Cristina Amado is currently an Assistant Professor in the Department of Economics at the University of Minho, Portugal, and an international research fellow at CREATES, Aarhus University. She holds a PhD in Economic Statistics from the Stockholm School of Economics with a thesis entitled "Four essays on the econometric modelling of volatility and durations" since 2009. She is also a research member at the Economic Policies Research Centre (NIPE). Her main research interests lie within the fields of time-series analysis, nonlinear modelling and mathematical statistics.
Time-series analysis
nonlinear modelling and mathematical statistics.
Selected publications
Campos-Martins, S., & Amado, C. (2022). Financial market linkages and the sovereign debt crisis. Journal of International Money and Finance, 123. DOI
Amado, C., & Teräsvirta, T. (2017). Specification and Testing of Multiplicative Time-Varying GARCH Models with Applications. Econometric Reviews, 36(4), 421–446. DOI
Amado, C., & Teräsvirta, T. (2014). Conditional correlation models of autoregressive conditional heteroscedasticity with nonstationary GARCH equations. Journal of Business and Economic Statistics, 32(1), 69–87. DOI
Amado, C., & Teräsvirta, T. (2014). Modelling changes in the unconditional variance of long stock return series. Journal of Empirical Finance, 25, 15–35. DOI
Amado, C., & Teräsvirta, T. (2013). Modelling volatility by variance decomposition. Journal of Econometrics, 175(2), 142–153. DOI
Other publications
Campos-Martins, S., & Amado, C. (2022). Financial market linkages and the sovereign debt crisis. Journal of International Money and Finance, 123. DOI
Amado, C., Silvennoinen, A., & Teräsvirta, T. (2019). Models with Multiplicative Decomposition of Conditional Variances and Correlations. In J. Chevallier, S. Goutte, D. Guerreiro, S. Saglio, & B. Sanhaji (Eds.), Financial Mathematics, Volatility and Covariance Modelling (1st ed., Vol. 2). Routledge. DOI
Amado, C., & Teräsvirta, T. (2017). Specification and Testing of Multiplicative Time-Varying GARCH Models with Applications. Econometric Reviews, 36(4), 421–446. DOI
Amado, C., Silvennoinen, A., & Teräsvirta, T. (2017). Modelling and Forecasting WIG20 Daily Returns. Central European Journal of Economic Modelling and Econometrics, 9, 173–200. DOI
Amado, C., & Laakkonen, H. (2014). Modelling Time-Varying Volatility in Financial Returns: Evidence from the Bond Markets. In N. Haldrup, M. Meitz, & P. Saikkonen (Eds.), Essays in Nonlinear Time Series Econometrics (pp. 139–160). Oxford University Press. DOI
Amado, C., & Teräsvirta, T. (2014). Conditional correlation models of autoregressive conditional heteroscedasticity with nonstationary GARCH equations. Journal of Business and Economic Statistics, 32(1), 69–87. DOI
Amado, C., & Teräsvirta, T. (2014). Modelling changes in the unconditional variance of long stock return series. Journal of Empirical Finance, 25, 15–35. DOI
Amado, C., & Teräsvirta, T. (2013). Modelling volatility by variance decomposition. Journal of Econometrics, 175(2), 142–153. DOI
Data Analysis and Transferable Skills
Master
