sample of publications

• articles 

  • Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models.  ECONOMICS LETTERS.  230. 2023
  • Direct versus iterated multiperiod Value-at-Risk forecasts.  JOURNAL OF ECONOMIC SURVEYS.  37:915-949. 2023
  • Dynamic factor models: Does the specification matter?.  Series-Journal of the Spanish Economic Association.  13:397-428. 2022
  • Accurate Confidence Regions for Principal Components Factors.  OXFORD BULLETIN OF ECONOMICS AND STATISTICS.  83:1432-1453. 2021
  • 30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial.  INTERNATIONAL JOURNAL OF FORECASTING.  37:1333-1337. 2021
  • Factor extraction using Kalman filter and smoothing: This is not just another survey.  INTERNATIONAL JOURNAL OF FORECASTING.  37:1399-1425. 2021
  • Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection.  JOURNAL OF BANKING & FINANCE.  118:1-13. 2020
  • Prediction regions for interval¿valued time series.  JOURNAL OF APPLIED ECONOMETRICS.  35:373-390. 2020
  • A bootstrap approach for generalized Autocontour testing Implications for VIX forecast densities.  Econometric Reviews.  39:971-990. 2020
  • Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation.  Econometrics and Statistics.  13:84-105. 2020
  • Estimating non-stationary common factors : implications for risk sharing.  Computational Economics.  55:37-60. 2020
  • Growth in stress.  INTERNATIONAL JOURNAL OF FORECASTING.  35:948-966. 2019
  • Uncertainty and density forecasts of ARMA models: comparison of asymptotic, bayesian and bootstrap procedures.  JOURNAL OF ECONOMIC SURVEYS.  32:388-419. 2018
  • MGARCH models: Trade-off between feasibility and flexibility.  INTERNATIONAL JOURNAL OF FORECASTING.  34:45-63. 2018
  • Robust bootstrap densities for dynamic conditional correlations: implications for portfolio selection and Value-at-Risk.  JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION.  88:1976-2000. 2018
  • Robust bootstrap forecast densities for GARCH returns and volatilities.  JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION.  87:3152-3174. 2017
  • Threshold stochastic volatility: Properties and forecasting.  INTERNATIONAL JOURNAL OF FORECASTING.  33:1105-1123. 2017
  • Determining the number of factors after stationary univariate transformations.  Empirical Economics.  53:351-372. 2017
  • The uncertainty of conditional returns, volatilities and correlations in DCC models.  COMPUTATIONAL STATISTICS & DATA ANALYSIS.  100:170-185. 2016
  • Frontiers in VaR forecasting and backtesting.  INTERNATIONAL JOURNAL OF FORECASTING.  32:475-501. 2016
  • Identification of asymmetric conditional heteroscedasticity in the presence of outliers.  Series-Journal of the Spanish Economic Association.  7:179-201. 2016
  • Bootstrap multi-step forecasts of non-Gaussian VAR models.  INTERNATIONAL JOURNAL OF FORECASTING.  31:834-848. 2015
  • Comparing univariate and multivariate models to forecast portfolio Value-at-Risk.  Journal of Financial Econometrics.  11:400-441. 2013
  • Statistical signal extraction and filtering.  COMPUTATIONAL STATISTICS & DATA ANALYSIS.  58:1-3. 2013
  • Introduction to flash indicators.  INTERNATIONAL JOURNAL OF FORECASTING.  29:642-643. 2013
  • Revisiting several popular GARCH models with leverage effect: differences and similarities.  Journal of Financial Econometrics.  10:637-668. 2012
  • Maximally Autocorrelated Power Transformations: A Closer Look at the Properties of Stochastic Volatility Models.  STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS.  16. 2012
  • Can we evaluate the predictability of financial markets?.  INTERNATIONAL JOURNAL OF FORECASTING.  28:1-2. 2012
  • Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters.  COMPUTATIONAL STATISTICS & DATA ANALYSIS.  56:62-74. 2012
  • Estimating GARCH volatility in the presence of outliers.  ECONOMICS LETTERS.  114:86-90. 2012
  • Prediction intervals in conditionally heteroscedastic time series with stochastic components.  INTERNATIONAL JOURNAL OF FORECASTING.  27:308-319. 2011
  • Estimando relaciones entre variables económicas (utilizando integrales, límites, inversión de matrices, maximización numérica y derivadas).  Matematicalia.  7:1-8. 2011
  • Interview with Professor Andrew Harvey.  Boletín inflación y análisis macroeconómico.  200:42-46. 2011
  • Conditionally Heterocedastic Unobserved Component Models and their Reduced Form.  ECONOMICS LETTERS.  107:88-90. 2010
  • El efecto de la crisis sobre la volatilidad y el riesgo del IBEX35.  Boletín inflación y análisis macroeconómico.  79-87. 2010
  • A Note on the Properties of Power-Transformed Returns in Long-Memory Stochastic Volatility Models with Leverage Effect.  COMPUTATIONAL STATISTICS & DATA ANALYSIS.  53:3593-3600. 2009
  • Testing for Conditional Heteroscedasticity in the Components of Inflation.  STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS.  13. 2009
  • Bootstrap Prediction Intervals in State-Space Models.  JOURNAL OF TIME SERIES ANALYSIS.  30:167-178. 2009
  • Modelling Long-Memory Volatilities with Leverage Effect: A-LMSV Versus FIEGARCH.  COMPUTATIONAL STATISTICS & DATA ANALYSIS.  52:2846-2862. 2008

