX an investor fear gauge in BRIC equity markets?. Journal of Multinational Financial Management, 22(3), 55-65. https://doi.org/10.1016/j.mulfin.2012.01.003
  37. Senturk, M., & Ducan, E. (2014). The Relationship between Exchange Rate-Interest Rate and Stock Return in Turkey: An Empirical Analysis. Business and Economics Research Journal, 5(3), 67.
  38. Sevinc, E. (2014). Determination of the impact of macroeconomic variables on stock returns traded on bist-30 by using arbitrage pricing theory. Istanbul University Journal of the School of Business, 43(2), 271-292.
  39. Shahzad, S. J. H., Aloui, C., & Jammazi, R. (2020). On the interplay between US sectoral CDS, stock and VIX indices: Fresh insights from wavelet approaches. Finance Research Letters, 33, 101208. https://doi.org/10.1016/j.frl.2019.06.006
  40. Shahzad, S. J. H., Bouri, E., Rehman, M. U., & Roubaud, D. (2022). The hedge asset for BRICS stock markets: Bitcoin, gold or VIX. The World Economy, 45(1), 292-316. https://doi.org/10.1111/twec.13138
  41. Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. Festschrift in honor of Peter Schmidt: Econometric methods and applications, 281-314. http://dx.doi.org/10.2139/ssrn.1807745
  42. Shu, J., & Zhang, J. E. (2012). Causality in the VIX futures market. Journal of Futures Markets, 32(1), 24-46. https://doi.org/10.1002/fut.20506
  43. TCMA (2024a). Turkish Capital Market Association, Publication No: 94, https://tspb.org.tr/en/yayinlar/kategori-yillik-yayinlar/, Access Date: 01.04.2024
  44. TCMA (2024b). Turkish Capital Market Association, https://tspb.org.tr/en/data-bank/, Access Date: 01.04.2024 Telek, C. (2020). Relationship Of VIX Index with Portfolio Investments and Exchange Rates in Turkey. İzmir Journal of Economics, 35(3), 635-646. https://doi.org/10.24988/ije.202035314
  45. Tsai, I.C. (2014). Spillover of fear: Evidence from the stock markets of five developed countries. International Review of Financial Analysis, 33, 281-288. https://doi.org/10.1016/j.irfa.2014.03.007
  46. Tuncel, M.B., & Gursoy, S. (2020). An Empirical Application on The Causality Relationship Among Fear Index (VIX), Bitcoin Prices and BIST100 Index. Electronic Journal of Social Sciences, 19(76), 1999-2011. https://doi.org/10.17755/esosder.712702
  47. Vergili, G., & Çelik, M. S. (2023). The Relationship Between the Indices of Volatility (VIX) and Sustainability (DJSEMUP): An ARDL Approach. Business and Economics Research Journal, 14(1), 19-29. doi: 10.20409/berj.2023.401
  48. WDI (2024). World Development Indicators, https://databank.worldbank.org/source/world-development-indicators#, Access Date: 01.04.2024.
  49. Yildiz, B. & Sanli, O. (2023). Investigation Of the Relationship Between Macroeconomic Indicators and Stock Market Indices and the Impact of COVID-19. International Journal of Social Humanities Sciences Research, 10(93), 628-644. https://doi.org/10.26450/jshsr.3587