References
Aielli, G. P. (2013). Dynamic conditional correlation: on properties and estimation. Journal of Business & Economic Statistics, 31(3), 282-299.
Allen, D., Amram, R., & McAleer, M. (2011). Volatility spillovers from the Chinese stock market to economic neighbors. Mathematics and Computers in Simulation, 94, 238-257.
Ardia, D. (2008). Bayesian estimation of the GARCH (1,1) model with normal innovations. Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications, 17-37.
Asai, M. (2016). Bayesian Analysis of General Asymmetric Multivariate GARCH Models and News Impact Curves. Journal of the Japan Statistical Society, 45(2), 129-144.
Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 74(1), 3-30.
Bala, D. A., & Takimoto, T. (2017). Stock market's volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach. Borsa Istanbul Review, 17(1), 25-48.
Bauwens, L., Hafner, C. M., & Pierret, D. (2013). Multivariate volatility modeling of electricity futures. Journal of Applied Econometrics, 28(5), 743-761.
Bauwens, L., & Laurent, S. (2005). A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models. Journal of Business & Economic Statistics, 23(3), 346-354.
Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of applied econometrics, 21(1), 79-109.
Bekaert, G., Harvey, C.R., & Ng, A. (2005). Market integration and contagion. The Journal of Business, 78, 39-69.
BenSaïda, A. (2018). The contagion effect in European sovereign debt markets: A regime-switching vine copula approach. International Review of Financial Analysis, 58, 153-165.
Berben, R. P., & Jansen, W. J. (2005). Comovement in international equity markets: A sectoral view. Journal of International Money and Finance, 24(5), 832-857.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. The review of economics and statistics, 72(3), 498-505.
Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. Journal of political Economy, 96(1), 116-131.
Bonga-Bonga, L. (2018). Uncovering equity market contagion among BRICS countries: an application of the multivariate GARCH model. The Quarterly Review of Economics and Finance, 67, 36-44.
De Grauwe, P. (2012). Lectures on behavioral macroeconomics. Princeton University Press.
Doan, T. A. (2013). RATS handbook for ARCH/GARCH and volatility models, June, 2013.
Dornbusch, R., Park, Y. C., & Claessens, S. (2000). Contagion: understanding how it spreads. The World Bank Research Observer, 15(2), 177-197.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
Engle, R. (2004). Risk and volatility: Econometric models and financial practice. American economic review, 94(3), 405-420.
Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(1), 122-150.
Engle, R. F. (2011). Long-term skewness and systemic risk. Journal of Financial Econometrics, 9(3), 437-468.
Fernández, C., & Steel, M. F. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371.
Fleming, J., Kirby, C., & Ostdiek, B. (1998). Information and volatility linkages in the stock, bond, and money markets1. Journal of financial economics, 49(1), 111-137.
Forbes, K., & Rigobon, R. (2001). Measuring contagion: conceptual and empirical issues. In International financial contagion (pp. 43-66). Springer, Boston, MA.
Frank, N., & Hesse, H. (2009). Financial spillovers to emerging markets during the global financial crisis (No. 9-104). International Monetary Fund.
Gómez, E., Gomez-Viilegas, M. A., & Marin, J. M. (1998). A multivariate generalization of the power exponential family of distributions. Communications in Statistics-Theory and Methods, 27(3), 589-600.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
He, C., Silvennoinen, A., & Teräsvirta, T. (2008). Parameterizing unconditional skewness in models for financial time series. Journal of Financial Econometrics, 6(2), 208-230.
Heidari, H., Molabahrami, A. (2012). Portfolio Optimization Using Multivariate GARCH Models: Evidence from Tehran Stock Exchange. Financial Research Journal, 12(30), 35-56. (in Persian)
Hein, L. U. (2015). Investigating Correlation and Volatility Transmission among Equity, Gold, Oil and Foreign Exchange. MaRBLe, 2.
Hong, H., & Stein, J. C. (2003). Differences of opinion, short-sales constraints, and market crashes. The Review of Financial Studies, 16(2), 487-525.
Karami, S., Rastegar, M. (2018). Estimation of Return and Volatilities Spillover between Different Industries of Tehran Stocks’ Exchange. Financial Engineering and Portfolio Management , 9(35), 323-342. (in Persian)
Khalifa, A. A., Hammoudeh, S., & Otranto, E. (2014). Patterns of volatility transmissions within regime switching across GCC and global markets. International Review of Economics & Finance, 29, 512-524.
Kroner, K. F., & Ng, V. K. (1998). Modeling asymmetric comovements of asset returns. The review of financial studies, 11(4), 817-844.
Laurent, S., Boudt, K., & Danielsson, J. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244-257.
Lin, W. L., Engle, R. F., & Ito, T. (1994). Do bulls and bears move across borders? International transmission of stock returns and volatility. Review of Financial Studies, 7(3), 507-538.
Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric theory, 19(2), 280-310.
Massacci, D. (2014). A two-regime threshold model with conditional skewed Student t distributions for stock returns. Economic Modelling, 43, 9-20.
Masson, M. P. R. (1998). Contagion: Monsoonal effects, spillovers, and jumps between multiple equilibria (No. 98-142). International Monetary Fund.
McAleer, M., Hoti, S., & Chan, F. (2009). Structure and asymptotic theory for multivariate asymmetric conditional volatility. Econometric Reviews, 28(5), 422-440.
Nasrollahi, Z., Tyebi, R., Fotovat, A., & Eskandaripour, Z . (2018). Transmission of Volatility between Stock Markets of Iran, India and Turkey Using BEKK-GARCH Model. Financial Monetary Economics, 25(15), 77-92. (in Persian)
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
Rostami, Mohammad Reza, & Haqiqi, Fatemeh. (2013). Using MGARCH to Estimate Value at Risk. Financial Research Journal, 15 (2), 215-228. (in Pesrsian)
Salisu, A. A. (2016). Modelling oil price volatility with the Beta-Skew-t-EGARCH framework. Economics Bulletin, 36(3), 1315-1324.
Shahyaki Tash, M., Mirbagherijam, M. (2015). Survey the asymmetric correlation between stock return, trading volume and volatility of Tehran stock exchange market (DCC-GARCH Approach). Journal of Economic Research (Tahghighat- E- Eghtesadi), 50(2), 359-387. (in Persian)
Stavroyiannis, S. (2017). Is the BRICS decoupling effect reversing? Evidence from dynamic models. International Journal of Economics and Business Research, 13(3), 303-315.
Tsutsui, Y. (2002). The interdependence and cause of Japanese and US stock prices: an event study. Asian Economic Journal, 16(2), 97-109.
Tse, Y. K., & Tsui, A. K. C. (2002). A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business & Economic Statistics, 20(3), 351-362.
Virbickaitė, A., Ausín, M. C., & Galeano, P. (2016). A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection. Computational Statistics & Data Analysis, 100, 814-829.