Tahvil ve Hisse Senedi Piyasaları Arasında Zamanla Değişen Nedensellik İlişkileri: Hatemi-J Dinamik Nedensellik Testi Bulguları

Yıl 2022, , 97 - 116, 24.10.2022

Mevlüt Camgöz

https://doi.org/10.25095/mufad.1118637

Öz

İktisadi gelişmeler ve risk faktörlerindeki belirsizlik durumu varlık sınıfları arasındaki ilişkileri zamana bağlı olarak
değiştirebilmektedir. Finans teorisinde merkezi bir konumda olan tahvil ve hisse senedi piyasaları arasındaki nedensellik
ilişkilerine odaklanan bu çalışma, tahvil piyasasından hisse senedi piyasasına doğru simetrik ve asimetrik nedensellik
ilişkisinin olup olmadığını ve eğer varsa bu ilişkinin zamana bağlı olarak değişip değişmediğini ortaya koymayı
amaçlamaktadır. Analiz yöntemi olarak Hatemi-J (2021) tarafından geliştirilen dinamik simetrik ve asimetrik nedensellik
testleri benimsenmiştir. Analiz Ocak 1980 ile Nisan 2022 tarihleri arasında gerçekleştirilmiş ve ABD (Amerika Birleşik
Devletleri) tahvil ve hisse senedi piyasası incelenmiştir. Dinamik simetrik ve asimetrik nedensellik testi bulguları tahvil
piyasasından hisse senedi piyasasına doğru olan nedensellik ilişkisinin zamana bağlı olarak değiştiğini göstermektedir. Yani
tahvil piyasası ile hisse senedi piyasası arasındaki nedensellik ilişkilerinde yapısal değişimler ve zaman bağımlılığı
mevcuttur. Bu durumda statik modellerin riskten korunma, çeşitlendirme ve varlık dağılımı gibi kararlarda taraflı
yönlendirme yapabileceği ortaya konulmuş olmaktadır.

Anahtar Kelimeler

Varlık Piyasaları Arası İlişkiler, Zamanla Değişen Nedensellik, Hatemi-J Dinamik Nedensellik Testi

Destekleyen Kurum

Yoktur

Proje Numarası

Yoktur

Teşekkür

-

Time Varying Causality Relationships Between Bonds and Stock Markets: Findings of the Hatemi-J Dynamic Causality Test

Öz

Economic developments and the uncertainty in risk factors can change the relationships between asset classes over time. Assuming that the relations between financial markets are of static in nature will result in erroneous investment and policy decisions, even if they are dynamic in reality. Focusing on the causality relationships between bond and stock markets, which has a central point of finance theory, this study aims to reveal whether there is symmetric and asymmetric causality relationship from bond market to the stock market, and if there is, whether this relationship changes over time. Dynamic symmetric and asymmetric causality tests developed by Hatemi-J (2021) are adopted as the analysis method. Dynamic symmetric and asymmetric causality test findings show that the causality relationship from bond market to the stock market changes over time. In conclusion, this study suggest that static models can lead to biased decisions such as hedging, diversification and asset allocation by demonstrating the structural changes and time dependency in the causal relationships between the bond and the stock market.

