Effect Mechanisms of Capital Markets on Housing Prices through Dynamic Causality: The Case of Turkey

Year 2022, Volume: 7 Issue: 2, 334 - 365, 30.06.2022

Erdost Torun Erhan Demireli

https://doi.org/10.30784/epfad.1107034

Abstract

Fluctuations in housing and stock markets affect economic growth, thus causing socio-economic changes in economies. In this context, examining the temporal variations of causality relations in these markets has become a necessity for investors and policymakers, as it provides useful insights in terms of understanding the nature of the inter-market information flows. The main purpose of this study is to reveal the time-based and scale-based causality information flow between housing price and stock market index, and to find evidence for both whether and when theories regarding the relationship between housing and stock markets are valid empirically by using the empirical approach proposing the use of SPH and CWTC tests. Through using the CWTC (Continuous Wavelet Transformation Based Granger Causality Test) and SPH, which allow for the analysis of non-stationary data directly, evidence that the causality between the housing and stock markets varies over time and has dynamics varying based on the time scale is found in this study. Moreover, results indicate that structural changes exist in the causality relationship. The growth model, Central Bank of Turkey (CBT) interest rate policy, Federal Reserve Bank (FED) interest policy, geopolitics risk factors and pandemics are possible factors affecting the causality relationship.  

Keywords

Causality Test, Non-stationary Data Analysis, Housing Market, Stock Market Index, Wavelet Analysis, LA-VAR

Konut Fiyatlarında Sermaye Piyasasının Etkileri: Dinamik Nedensellik İle Türkiye Üzerine Bir İnceleme

Abstract

Konut piyasaları ve borsalar, servetin önemli bileşenlerinden olmaları nedeniyle sözkonusu piyasalarda meydana gelen dalgalanmalar ekonomik büyümeyi etkileyerek sosyo-ekonomik değişimlere neden olmaktadır. Sözkonusu nedensellik ilişkilerinin zamana bağlı değişiminin incelenmesi, piyasalar arası bilgi akışının doğasının anlaşılması açısından yararlı bilgiler sunması nedeniyle yatırımcı ve politika yapıcılar için zorunluluk halini almıştır. Çalışmanın temel amacı, zamana bağlı değişen nedensellik testlerinin kullanılmasını öngören ampirik yaklaşım aracılığıyla konut fiyatları ve borsa endeksi arasındaki zamana dayalı nedensellik etkisinin zamana ve zaman skalasına göre değişiminin ortaya çıkarılarak ilgili ilişkiye ait teorilerin geçerliliğine dair kanıt bulmak ve sözkonusu teorilerin geçerli olabileceği zaman ve frekans dönemlerini incelemektir. Çalışmada durağan olmayan verilerin analizine izin veren CWTC (Continuous Wavelet Transformantion Based Granger Casuality Test) ve SPH (Shi – Hurn – Phillips (2020) test) testleri kullanılarak, konut piyasası ve borsa endeksi arasındaki nedenselliğin zamana bağlı değiştiği ve zaman skalasına göre değişen dinamiklere sahip olduğuna ilişkin kanıtlar bulunmuştur. Bununla birlikte ilgili piyasalarda yapısal kırılmalar meydana geldiğine dair kanıt elde edilmiştir. Nedensellik örüntüsündeki değişimlerin olduğu dönemlerin incelenmesi sonucunda; büyüme modeli, Türkiye Cumhuriyet Merkez Bankası (TCMB) faiz politikası, Amerikan Merkez Bankası (FED) faiz politikası, jeopolitik riskler ve pandemi sürecinin olduğu dönemlerde nedensellik tespit edilmiştir. 

Keywords

Nedensellik Testi, Durağan Olmayan Veri Analizi, Konut Piyasası, Borsa Endeksi, Dalgacık Analizi, LA-VAR

