Directional dependence via Gaussian copula beta regression model with asymmetric GARCH marginals

References

• Almeida, C., Czado, C. (2012). Efficient Bayesian inference for stochastic time-varying copula models. Computational Statistics & Data Analysis 56(6):1511–1527.
   Google Scholar
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307–328.
   Google Scholar
• Casarin, R., Dalla Valle, L., Leisen, F. (2012). Bayesian Model Selection for Beta Autoregressive Processes. Bayesian Analysis 7(2):385–410.
   Google Scholar
• Casarin, R., Leisen, F., Molina, G., ter Horst, E. (2015). A Bayesian Beta Markov Random Field Calibration of the Term Structure of Implied Risk Neutral Densities. Bayesian Analysis 10(4):791–819.
   Google Scholar
• Chiou, J.-S., Lee, Y.-H., Lin, C.-M. (2008). Existence of a Long-Run Equilibrium between the S&P 500 and Oil Prices. International Research Journal of Finance and Economics 21:68–75.
   Google Scholar
• Choi, M. S., Park, J. A., Hwang, S. Y. (2012). Asymmetric GARCH processes featuring both threshold effect and bilinear structure. Statistics and Probability Letters 82:419–426.
   Google Scholar
• Cherubini, U., Mulinacci, S., Gobbi, F., Romagnoli, S. (2011). Dynamic Copula Methods in Finance. 1st ed. New York: Wiley.
   Google Scholar
• Cribari-Neto, F., Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software 34(2):1–24.
   Google Scholar
• Durante, F. (2009). Construction of non-exchangeable bivariate distribution functions. Statistics Papers 50(2):383–391.
   Google Scholar
• Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007.
   Google Scholar
• Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business and Economic Statistics 20:339–350.
   Google Scholar
• Engle, R. F., Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance 48(5):1749–1778.
   Google Scholar
• Ferrari, S. L. P., Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics 31:799–815.
   Google Scholar
• Figueroa-Zuniga, J. I., Arellano-Valle, R. B., Ferrari, S. L. P. (2013). Mixed beta regression: A Bayesian perspective. Computational Statistics & Data Analysis 61:137–147.
   Google Scholar
• Fornari, F., Mele, A. (1997). Sign- and volatility-switching ARCH models: Theory and applications to International Stock Markets. Journal of Applied Econometrics 12(1):49–65.
   Google Scholar
• Forbes, K. J., Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. Journal of Finance 57(5):2223–2261.
   Google Scholar
• 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. Journal of Finance 48:1779–1801.
   Google Scholar
• Gokmenoglu, K. K., Fazlollahi, N. (2015). The Interactions among Gold, Oil, and Stock Market: Evidence from S&P500. Procedia Economics and Finance 25:478–488.
   Google Scholar
• Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438.
   Google Scholar
• Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control 2:329–352.
   Google Scholar
• Guolo, A., Varin, C. (2014). Beta regression for time series analysis of bounded data, with application to Canada Google flu trends. The Annals of Applied Statistics 8(1):74–88.
   Google Scholar
• Hu, M., Liang, H. (2014). A copula approach to assessing Granger causality. NeuroImage 100:125–134.
   Google Scholar
• Jondeau, E., Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock market application. Journal of International Money and Finance 25(5):827–853.
   Google Scholar
• Jung, Y, Kim, J.-M., Kim, J. (2008). New approach of directional dependence in exchange markets using generalized FGM copula functions. Communications in Statistics: Simulation and Computation 37(4):772–788.
   Google Scholar
• Kim, J.-M., Jung, Y., Soderberg, T. (2009). Directional dependence of genes using survival truncated FGM type modification copulas. Communications in Statistics: Simulation and Computation 38(7):1470–1484.
   Google Scholar
• Kim, J.-M., Jung, Y., Sungur, E. A., Han, K., Park, C., Sohn, I. (2008). A copula method for modeling directional dependence of genes. BMC Bioinformatics 9:225.