Anahtar Kelimeler

Asset Markets Linkages, Time Varying Causality, Hatemi-J Dynamic Causality Test

Proje Numarası

Yoktur

Kaynakça

• Bahmani-Oskooee, Mohsen - Ghodsi, Seyed Hesam - Hadzic, Muris (2020), “Asymmetric Causality Between Stock Returns and Usual Hedges: An Industry-Level Analysis”, The Journal of Economic Asymmetries, 21, e00160.
• Balcilar, Mehmet - Demirer, Rıza - Gupta, Rangan - Wohar, Mark E. (2020), “The Effect of Global and Regional Stock Market Shocks on Safe Haven Assets”, Structural Change and Economic Dynamics, 54, 297-308.
• Barnett, Alina - Mumtaz, Haroon - Theodoridis, Konstantinos (2012), “Forecasting UK GDP Growth, Inflation and Interest Rates Under Structural Change: A Comparison of Models with Time Varying Parameters”, Bank of England Working Papers 450, 1–55.
• Bredin, Don - Conlon, Thomas - Poti, Valerio (2015), “Does Gold Glitter in the Long-Run? Gold as a Hedge and Safe Haven Across Time and Investment Horizon”, International Review of Financial Analysis, 41, 320–328.
• Ciner, Cetin - Gurdgiev, Constantin - Lucey, Brian M. (2013), “Hedges and Safe Havens: An Examination of Stocks, Bonds, Gold, Oil and Exchange Rates”, International Review of Financial Analysis, 29, 202–211.
• Connolly, Robert - Stivers, Chris - Sun, Licheng (2005), “Stock Market Uncertainty and the Stock-Bond Return Relation”, Journal of Financial and Quantitative Analysis, 40(1), 161–194.
• Dogan, Eyup - Majeed, Muhammad Tariq - Luni, Tania (2022), “Analyzing the Nexus of Covid-19 and Natural Resources and Commodities: Evidence from Time-Varying Causality”, Resources Policy, 77, 102694.
• Engle, Rober F. - Granger, Clive W. J. (1987), “Co-Integration and Error Correction: Representation, Estimation, and Testing”, Econometrica, 55(2), 251–276.
• Fatum, Rasmus - Yamamoto, Yohei (2016), “Intra-Safe Haven Currency Behavior During the Global Financial Crisis”, Journal of International Money and Finance, 66, 49–64.
• Flavin, Thomas J. - Morley, Ciara E. - Panopoulou, Ekaterini (2014), “Identifying Safe Haven Assets for Equity Investors Through an Analysis of the Stability of Shock Transmission”, Journal of International Financial Markets, Institutions & Money, 33, 137–154.
• Granger, Clive - Yoon, Gawon (2002), “Hidden Cointegration”, SSRN Journal (SSRN Electronic Journal) University of California, Economics Working Paper No. 2002-02.
• Granger, Clive W. (1986), “Developments in the Study of Cointegrated Economic Variables”, Oxford Bulletin of Economics and Statistics, 48(3), 213-228.
• Granger, Clive W. (1988), “Some Recent Development in a Concept of Causality”, Journal of Econometrics, 39(1-2), 199-211.
• Gulko, Les (2002), “Decoupling”, Journal of Portfolio Management, 28, 59–66.
• Hacker R. Scott - Hatemi-J, Abdulnasser (2003), “How Productivity and Domestic Output are Related to Exports and Foreign Output in the Case of Sweden”, Empirical Economics, 28(4), 767–782.
• Hacker R. Scott - Hatemi-J, Abdulnasser (2006), “Tests for Causality Between Integrated Variables Using Asymptotic and Bootstrap Distributions: Theory and Application”, Applied Economics, 38(13), 1489–1500.
• Hacker R. Scott - Hatemi-J, Abdulnasser (2010), “The Properties of Procedures Dealing with Uncertainty about Intercept and Deterministic Trend in Unit Root Testing”, Working Paper Series in Economics and Institutions of Innovation 214, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies, Stockholm.
• Hatemi-J, Abdulnasser (2003), “A New Method to Choose Optimal Lag Order in Stable and Unstable VAR Models”, Applied Economics Letters, 10(3), 135–137.
• Hatemi-J, Abdulnasser (2008), “Forecasting Properties of a New Method to Determine Optimal Lag Order in Stable and Unstable VAR Models”, Applied Economics Letters, 15(4), 239–243.
• Hatemi-J, Abdulnasser (2012), “Asymmetric Causality Tests with an Application”, Empirical Economics, 43(1), 447–456.
• Hatemi-J, Abdulnasser (2021), “Dynamic Asymmetric Causality Tests with an Application”, Papers 2106.07612, arXiv.org.
• Li, Lingfeng (2002), “Macroeconomic Factors and the Correlation of Stock and Bond Returns”, Yale ICF Working Paper, 02-46.
• Pericoli, Marcello (2018), “Macroeconomics Determinants of the Correlation Between Stocks and Bonds”, Temi di discussione (Economic working papers) 1198, Bank of Italy, Economic Research and International Relations Area.
• Pesaran, M. Hashem - Shin, Yongcheol - Smith, Richard J. (2001), “Bounds Testing Approaches to the Analysis of Level Relationships”, Journal of Applied Econometrics, 16(3), 289–326.
• Phillips, Peter C. B. - Shi, Shu-Ping - Yu, Jun (2015), “Testing for multiple bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500”, International Economic Review, 56(4), 1043–1078.
• Sakurai, Yuji (2021), “How has the Relationship Between Safe Haven Assets and the US Stock Market Changed After the Global Financial Crisis?”, Journal of International Financial Markets, Institutions and Money, 101351.
• Shi, Shuping - Hurn, Stan - Phillips, Peter C. B. (2020), “Causal change detection in Possibly Integrated Systems: Revisiting the Money–Income Relationship”, Journal of Financial Economics, 18(1), 158–180.
• Shi, Shuping - Phillips, Peter - Hurn, Stan (2018), “Change Detection and the Causal Impact of the Yield Curve”, Journal of Time Series Analysis, 39(6), 966-987.
• Shin, Yongcheol - Yu, Byungchul - Greenwood-Nimmo, Matthew (2014), “Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework”, In R. Sickels, & W. Horrace (Eds.), Festschrift in honor of Peter Schmidt: Econometric methods and applications (pp. 281–314). New York: Springer.
• Song, Dongho (2017), “Bond Market Exposures to Macroeconomic and Monetary Policy Risks”, Review of Financial Studies, 30(8), 2761–2817.
• Tachibana, Minoru (2022), “Safe Haven Assets for international stock markets: A Regime-Switching Factor Copula Approach”, Research in International Business and Finance, 60, 101591.
• Tobin, James (1958), “Liquidity Preference as Behavior towards Risk”, Review of Economic Studies, 25, 65-86.
• Toda, Hiro Y. – Yamamoto, Taku (1995), “Statistical Inference in Vector Autoregressions with Possibly Integrated Processes”, Journal of Econometrics, 66, 225-250.
• Tuna, Vedat Ender - Tuna, Gülfen - Kostak, Nurcan (2021), “The Effect of Oil Market Shocks on the Stock Markets: Time-Varying Asymmetric Causal Relationship for Conventional and Islamic Stock Markets”, Energy Reports, 7, 2759–2774.
• Yilanci, Veli - Kilci, Esra N. (2021), “The Role of Economic Policy Uncertainty and Geopolitical Risk in Predicting Prices of Precious Metals: Evidence from A Time-Varying Bootstrap Causality Test”, Resources Policy, 72, 102039.
• Yunus, Nafeesa (2020). “Time-Varying Linkages Among Gold, Stocks, Bonds and Real Estate”, The Quarterly Review of Economics and Finance, 77, 165-185.