Online Estimation Platform
This website is created to offer everyone who is interested in spillover effects the opportunity to start their own investigations. We strongly believe that this approach is a milestone in econometrics since it demonstrates how shocks spread within a predetermined network which facilitate visualising how the transmission mechanism of different crises worked through various economic channels. As highlighted in Diebold and Yılmaz (2014) this procedure can be used as early warning system for numerous economic entities. Unfortunately, only few statistical softwares include a package regarding this methodology which motivated us to provide a general framework without any barriers for those without programming skills or which stick to a specific software to apply the dynamic connectedness approach . We would be glad if you honour our effort by citing one of our studies in your research if you are using our online implementation.
How does the online dynamic connectedness approach work?
- 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. [Dataset][Working Paper]
- 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. [Dataset][Working Paper]
- Antonakakis, N. (2012). Exchange return co-movements and volatility spillovers before and after the introduction of euro. Journal of International Financial Markets, Institutions and Money, 22(5), 1091-1109. [PreEuro][PostEuro][Working Paper]
- Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & Perez de Garcia, F. (2018). Oil volatility, oil and gas firms and portfolio diversification. Energy Economics . [Dataset][Working Paper]
- Antonakakis, N., & Gabauer, D. (2017). Refined Measures of Dynamic Connectedness based on TVP-VAR. [Dataset][Working Paper]
- H. Markowitz (1952). Portfolio selection. Journal of finance, pages 77–91.
- P.C. Fishburn (1977). Mean-risk analysis with risk associated with below-target returns. The American Economic Review, pages 116–126.
- M.R. Young (1998). A minimax portfolio selection rule with linear programming solution. Management science, pages 673–683.
- C. Acerbi and D. Tasche (2002). Expected shortfall: A natural coherent alternative to value at risk. Economic Notes, 31(2):379–388.
- A. Chekhlov, S. Uryasev, and M. Zabarankin (2005). Drawdown measure in portfolio optimization. International Journal of Theoretical and Applied Finance, 8(1):13–58, 2005.
- H. Konno, K. Tanaka, and R. Yamamoto (2011). Construction of a portfolio with shorter downside tail and longer upside tail. Computational Optimization and Applications, 48(2):199–212.