   Google Scholar
• Kim, J.-.M., Jung, Y., Sungur, E. A. (2014). Copulas with directional dependence property: Application to foreign exchange currency data. Models Assisted Statistics and Applications 9:309–324.
   Google Scholar
• Kim, D., Kim, J.-M. (2014). Analysis of Directional Dependence using Asymmetric Copula-based Regression Models. Journal of Statistical Computation and Simulation 84(9):1990–2010.
   Google Scholar
• Kojadinovic, I., Yan, J. (2010). Modeling multivariate distributions with continuous margins using the copula R package. Journal of Statistical Software 34(9):1–20.
   Google Scholar
• Lee, Y.-H., Hao, F. (2012). Oil and S&P 500 markets: Evidence from the nonlinear model. International Journal of Economics and Financial Issues 2(3):272–280.
   Google Scholar
• Lee, T.-H., Yang, W. (2014). Granger-causality in quantiles between financial markets: Using copula approach. International Review of Financial Analysis 33:70–78.
   Google Scholar
• Masarotto, G., Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6:1517–1549.
   Google Scholar
• Nelsen, R. (2006). An Introduction to Copulas. 2nd ed. New York: Springer.
   Google Scholar
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59:347–370.
   Google Scholar
• Ospina, R., Ferrari, S. L. P. (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis 56:1609–1623.
   Google Scholar
• Paolino, P. (2001). Maximum likelihood estimation of models with beta-distributed dependent variables. Political Analysis 9:325–346.
   Google Scholar
• Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International Economic Review 47(2):527–556.
   Google Scholar
• Pitt, M., Chan, D., Kohn, R. (2006). Efficient Bayesian inference for Gaussian copula regression models. Biometrika 93:537–554.
   Google Scholar
• Rabemananjara, R., Zakoian, J. M. (1993). Threshold arch models and asymmetries in volatility. Journal of Applied Econometrics 8(1):31–49.
   Google Scholar
• Rodríguez-Lallena, J. A., Úbeda-Flores, M. (2004). A new class of bivariate copulas. Statistics & Probability Letters 66:315–325.
   Google Scholar
• Schmid, M., Wickler, F., Maloney, K. O., Mitchell, R., Fenske, N., Mayr, A. (2013). Boosted beta regression. PLOS ONE 8(4):e61623.
   Google Scholar
• Simas, A. B., Barreto-Souza, W., Rocha, A. V. (2010). Improved estimators for a general class of beta regression models. Computational Statistics & Data Analysis 54(2):348–366.
   Google Scholar
• Sklar, A. (1959). Fonctions de repartition á n dimensions et leurs marges. Publications de l’Institut de statistique de l’Universit de Paris 8:229–231.
   Google Scholar
• So, M. K. P. So, Yeung, C. Y. T. (2014). Vine-copula GARCH model with dynamic conditional dependence. Computational Statistics & Data Analysis 76:655–671.
   Google Scholar
• Song, P. X.-K. (2000). Multivariate dispersion models generated from Gaussian copula. Scandinavian Journal of Statistics 27:305–320.
   Google Scholar
• Song, P. X.-K., Li, M., Yuan, Y. (2009). Joint regression analysis of correlated data using Gaussian copulas. Biometrics 65:60–68.
   Google Scholar
• Stoeber, J., Czado, C. (2012). Detecting regime switches in the dependence structure of high dimensional financial data. arXiv:1202.2009.
   Google Scholar
• Sungur, E. A. (2005). A note on directional dependence in regression setting. Communications in Statistics: Theory and Methods 34:1957–1965.
   Google Scholar
• Uhm, D., Kim, J.-M., Jung, Y. (2012). Large asymmetry and directional dependence by using copula modeling to currency exchange rates. Models Assisted Statistics and Applications 7(4):327–340.
   Google Scholar
• Wiener, N. (1956). The Theory of Prediction. In Modern Mathematics for Engineers, vol. 1. New York: McGraw-Hill.
   Google Scholar