Global Belirsizlik Faktörlerinin BIST Hisse Senedi Fiyatlarına Asimetrik Etkilerinin NARDL Modeliyle Analizi

Year 2022, Issue: 118, 71 - 100, 21.10.2022

Mevlüt Camgöz

https://doi.org/10.33203/mfy.1103403

Cited By: 1

Abstract

Bu çalışmanın amacı global belirsizlik faktörlerinin BIST hisse senedi fiyatlarına olan kısa ve uzun dönem asimetrik etkilerinin NARDL (Nonlineer Gecikmesi Dağıtılmış Otoregresif) yöntemi kullanılarak ortaya konulmasıdır. Global belirsizlik faktörlerini temsilen ekonomik, jeopolitik, enerji ve finansal risklerin ölçüldüğü zımni volatilite endeksleri kullanılmıştır. Ampirik bulgulara göre, incelenen tüm hisse senedi fiyatları ile global belirsizlik faktörleri arasında uzun dönem asimetrik eşbütünleşme ilişkisi saptanmıştır. Hem uzun dönem hem de kısa dönem analizlerde, belirsizlik faktörleri hisse senedi fiyatlarını farklı yön ve ölçüde, asimetrik olarak etkilemektedir. Elde edilen bulgular yatırımcılar açısından varlık dağılımı, çeşitlendirme, risk yönetimi ve alım-satım kararlarının verilmesinde önemli bir rol oynayacaktır.

Keywords

Belirsizlik Faktörleri, Zımni Volatilite Endeksleri, Hisse Senedi Piyasası, NARDL Yöntemi

Supporting Institution

Yoktur

Project Number

Yoktur

Thanks

-

Analysis of the Short and Long Term Asymmetric Effects of Global Uncertainty Factors on BIST Stock Prices via the NARDL Method

Abstract

The aim of this study is to examine the short and long-term asymmetric effects of global uncertainty factors on BIST stock prices, employing the NARDL (Nonlinear Autoregressive Distributed Lag) method. Implied volatility indices, which measure economic, geopolitical, energy, and financial risks, are used to represent global uncertainty factors. Empirical findings show that there is a long-term asymmetric cointegration relationship between all stocks and global uncertainty factors under investigation. In both long-term and short-term analyzes, uncertainty factors affect stock prices in different directions and extents asymmetrically. The findings will figure an important role in making asset allocation, diversification, risk management, and trading decisions for investors and portfolio managers.

