PARA POLİTİKASI KARARLARININ BIST100’E ETKİSİNİN YAPISAL VAR MODELİ İLE ANALİZİ

Year 2020, Volume: 5 Issue: 12, 141 - 155, 30.06.2020

Mortaza Ojaghlou Özge Demirkale

https://doi.org/10.25204/iktisad.695741

Cited By: 1

Abstract

Bu çalışma Türkiye Cumhuriyeti Merkez Bankası para politikası kararlarının ve parasal değişkenlerin Borsa İstanbul üzerindeki uzun dönemli etkisini ölçmeyi amaçlamaktadır. Söz konusu amaca ulaşabilmek için HP filter (Hodrick–Prescott) ve Uzun Dönem (F matris) Yapısal VAR Modeli yöntemlerinden yararlanılmıştır. Analiz 2005Q04 ve 2019Q03 dönemlerini kapsamaktadır. Türkiye Cumhuriyeti Merkez Bankası para politikası kararlarının ve parasal değişkenlerin BIST100 üzerindeki etkisini ölçebilmek için M2 ve M3 para arzının dahil edildiği iki model kurulmuştur. Çalışmada kullanılan değişkenler M2, M3 para arzı, enflasyon ve kısa dönem faiz oranı, döviz kuru, S&P500 ve GDP değerleridir. Uzun Dönem Yapısal VAR modeli analize dahil edilen değişkenlere verilen ani şokların Borsa İstanbul’u ne yönde etkileyeceğini görmek amacıyla kullanılmıştır. Analizden elde edilen ampirik bulgular döviz kuru, para arzı (M2 ve M3), enflasyon ve faiz oranının BIST100 üzerinde ki etkilerinin kurulan iki modelde de negatif olduğu fakat GDP ve S&P etkisinin farklılık gösterdiği sonucuna ulaşılmıştır.

Keywords

Merkez Bankası, Para Politikası, BIST100, Uzun Dönem Yapısal VAR

STRUCTURAL VAR MODEL ANALYSIS OF THE IMPACT OF MONETARY POLICY DECISIONS ON BIST100

Abstract

This study aims to investigate the long-term impact of monetary policy decisions of the Central Bank of Republic of Turkey and monetary variables on Borsa Istanbul. To achieve this goal HP (Hodrick–Prescott) and long run (F matrix) of the Structured VAR (SVAR) model has been used in the study. The analysis covers the periods 2005Q04 and 2019Q03. In order to measure the impact of monetary policy of the Central Bank of the Republic of Turkey and monetary variables on BIST100, two models were applied which included the money supply of M2 and M3. The variables used in the study were M2, M3 money supply, inflation and short-term interest rate, exchange rate, S&P500 and GDP values. The Long Run Structural VAR model was used to see how the sudden shocks given to the variables - included in the analysis - would affect Borsa Istanbul. Empirical findings from the analysis the conclusion reached that the effects of exchange rate, money supply (M2 and M3), inflation and interest rate on BIST100 were negative in both applied models, but the effects of GDP and S&P were differed.

