Luiz K. Hotta

Personal Details

First Name:	Luiz
Middle Name:	K.
Last Name:	Hotta
Suffix:
RePEc Short-ID:	pho297
	[This author has chosen not to make the email address public]
Terminal Degree:	1983 Economics Department; London School of Economics (LSE) (from RePEc Genealogy)

Affiliation

Universidade Estadual de Campinas-Departamento de Estatística

http://www.ime.unicamp.br/de.html
Brazil, Campinas

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
   • Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.

   Cited by:

   1. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
   2. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
   3. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.

2. Marc Hallin & Luis K. Hotta & João H. G Mazzeu & Carlos Cesar Trucios-Maza & Pedro L. Valls Pereira & Mauricio Zevallos, 2019. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach," Working Papers ECARES 2019-14, ULB -- Universite Libre de Bruxelles.
   • Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
   • Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

   Cited by:

   1. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
   2. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
      • Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
   3. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.

3. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
   • Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.

   Cited by:

   1. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
   2. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
      • Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
      • Marc Hallin & Luis K. Hotta & João H. G Mazzeu & Carlos Cesar Trucios-Maza & Pedro L. Valls Pereira & Mauricio Zevallos, 2019. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach," Working Papers ECARES 2019-14, ULB -- Universite Libre de Bruxelles.
   3. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
      • Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
   4. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Working Papers ECARES 2023-15, ULB -- Universite Libre de Bruxelles.
      • Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
   5. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
   6. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
   7. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.

4. Almeida, Daniel de & Hotta, Luiz & Ruiz Ortega, Esther, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de Estadística.
   • de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.

   Cited by:

   1. Ariana Paola Cortés Ángel & Mustafa Hakan Eratalay, 2022. "Deep diving into the S&P Europe 350 index network and its reaction to COVID-19," Journal of Computational Social Science, Springer, vol. 5(2), pages 1343-1408, November.
   2. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
   3. Karanasos, Menelaos & Xu, Yongdeng & Yfanti, Stavroula, 2017. "Constrained QML Estimation for Multivariate Asymmetric MEM with Spillovers: The Practicality of Matrix Inequalities," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
   4. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
   5. Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.
      • Cristina Amado & Annastiina Silvennoinen & Timo Ter¨asvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," NIPE Working Papers 07/2018, NIPE - Universidade do Minho.
   6. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
      • Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
      • Marc Hallin & Luis K. Hotta & João H. G Mazzeu & Carlos Cesar Trucios-Maza & Pedro L. Valls Pereira & Mauricio Zevallos, 2019. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach," Working Papers ECARES 2019-14, ULB -- Universite Libre de Bruxelles.
   7. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: an estimation strategy based on forecasting-driven model selection," Working Papers hal-03224910, HAL.
   8. Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
   9. Jingwei Pan, 0000. "Evaluating Correlation Forecasts Under Asymmetric Loss," Proceedings of Economics and Finance Conferences 11413234, International Institute of Social and Economic Sciences.
   10. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
   11. Ángeles Cebrián-Hernández & Enrique Jiménez-Rodríguez, 2021. "Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
   12. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
   13. Moura, Guilherme V. & Santos, André A. P. & Ruiz Ortega, Esther, 2019. "Comparing Forecasts of Extremely Large Conditional Covariance Matrices," DES - Working Papers. Statistics and Econometrics. WS 29291, Universidad Carlos III de Madrid. Departamento de Estadística.
   14. Văn, Lê & Bảo, Nguyễn Khắc Quốc, 2022. "The relationship between global stock and precious metals under Covid-19 and happiness perspectives," Resources Policy, Elsevier, vol. 77(C).
   15. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
   16. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
   17. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," SEEDS Working Papers 0120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2020.
   18. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
   19. Ana Alzate-Ortega & Natalia Garzón & Jesús Molina-Muñoz, 2024. "Volatility Spillovers in Emerging Markets: Oil Shocks, Energy, Stocks, and Gold," Energies, MDPI, vol. 17(2), pages 1-19, January.
   20. Shimada, Junji & Tsukuda, Yoshihiko & Miyakoshi, Tatsuyoshi, 2021. "Who is the center of local currency Asian government bond markets?," Japan and the World Economy, Elsevier, vol. 59(C).
   21. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
   22. Vogler, Jan & Golosnoy, Vasyl, 2023. "Unrestricted maximum likelihood estimation of multivariate realized volatility models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1063-1074.
   23. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: An estimation strategy based on forecasting-driven model selection," SEEDS Working Papers 0621, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2021.
   24. Ariana Paola Cortés à ngel & Mustafa Hakan Eratalay, 2021. "Deedp Diving Into The S&P 350 Europe Index Network Ans Its Reaction To Covid-19," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 134, Faculty of Economics and Business Administration, University of Tartu (Estonia).

