INTERACTION AND VOLATILITY SPILLOVER AMONG SELECTED FINANCIAL ASSETS IN TÜRKİYE
Abstract
This study aims to examine the dynamic relationships and volatility propagation mechanisms among selected financial assets in Türkiye. By analyzing the interactions between the Borsa Istanbul 100 Index (XU100), interest rates, gold prices and the USD/TRY exchange rate, the study assesses how shocks between these assets propagate. Using a time-varying parameter vector autoregression (TVP-VAR) model, the analysis analyzes monthly data for the period 01:2002-10:2024. The findings of the study shed light on the complex and interconnected nature of financial markets. While the XU100 is most affected by its own past shocks, assets such as gold and exchange rates are more exposed to external shocks. Aggregate volatility dispersion analysis reveals that Borsa Istanbul and gold act as net shock emitters, while interest rates and exchange rates act as net shock receivers. These results have important implications for both policymakers and investors. Investors should optimize their portfolio management strategies in line with these dynamics, while policymakers should take measures to minimize economic uncertainties and the effects of external shocks. The study contributes to a more effective and sustainable analysis of Turkish financial markets.
Keywords
Financial Markets, Volatility, Time-Varying Parameter Vector Autoregressive Models (TVP-VAR), Türkiye
Supporting Institution
Bu araştırmanın hazırlanmasında herhangi bir kurumdan destek alınmamıştır.
Ethical Statement
Bu çalışmanın tüm hazırlanma süreçlerinde etik kurallara uyulduğunu yazarlar beyan eder. Aksi bir durumun tespiti halinde Akademik İzdüşüm Dergisinin hiçbir sorumluluğu olmayıp, tüm sorumluluk çalışmanın yazarlarına aittir.
References
- Adekoya, O. B. & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898.
- Akkuş, H. T. & Doğan, M. (2023) Analysis of dynamic connectedness relationships between cryptocurrency. NFT and DeFi assets: TVP-VAR approach. Applied Economics Letters, 1-6.
- Akyıldırım, E., Güneş, H. & Çelik, İ. (2022). Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar. Gazi İktisat Ve İşletme Dergisi, 8(2), 346-363.
- Antonakakis, N. & Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR. MPRA Paper No: 78282. Balcılar, M. & Zeydan, S. (2020). Turkey's Financial Markets and Volatility Dynamics: A Review of the Literature. Turkish Economic Review, 7(2), 129-155.
- Baur, D. G. & McDermott, J. B. (2010). Risk and return in emerging markets. Journal of International Money and Finance, 29(3), 442-457.
- Cao, G. & Xie, W. (2022). Asymmetric dynamic spillover effect between cryptocurrency and China’s financial market: Evidence from TVP-VAR based connectedness approach. Finance Research Letters, 49, 103070.
- Chatziantoniou, I., Floros, C. & Gabauer, D. (2022). Volatility contagion between crude oil and G7 stock markets in the light of trade wars and COVID-19: A TVP-VAR extended joint connectedness approach. In Applications in Energy Finance: The Energy Sector, Economic Activity, Financial Markets and the Environment (pp. 145-168). Cham: Springer International Publishing.
- Dahir, A. M., Mahat, F., Amin Noordin, B. A. & Hisyam Ab Razak, N. (2020). Dynamic connectedness between Bitcoin and equity market information across BRICS countries: Evidence from TVP-VAR connectedness approach. International Journal of Managerial Finance, 16(3), 357-371.
- Diebold, F. X. & Yılmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171.
- Diebold, F. X. & Yılmaz, K. (2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28(1), 57-66.