Keywords

Central Bank, Monetary Policy, BIST100, Long Run SVAR

References

• Alexander, W.E., Balino, T.J.T. & Enoch, C. (1995). The Adoption of Indirect Instruments of Monetary Policy. IMF Çalışma Tebliği. 126.
• Alshogeathri, M.A.M. (2011). Macroeconomic Determinants of the Stock Market Movements: Empirical Evidence from the Saudi Stock Market. Ph.D thesis, Kansas State University.
• Ashley, R. A., & Verbrugge, R.J. (2009). To Difference or Not to Difference: A Monte Carlo Investigation of Inference in Vector Autoregression Models. International Journal of Data Analysis Techniques and Strategies, 1(3), 242-274.
• Andersson, M. (2007). Using Intraday Data to Gauge Financial Market Responses to FED and ECB Monetary Policy Decisions, ECB Working Paper, No. 726.
• Bernanke, B. S. (1986). Alternative Explanations of the Money-Income Correlation. Carnegie-Rochester Conference Series on Public Policy, Elsevier, 25(1), 49-99.
• Berument, H.& Kutan, A.M. (2007). Para Politikası Kararlarının Hisse Senedi Piyasası Üzerine Etkisi: Türkiye Uygulaması. Scientific Journal of Administrive Development. (5), 117‐144.
• Bjørnland, H. & Leitemo, K. (2005). Identifying the Interdependence Between US Monetary Policy and The Stock Market. Journal of Monetary Economics, 56(2), 275-282.
• Boyd, J.H., Jagannathan, R. & Hu, J. (2001). The Stock Market’s Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks, NBER Working Paper 8092.
• Bhattacharya, B., & Mukherjee, J. (2003). Casual Relationship between Stock Market and Exchange Rate, Foreign Exchange Reserves and Value of Trade Balance. Presented in 5th Annual Conference on Money and Finance in India
• Conover, C. M., Jensen, G.R. & Johnson, R. R. (1999). Monetary Environments and International Stock Returns. Journal of Banking and Finance. 9 (23), 1357‐1381.
• Cook, T. & Hahn, T. (1989). The effect of Changes in The Federal Funds Rate Target on Market Interest Rates in The 1970s. Journal of Monetary Economics, 24(3), 331-351.
• Demiralp, S. &Yılmaz, K. (2010). Para Politikası Beklentierinin Sermaye Piyasaları Üzerindeki Etkisi. TÜSİAD, Koç University Economic Research Forum Working Paper Series.
• Duran, M., Özlü, P. & Ünalmış, D. (2010) TCMB Faiz Kararlarının Hisse Senedi Piyasaları Üzerine Etkisi, Central Bank Review, 10(2).
• Fair, R.C. (2006). Events That Shook the Market, The Journal of Business 75(2).
• Flannery, M. J., & Protopapadakis A.A. (2002). Macroeconomic Factors Do Influence Aggregate Stock Returns. The Review of Financial Studies. 15(3), 751-782.
• Franck, P., & Young., A. (1972). Stock Price Reaction of Multinational Firms to Exchange Realignments. Financial Management, 1(3), 66-73.
• Ionnidis, C.&Kontokinas, A. (2008). The Impact of Monetary Policy on Stock Prices, Journal of Policy Modeling. 1(30), 33‐53.
• Lange, Joe, Sack, Brian & William Whitesell, (2003). Anticipations of Monetary Policy in Financial Markets, Journal of Money, Credit, and Banking, 35: 889-909.
• Lindsey, D.E. & Wallich, H.C. (1996). The New Palgrave A Dictionary of Economics. Machmillan, (3), 508.
• Mishkin, F.S. (1996). The Channels of Monetary Transmission: Lessons for Monetary Policy, National Bureau of Economic Research, Working Paper 5464.
• Otsubo, K. P. (2019). The Effects of Unconventional Monetary Policy in Japan: New Evidence From Time-Varying Parameter VAR Analysis. The Singapore Economic Review (In progress), 1-31.
• Palley, T.I. (2001). The Stock Market and Investment: Another Look at the MicroFoundations of Q Theory. Cambridge Journal of Economics, Volume 25.
• Peersman, G. (2005). What Caused the Early Millennium Slowdown? Evidence Based on Autoregressions. Journal of Applied Econometrics, (20), 185-207.
• Pedroni, P. (2013). Structural Panel VARs, Econometrics, (2), 80-206.
• Pfaff, B. (2008). VAR, SVAR and SVEC Models: Implementation within R Package vars. Journal of Statistical Software, 27(4), 1-32.
• Rigobon, R. & Sack, B. (2002). The Impact of Monetary Policy on Asset Prices. Finance and Economics Discussion Series 2002-4. Board of Governors of the Federal Reserve System.
• Rubio-Ramirez, J., Waggoner, D. & T. Zha, T. (2010). Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference. Review of Economic Studies 77 (2): 665–696.
• Rüffer, R., & Stracca, L. (2006). What Is Global Excess Liquidity, And Does It Matter? ECB Working Paper Series, No. 696.
• Sancar, C. (2014). 2008 Finansal Kriz Öncesi ve Sonrasında Hisse Senedi Fiyat Endeksleri ve Para Politikası Arasındaki İlişki: OECD Ülkelerinde Bir Uygulama. Doktora Tezi. İnönü Üniversitesi Sosyal Bilimler Enstitüsü İktisat Anabilim Dalı
• Sims, C.A. (1980). Macroeconomics and Reality. Econometrica: Journal of the Econometric Society, 48(1), 1-48.
• Sims, C.A. (1986). Are Forecasting Models Usable for Policy Analysis?. Quarterly Review, (Win), 2-16.
• Soenen, L.A., & Henniger, E.S. (1988), An analysis of exchange rates and stock prices: The US experience between 1980 and 1986. Akron Business and Economic Review, 19(4), 7-16.
• Suhaibu, I., Harvey, S.K., & Amidu, M. (2017). The impact of monetary policy on stock market performance: Evidence from twelve (12) African countries. Research in International Business and Finance 42, 1372–1382.