5. Hotta, Luiz & Trucíos, Carlos & Ruiz Ortega, Esther, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.

   Cited by:

   1. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
   2. Trucíos, Carlos & Hotta, Luiz K., 2016. "Bootstrap prediction in univariate volatility models with leverage effect," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 120(C), pages 91-103.
   3. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
      • Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.

6. Márcio Laurini & Luiz Koodi Hotta, 2011. "Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations," IBMEC RJ Economics Discussion Papers 2011-01, Economics Research Group, IBMEC Business School - Rio de Janeiro.
   • MÁrcio Poletti Laurini & Luiz Koodi Hotta, 2014. "Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 214-230, April.

   Cited by:

   1. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
   2. Márcio Poletti Laurini & Armênio Westin Neto, 2014. "Arbitrage in the Term Structure of Interest Rates: a Bayesian Approach," International Econometric Review (IER), Econometric Research Association, vol. 6(2), pages 77-99, September.
   3. Márcio Poletti Laurini, 2017. "A continuous spatio-temporal model for house prices in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 235-269, January.

7. Márcio Laurini & Luiz Hotta, 2009. "Modelos de fatores latentes generalizados para curvas de juros em múltiplos mercados," Working Papers 09_03, Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto.
   • Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2009. "Modelos de Fatores Latentes Generalizados para Curvas de Juros em Múltiplos Mercados," Insper Working Papers wpe_161, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
   • Laurini, Márcio Poletti L. & Hotta, Luiz K., 2009. "Modelos de Fatores Latentes Generalizados para Curvas de Juros em Múltiplos Mercados," Insper Working Papers wpe_169, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

   Cited by:

   1. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.

8. Laurini, Márcio P. & Hotta, Luiz K., 2008. "Bayesian extensions to diebold-li term structure model," Insper Working Papers wpe_122, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
   • Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2010. "Bayesian extensions to Diebold-Li term structure model," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 342-350, December.

   Cited by:

   1. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
   2. Márcio Laurini, 2012. "Dynamic Functional Data Analysis with Nonparametric State Space Models," IBMEC RJ Economics Discussion Papers 2012-01, Economics Research Group, IBMEC Business School - Rio de Janeiro.
      • M�rcio Poletti Laurini, 2014. "Dynamic functional data analysis with non-parametric state space models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 142-163, January.
   3. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
   4. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
   5. Dang-Nguyen, Stéphane & Le Caillec, Jean-Marc & Hillion, Alain, 2014. "The deterministic shift extension and the affine dynamic Nelson–Siegel model," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 402-417.
   6. Victor A. Lapshin & Vadim Ya. Kaushanskiy, 2014. "A Nonparametric Method For Term Structure Fitting With Automatic Smoothing," HSE Working papers WP BRP 39/FE/2014, National Research University Higher School of Economics.
      • Vadim Kaushanskiy & Victor Lapshin, 2016. "A nonparametric method for term structure fitting with automatic smoothing," Applied Economics, Taylor & Francis Journals, vol. 48(58), pages 5654-5666, December.
   7. Márcio Poletti Laurini & Armênio Westin Neto, 2014. "Arbitrage in the Term Structure of Interest Rates: a Bayesian Approach," International Econometric Review (IER), Econometric Research Association, vol. 6(2), pages 77-99, September.
   8. Tunaru, Diana, 2017. "Gaussian estimation and forecasting of the U.K. yield curve with multi-factor continuous-time models," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 119-129.
   9. Daniel R. Kowal & Antonio Canale, 2021. "Semiparametric Functional Factor Models with Bayesian Rank Selection," Papers 2108.02151, arXiv.org, revised May 2022.
   10. Caldeira, João F. & Laurini, Márcio P. & Portugal, Marcelo S., 2010. "Bayesian Inference Applied to Dynamic Nelson-Siegel Model with Stochastic Volatility," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.
   11. Aryo Sasongko & Cynthia Afriani Utama & Buddi Wibowo & Zaäfri Ananto Husodo, 2019. "Modifying Hybrid Optimisation Algorithms to Construct Spot Term Structure of Interest Rates and Proposing a Standardised Assessment," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 957-1003, October.
   12. Sourish Das, 2018. "Modeling Nelson-Siegel Yield Curve using Bayesian Approach," Papers 1809.06077, arXiv.org, revised Oct 2018.
   13. Tunaru, Radu & Zheng, Teng, 2017. "Parameter estimation risk in asset pricing and risk management: A Bayesian approach," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 80-93.