• book chapters 

  • Presentación. In: Análisis econométrico y big data.  FUNDACIÓN DE LOS BANCOS Y CAJAS DE CECA (FUNCAS). 2021
  • Predicción de series temporales basada en Machine Learning: aplicaciones económicas y financieras. In: Nuevo métodos de predicción económica con datos masivos.  FUNDACIÓN DE LOS BANCOS Y CAJAS DE CECA (FUNCAS). 189-214. 2021
  • Presentación. In: Nuevo métodos de predicción económica con datos masivos.  FUNDACIÓN DE LOS BANCOS Y CAJAS DE CECA (FUNCAS). 1-3. 2021
  • Small- versus big-data factor extraction in dynamic factor models: an empirical assessment. In: Dynamic factor models.  EMERALD GROUP PUBLISHING LIMITED. 2016
  • More is not always better: Kalman filtering in dynamic factor models. In: Unobserved components and time series econometrics.  Oxford University Press (Ed.). 2015

• conference contributions 

  • Factor extraction using Kalman filter and smoothing 2020
  • Predicción de series temporales basada en Machine Learning: aplicaciones económicas y financieras 2020
  • Prediction regions for interval-valued time series.  97-98. 2018
  • Estimating non-stationary common factors: implications for risk sharing 2017
  • Resampling uncertainty of Principal Components factors 2017
  • A bootstrap approach for Generalized autocontour testing 2016
  • A bootstrap approach for generalized autocontour testing.  99. 2016
  • Asymmetric stochastic volatility models: properties and estimation 2016
  • Determining the number of factors after stationary univariate transformations.  99. 2016
  • MGARCH models: trade-off between feasibility and flexibility.  92. 2016
  • Measuring the uncertainty of principal components in dynamic factor models 2016
  • Dimensionality: curse or blessing? : an empirical assessment when estimating factors in DFM 2014
  • Dimensionality: curse or blessing? An empirical assessment when estimating factors in DFM 2014
  • One for all: nesting asymmetric stochastic volatility models 2014
  • One for all: nesting asymmetric stochastic volatility models 2014
  • Dynamic factor models 2013
  • Nesting asymmetric stochastic volatility models 2013
  • The uncertainty of conditional correlations in DCC models 2013
  • Bootstrapping Prediction Intervals 2012
  • On the Issue of How Many Variables to Use When Estimating Common Factors using the Kalman Fiter 2012
  • On the Issue of How Many Variables to Use when Estimating Common Factors Using the Kalman Filter 2012
  • On the Issue of how many Variables to use when Estimating Common Factors using the Kalman Filter 2012
  • Bootstrap Forecast of Multivariate VAR Models 2011
  • On the Issue of How Many Variables to Use When Estimating Common Factors Using the Kalman Filter 2011
  • Modelling intra-daily volatility by functional data analysis: an empirical application to the Spanish stock market 2009
  • ARIMA-GARCH and Unobserved Component Models with GARCH Disturbances: Are their Prediction Intervals Different? 2008
  • Bootstrap Forecast in Conditionally Heteroscedastic Unobserved Component Models 2008
  • The Relationship between ARIMA-GARCH and Unobserved Component Models with GARCH Disturbances 2008

• working papers 

  • International vulnerability of inflation 2024
  • Extreme temperatures and the profitability of large European firms 2024
  • Economic activity and C02 emissions in Spain 2023
  • Effects of extreme temperature on the European equity market 2023
  • Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula 2023
  • Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models 2022
  • Economic activity and climate change 2022
  • Dynamic factor models: does the specification matter? 2021
  • Expecting the unexpected: economic growth under stress 2021
  • Factor extraction using Kalman filter and smoothing: this is not just another survey 2020
  • Direct versus iterated multi-period Value at Risk 2020
  • A comment on the dynamic factor model with dynamic factors 2020
  • Comparing Forecasts of Extremely Large Conditional Covariance Matrices 2019
  • Prediction regions for interval-valued time series 2019
  • Growth in Stress 2018
  • Estimating non-stationary common factors : Implications for risk sharing 2017
  • Measuring the uncertainty of Principal Components in Dynamic Factor Models 2016
  • A Bootstrap Approach for Generalized Autocontour Testing 2016
  • Determining the number of factors after stationary univariate transformations 2016
  • Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk 2015
  • MGARCH models: tradeoff between feasibility and flexibility 2015
  • Model uncertainty and the forecast accuracy of ARMA models: A survey 2015
  • Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment 2015
  • Score driven asymmetric stochastic volatility models 2014
  • Identification of asymmetric conditional heteroscedasticity in the presence of outliers 2014
  • The uncertainty of conditional returns, volatilities and correlations in DCC models 2014
  • One for all: nesting asymmetric stochastic volatility models 2013
  • More is not always better : back to the Kalman filter in dynamic factor models 2012
  • Bootstrap forecast of multivariate VAR models without using the backward representation 2011
  • Comparing Sample and Plug-in Moments in Asymmetric Garch Models 2010
  • Bootstrap Prediction Intervals for VaR and ES in the Context of GARCH Models 2010
  • Optimal Portfolios with Minimum Capital Requirements 2010
  • Bootstrap Prediction Mean Squared Errors of Unobserved States Based on the Kalman Filter with Estimated Parameters 2010
  • Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk 2009
  • Modelling Intra-Daily Volatility by Functional Data Analysis: An Empirical Application to the Spanish Stock Market 2009
  • Measuring Financial Risk: Comparison of Alternative Procedures to Estimate VaR and ES 2008
  • Bootstrap Prediction Intervals in State Space Models 2008
  • Estimating and Forecasting GARCH Volatility in the Presence of Outliers 2008