References

• Aguiar-Conraria, L., Azevedo, N. and Soares, M.J. (2008). Using wavelets to decompose the time–frequency effects of monetary policy. Physica A, 387, 2863–2878. https://doi.org/10.1016/j.physa.2008.01.063
• Aguiar-Conraria, L., Soares, M.J. and Sousa, R. (2018). California’s carbon market and energy prices: A wavelet analysis. Philosophical Transactions of the Royal Society Mathematical Physical and Engineering Sciences, 376, 1-16. https://doi.org/10.1098/rsta.2017.0256
• Albulescu, C.T., Goyeau, D. and Tiwari, A.K. (2017). Co-movements and contagion between international stock index futures markets. Empirical Economics, 52(4), 1529-1568. https://doi.org/10.1007/s00181-016-1113-5
• Almasri, A. and Shukur, G. (2003). An illustration of the causality relationship between government spending and revenue using wavelets analysis on Finnish data. Journal of Applied Statistics, 30(5), 571–584. https://doi.org/10.1080/0266476032000053682
• Andries, A.M., Căpraru, B., Ihnatov, I. and Tiwari, A.K. (2017). The relationship between exchange rates and interest rates in a small open emerging economy: The case of Romania. Economic Modelling, 67, 261-274. https://doi.org/10.1016/j.econmod.2016.12.025
• Andries, A.M., Ihnatov, I. and Tiwari, A.K. (2014). Analyzing time–frequency relationship between interest rate, stock price and exchange rate through continuous wavelet. Economic Modelling, 41, 227-238. https://doi.org/10.1016/j.econmod.2014.05.013
• Bekiros, S.D. and Diks, C.G.H. (2008). The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality. Energy Economics, 30, 2673–2685. https://doi.org/10.1016/j.eneco.2008.03.006
• Benhmad, F. (2012). Modeling nonlinear Granger causality between the oil price and U.S. dollar: A wavelet based approach. Economic Modelling, 29, 1505–1514. https://doi.org/10.1016/j.econmod.2012.01.003
• Breitung, J. and Candelon, B. (2006). Testing for short- and long-run causality: A frequency-domain approach. Journal of Econometrics, 132(2), 363-378. https://doi.org/10.1016/j.jeconom.2005.02.004
• Case, K.E., Quigley, J.M. and Shiller, R.J. (2005). Comparing wealth effects: The stock market versus the housing market. Advances in Macroeconomics, 5, 1–32. https://doi.org/10.2202/1534-6013.1235
• Chauvet, M. (1999). Stock market fluctuations and the business cycle. Journal of Economic and Social Measurement, 25, 235–257. doi:10.3233/JEM-1999-0166
• Chou, C.C. and Chen S.-L. (2011). Integrated or segmented? A wavelet transform analysis on relationship between stock and real estate markets. Economics Bulletin, 31(4), 3030-3040. Retrieved from http://www.accessecon.com/pubs/EB/
• Christiano, L.J. and Ljungqvist, L. (1988). Money does Granger-cause output in the bivariate money–output relation. Journal of Monetary Economics, 22, 217–235. https://doi.org/10.1016/0304-3932(88)90020-7
• Crowley, P.M. and Mayes, D.G. (2009). How fused is the euro area core? Journal of Business Cycle Measurement and Analysis, 1, 63-95. https://doi.org/10.1787/19952899
• Dhamala, M., Rangarajan, G. and Ding, M. (2008a). Estimating Granger causality from Fourier and wavelet transforms of time series data. Physical Review Letters, 100(1), 018701. https://doi.org/10.1103/PhysRevLett.100.018701
• Dhamala, M., Rangarajan, G. and Ding, M. (2008b). Analyzing information flow in brain networks with nonparametric Granger causality. NeuroImage, 41, 354–362. https://doi.org/10.1016/j.neuroimage.2008.02.020
• Diks, C. and Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics & Control, 30, 1647–1669. https://doi.org/10.1016/j.jedc.2005.08.008
• Durai, S.R.S. and Bhaduri, S.N. (2009). Stock prices, inflation and output: Evidence from wavelet analysis. Economic Modelling, 26(5), 1089-1092. https://doi.org/10.1016/j.econmod.2009.04.005
• Eichenbaum, M. and Singleton, K.J. (1986). Do equilibrium real business cycle theories explain postwar U.S. business cycles. NBER Macroeconomics Annual, 1, 91–146. Retrieved from http://www.nber.org/
• Eichler, M. (2007). Granger causality and path diagrams for multivariate time series. Journal of Econometrics, 137(2), 334-353. https://doi.org/10.1016/j.jeconom.2005.06.032
• Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association, 77(378), 304-313. doi:10.1080/01621459.1982.10477803
• Grinsted, A., Moore, J.C. and Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11, 561–566 https://doi.org/10.5194/npg-11-561-2004
• Hong, Y., Liu, Y. and Wang, S. (2009). Granger causality in risk and detection of extreme risk spillover between financial markets. Journal of Econometrics, 150(2), 271-287. https://doi.org/10.1016/j.jeconom.2008.12.013
• Iacoviello, M. and Neri, S. (2010). Housing market spillovers: Evidence from an estimated DSGE model. American Economic Journal: Macroeconomics, 2, 125–164. doi:10.1257/mac.2.2.125