Keywords

Uncertainty Factors, Implied Volatility Indices, Stock Market, NARDL Method

Project Number

Yoktur

References

• Abakah, E.J.A., Tiwari, A.K., Alagidede, I.P., & Gil-Alana, L.A. (2022). Re-Examination of Risk-Return Dynamics in International Equity Markets and The Role of Policy Uncertainty, Geopolitical Risk and VIX: Evidence Using Markov-Switching Copulas. Finance Research Letters, 102535.
• Alexeev, V., & Tapon, F. (2012). Equity Portfolio Diversification: How Many Stocks are Enough? Evidence from Five Developed Markets. SSRN Electronic Journal.
• Arouri, M., Estay, C., Rault, C., & Roubaud, D. (2016). Economic Policy Uncertainty and Stock Markets: Long-Run Evidence from The US. Finance Research Letters, 18, 136–141.
• Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring Economic Policy Uncertainty. Quarterly Journal of Economics, 131(4), 1593–1636.
• Balcilar, M., Bonato, M., Demirer, R., & Gupta, R. (2018). Geopolitical Risks and Stock Market Dynamics of The BRICKs. Economic Systems, 42(2), 295–306.
• Bartsch, Z. (2019). Economic Policy Uncertainty and Dollar-Pound Exchange Rate Return Volatility. Journal of International Money and Finance, 98, 102067.
• Batabyal, S. & Killins, R. (2021). Economic Policy Uncertainty and Stock Market Returns: Evidence from Canada. The Journal of Economic Asymmetries, 24, e00215.
• Belcaid, K. & El Ghini, A. (2019). U.S., European, Chinese Economic Policy Uncertainty and Moroccan Stock Market Volatility. The Journal of Economic Asymmetries, 20, e00128.
• Burmeister, E. & Wall, K.D. (1986). The Arbitrage Pricing Theory and Macroeconomic Factor Measures. Financial Review, 21, 1-20.
• Caggiano, G., Castelnuovo, E., & Figueres, J. M. (2017). Economic Policy Uncertainty and Unemployment in The United States: A Nonlinear Approach. Economics Letters, 151, 31–34.
• Caldara, D., & Iacoviello, M. (2018). Measuring Geopolitical Risk. Working Paper. Board of Governors of the Federal Reserve Board.
• Chen, N., Roll, R., & Ross, S. A. (1986). Economic Forces and the Stock Market. The Journal of Business, 59(3), 383–403.
• Choi, S.-Y. (2022). Evidence from A Multiple and Partial Wavelet Analysis On the Impact of Geopolitical Concerns On Stock Markets in North-East Asian Countries. Finance Research Letters, 102465.
• Diaz, E. M., Molero, J. C., & Perez de Gracia, F. (2016). Oil Price Volatility and Stock Returns in the G7 Economies. Energy Economics, 54, 417–430.
• Dutta, P., Noor, M.H., & Dutta, A. (2017). Impact of Oil Volatility Shocks On Global Emerging Market Stock Returns. International Journal of Managerial Finance, 13(5), 578–591.
• Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251–276.
• Fousekis, P., Katrakilidis, C. & Trachanas, E. (2016). Vertical Price Transmission in The US Beef Sector: Evidence from The Nonlinear ARDL Model. Economic Modelling, 52, 499-506.
• Gu, R. & Liu, S. (2022). Nonlinear Analysis of Economic Policy Uncertainty: Based On the Data in China, The US and The Global. Physica A: Statistical Mechanics and its Applications, 593, 126897.
• Gu, X., Zhu, Z. & Yu, M. (2021). The Macro Effects of GPR and EPU Indexes Over the Global Oil Market—Are The Two Types of Uncertainty Shock Alike? Energy Economics, 100, 105394.
• Hatemi-J, A., Al Shayeb, A., & Roca, E. (2017). The Effect of Oil Prices On Stock Prices: Fresh Evidence from Asymmetric Causality Tests. Applied Economics, 49, 1584–1592.
• Hoque, M.E., & Zaidi, M.A.S. (2020). Global and Country-Specific Geopolitical Risk Uncertainty and Stock Return of Fragile Emerging Economies. Borsa Istanbul Review, 20(3), 197–213.
• Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231–254.
• Liang, C.C., Troy, C., & Rouyer, E. (2020). US Uncertainty and Asian Stock Prices: Evidence from the Asymmetric NARDL Model. The North American Journal of Economics and Finance, 51, 101046.
• McElroy, M. B., & Burmeister, E. (1988). Arbitrage Pricing Theory as a Restricted Nonlinear Multivariate Regression Model Iterated Nonlinear Seemingly Unrelated Regression Estimates. Journal of Business & Economic Statistics, 6(1), 29–42.
• P´astor, ˇL., & Veronesi, P. (2012). Uncertainty About Government Policy and Stock Prices. The Journal of Finance, 67(4), 1219–1264.
• 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.
• Ren, Y., Tan, A., Zhu, H., & Zhao, W. (2022). Does Economic Policy Uncertainty Drive Nonlinear Risk Spillover in The Commodity Futures Market? International Review of Financial Analysis, 81, 102084.
• Ross, S. (1971). Portfolio and Capital Market Theory with Arbitrary Preferences and Distributions: The General Validity of the Mean-Variance Approach in Large Markets. Working Paper No. 12-72, Rodney L. White Center for Financial Research.
• Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in A Nonlinear ARDL Framework. In Festschrift in honor of Peter Schmidt (pp. 281-314). Springer, New York, NY.
• Soucek, M., & Todorova, N. (2013). Economic Significance of Oil Price Changes On Russian and Chinese Stock Markets. Applied Financial Economics, 23, 561–571.
• Statman, M. (1987). How Many Stocks Make a Diversified Portfolio? The Journal of Financial and Quantitative Analysis, 22(3), 353–363.
• Wei, C. (2003). Energy, the Stock Market, and The Putty-Clay Investment Model. The American Economic Review, 93, 311–323.
• Xiao, J., Zhou, M., Wen, F., & Wen, F. (2018). Asymmetric Impacts of Oil Price Uncertainty On Chinese Stock Returns Under Different Market Conditions: Evidence from Oil Volatility Index. Energy Economics, 74, 777–786.
• Xie, Q., Wu, H., & Ma, Y. (2021). Refining The Asymmetric Impacts of Oil Price Uncertainty On Chinese Stock Returns Based On a Semiparametric Additive Quantile Regression Analysis. Energy Economics, 102, 105495.
• Xu, B. (2015). Oil Prices and UK Industry-Level Stock Returns. Applied Economics, 47, 2608–2627.
• Zaremba, A., Cakici, N., Demir, E., & Long, H. (2022). When Bad News Is Good News: Geopolitical Risk and The Cross-Section of Emerging Market Stock Returns. Journal of Financial Stability, 58, 100964.