9. Laurini, Márcio P. & Hotta, Luiz K., 2007. "Extensões Bayesianas do Modelo de Estrutura a Termo de Diebold-Li," Insper Working Papers wpe_88, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

   Cited by:

   1. Almeida, Caio & Gomes, Romeu & Leite, André & Vicente, José, 2008. "Movimentos da Estrutura a Termo e Critérios de Minimização do Erro de Previsão em um Modelo Paramétrico Exponencial," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 62(4), December.
      • Caio Almeida & Romeu Gomes & André Leite & José Vicente, 2007. "Movimentos da Estrutura a Termo e Critérios de Minimização do Erro de Previsão em um Modelo Paramétrico Exponencial," Working Papers Series 146, Central Bank of Brazil, Research Department.

10. Pedro L. Valls Pereira & Hotta, L.K. & Souza, L.A.R., 1999. "Alternative Models to extract asset volatility: a comparative study," Finance Lab Working Papers flwp_14, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    • Pereira, Pedro L. Valls & Hotta, Luiz K. & Souza, Luiz Alvares R. de & Almeida, Nuno Miguel C. G. de, 1999. "Alternative Models To Extract Asset Volatility: A Comparative Study," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 19(1), May.

    Cited by:

    1. Marçal, Emerson Fernandes & Pereira, Pedro L. Valls, 2008. "Testing the Hypothesis of Contagion Using Multivariate Volatility Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 28(2), November.
       • Pereira, Pedro L. Valls, 2009. "Testing the hypothesis of contagion using multivariate volatility models," Textos para discussão 174, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
       • Marçal, Emerson F. & Valls Pereira, Pedro L., 2008. "Testing the Hypothesis of Contagion using Multivariate Volatility Models," MPRA Paper 15623, University Library of Munich, Germany.
    2. José Fajardo & Aquiles Farias, 2002. "Generalized Hyperbolic Distributions and Brazilian Data," Working Papers Series 52, Central Bank of Brazil, Research Department.
       • Fajardo, José & Farias, Aquiles, 2004. "Generalized Hyperbolic Distributions and Brazilian Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 24(2), November.
       • Fajardo, J. & Farias, A., 2003. "Generalized Hyperbolic Distributions and Brazilian Data," Finance Lab Working Papers flwp_57, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    3. Fajardo, J. & Cajueiro, D. O., 2003. "Volatility Estimation and Option Pricing with Fractional Brownian Motion," Finance Lab Working Papers flwp_53, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    4. Marçal, Emerson F. & Valls Pereira, Pedro L., 2008. "Testando A Hipótese De Contágio A Partir De Modelos Multivariados De Volatilidade [Testing the contagion hypotheses using multivariate volatility models]," MPRA Paper 10356, University Library of Munich, Germany.
    5. Douglas Gomes dos Santos & Flávio Augusto Ziegelmann, 2008. "Estimação de volatilidade em períodos de crise: Modelos aditivos semi-paramétricos versus modelos versus modelo Garch," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807201932370, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    6. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Fear of disruption: a model of Markov-switching regimes for the Brazilian country risk conditional volatility," Econometrics 0509005, University Library of Munich, Germany.
    7. Oliveira, André Barbosa & Pereira, Pedro L. Valls, 2018. "Uncertainty times for portfolio selection at financial market," Textos para discussão 473, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    8. Barbachan, José Fajardo & Schuschny, Andrés Ricardo & Silva, André de Castro, 2001. "Lévy processes and the Brazilian market," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 21(2), November.

Articles

1. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
   • Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
   • Marc Hallin & Luis K. Hotta & João H. G Mazzeu & Carlos Cesar Trucios-Maza & Pedro L. Valls Pereira & Mauricio Zevallos, 2019. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach," Working Papers ECARES 2019-14, ULB -- Universite Libre de Bruxelles.
   See citations under working paper version above.
2. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
   • Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
   See citations under working paper version above.
3. Carlos Trucíos & Mauricio Zevallos & Luiz K. Hotta & André A. P. Santos, 2019. "Covariance Prediction in Large Portfolio Allocation," Econometrics, MDPI, vol. 7(2), pages 1-24, May.