• In, F. and Kim, S. (2006). The hedge ratio and the empirical relationship between the stock and futures markets: A new approach using wavelet analysis. The Journal of Business, 79(2), 799-820. https://doi.org/10.1086/499138
• Kapopoulos, P. and Siokis, F. (2005). Stock and real estate prices in Greece: Wealth versus ‘credit-price’ effect. Applied Economics Letters, 12(2), 125–128. https://doi.org/10.1080/1350485042000307107
• Kim, S. and In, F.H. (2003). The relationship between financial variables and real economic activity: Evidence from spectral and wavelet analyses. Studies in Nonlinear Dynamics & Econometrics, 7(4). https://doi.org/10.2202/1558-3708.1183
• Leamer, E.E. (2007). Housing is the business cycle (NBER Working Paper No. 13428). Retrieved from https://www.nber.org/system/files/working_papers/w13428/w13428.pdf
• Leamer, E.E. (2015). Housing really is the business cycle: What survives the lessons of 2008–09? Journal of Money, Credit and Banking, 47(1), 53-50. https://doi.org/10.1111/jmcb.12189
• Li, J-P., Fan, J-J., Su, C-W. and Lobont, O-R. (2017). Investment coordinates in the context of housing and stock markets nexus. Applied Economics Letters, 24(20), 1455-1463. https://doi.org/10.1080/13504851.2017.1284978
• Li, X.L., Tsangyao, C., Miller, S.M., Balcilar, M. and Gupta, R. (2015). The Co-movement and causality between the U.S housing and stock markets in the time and frequency domains. International Review of Economics and Finance, 38, 220–233. https://doi.org/10.1016/j.iref.2015.02.028
• Månsson, K. (2012). A wavelet-based approach of testing for Granger causality in the presence of GARCH effects. Communications in Statistics-Theory and Methods, 41(4), 717-728. https://doi.org/10.1080/03610926.2010.529535
• Mitra, S. (2006). A wavelet filtering based analysis of macroeconomic indicators: The Indian evidence. Applied Mathematics and Computation 175, 1055–1079. https://doi.org/10.1016/j.amc.2005.08.019
• Moore, G.H. (1983). Security markets and business cycles. In G.H. Moore (Ed.), Business cycles, & forecasting inflation (pp .139–160). California: Ballinger
• Olayeni, O.R. (2016). Causality in continuous wavelet transform without spectral matrix factorization: Theory and application. Computational Economics, 47(3), 321-340. https://doi.org/10.1007/s10614-015-9489-4
• Polanco-Martínez, J.M. and Abadie, L.M. (2016). Analyzing crude oil spot price dynamics versus long term future prices: A wavelet analysis approach. Energies, 9(12), 1089. 1-19, https://doi.org/10.3390/en9121089
• Rhif, M., Ben Abbes, A., Farah, I.R., Martínez, B. and Sang, Y. (2019). Wavelet transform application for/in non-stationary time-series analysis: A review. Applied Sciences, 9(7), 1345. https://doi.org/10.3390/app9071345
• Rua, A. (2010). Measuring comovement in the time frequency space. Journal of Macroeconomics, 32, 685–91. https://doi.org/10.1016/j.jmacro.2009.12.005
• Rua, A. (2013). Worldwide synchronization since the nineteenth century: A wavelet-based view. Applied Economics Letters, 20(8), 773-776. https://doi.org/10.1080/13504851.2012.744129
• Rua, A. and Nunes, L.C. (2012). A wavelet-based assessment of market risk: The emerging markets case. The Quarterly Review of Economics and Finance, 52(1), 84-92. https://doi.org/10.1016/j.qref.2011.12.001
• Shi, S., Hurn, S. and Phillips, P.B. (2020). Causal change detection in possibly integrated systems: Revisiting the money- income relationship. Journal of Financial Econometrics, 18(1), 158-180. https://doi.org/10.1093/jjfinec/nbz004
• Sims, C.A. (1987). Vector Autoregressions and reality: Comment. Journal of Business & Economic Statistics, 5(4), 443–449. https://doi.org/10.2307/1391993
• Stock, J.H. and Watson, M.W. (1989). Interpreting the evidence on money–income causality. Journal of Econometrics, 40(1), 161–181. https://doi.org/10.1016/0304-4076(89)90035-3
• Tiwari, A.K., Mutascu, M.I. and Albulescu, C.T. (2013). The influence of the international oil prices on the real effective exchange rate in Romania in a wavelet transform framework. Energy Economics, 40, 714-733. https://doi.org/10.1016/j.eneco.2013.08.016
• Toda, H.Y. and Yamamoto, T. (1995). Statistical inference in vector autoregressive with possibly integrated process. Journal of Econometrics, 66, 225-250. https://doi.org/10.1016/0304-4076(94)01616-8
• Torrence, C. and Compo, G.P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61-78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
• Wilson, G.T. (1972). The factorization of matricial spectral densities. SIAM Journal on Applied Mathematics, 23(4), 420-426. https://doi.org/10.1137/0123044
• Wilson, G.T. (1978). A convergence theorem for spectral factorization. Journal of Multivariate Analysis, 8(2), 222 - 232. https://doi.org/10.1016/0047-259X(78)90073-8