   Cited by:

   1. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
   2. Prayut Jain & Shashi Jain, 2019. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification," Risks, MDPI, vol. 7(3), pages 1-27, July.
   3. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
      • Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2022. "Can Volatility Solve the Naive Portfolio Puzzle?," Villanova School of Business Department of Economics and Statistics Working Paper Series 52, Villanova School of Business Department of Economics and Statistics.

4. Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.
   • Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
   See citations under working paper version above.
5. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
   • Almeida, Daniel de & Hotta, Luiz & Ruiz Ortega, Esther, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de Estadística.
   See citations under working paper version above.
6. Trucíos, Carlos & Hotta, Luiz K., 2016. "Bootstrap prediction in univariate volatility models with leverage effect," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 120(C), pages 91-103.

   Cited by:

   1. Hotta, Luiz & Trucíos, Carlos & Ruiz Ortega, Esther, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
   2. Mahsa Gorji & Rasoul Sajjad, 2017. "Improving Value-at-Risk Estimation from the Normal EGARCH Model," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 11(1), March.
   3. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
   4. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
   5. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
      • Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.

7. MÁrcio Poletti Laurini & Luiz Koodi Hotta, 2014. "Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 214-230, April.
   • Márcio Laurini & Luiz Koodi Hotta, 2011. "Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations," IBMEC RJ Economics Discussion Papers 2011-01, Economics Research Group, IBMEC Business School - Rio de Janeiro.
   See citations under working paper version above.
8. Ribeiro, André L.P. & Hotta, Luiz K., 2013. "An analysis of contagion among Asian countries using the canonical model of contagion," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 62-69.

   Cited by:

   1. André L P Ribeiro & Luiz K Hotta, 2016. "Estimation of the Heteroskedastic Canonical Contagion Model with Instrumental Variables," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-13, December.

9. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2013. "Indirect Inference in fractional short-term interest rate diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 109-126.

   Cited by:

   1. Xu, Dinghua & He, Yangao & Yu, Yue & Zhang, Qifeng, 2018. "Multiple parameter determination in textile material design:A Bayesian inference approach based on simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 151(C), pages 1-14.
   2. Richard A. Davis & Thiago do Rêgo Sousa & Claudia Klüppelberg, 2021. "Indirect inference for time series using the empirical characteristic function and control variates," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 653-684, September.

10. Luiz Hotta, 2010. "Bayesian Melding Estimation of a Stochastic SEIR Model," Mathematical Population Studies, Taylor & Francis Journals, vol. 17(2), pages 101-111.

    Cited by:

    1. Artalejo, J.R. & Economou, A. & Lopez-Herrero, M.J., 2015. "The stochastic SEIR model before extinction: Computational approaches," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 1026-1043.

11. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2010. "Bayesian extensions to Diebold-Li term structure model," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 342-350, December.
    • Laurini, Márcio P. & Hotta, Luiz K., 2008. "Bayesian extensions to diebold-li term structure model," Insper Working Papers wpe_122, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    See citations under working paper version above.
12. L. K. Hotta & E. C. Lucas & H. P Palaro, 2008. "Estimation of VaR Using Copula and Extreme Value Theory," Multinational Finance Journal, Multinational Finance Journal, vol. 12(3-4), pages 205-218, September.

    Cited by:

    1. Junni L. Zhang & Wolfgang Karl Hardle & Cathy Y. Chen & Elisabeth Bommes, 2020. "Distillation of News Flow into Analysis of Stock Reactions," Papers 2009.10392, arXiv.org.
    2. Chun-Pin Hsu & Chin-Wen Huang & Wan-Jiun Chiou, 2012. "Effectiveness of copula-extreme value theory in estimating value-at-risk: empirical evidence from Asian emerging markets," Review of Quantitative Finance and Accounting, Springer, vol. 39(4), pages 447-468, November.
    3. Karmakar, Madhusudan, 2017. "Dependence structure and portfolio risk in Indian foreign exchange market: A GARCH-EVT-Copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 275-291.
    4. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.
    5. Mirela NICHITA, 2015. "An Overview On State Of Knowledge Of Risk And Risk Management In Economics Fields," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 7, pages 423-430, April.
    6. Anderson Ara & Francisco Louzada & Carlos A. R. Diniz, 2017. "Statistical monitoring of a web server for error rates: a bivariate time-series copula-based modeling approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2287-2300, October.
    7. Duy Duong & Toan Luu Duc Huynh, 2020. "Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-26, December.
    8. Kellner, Ralf & Gatzert, Nadine, 2013. "Estimating the basis risk of index-linked hedging strategies using multivariate extreme value theory," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4353-4367.
    9. Huang, Jen-Jsung & Lee, Kuo-Jung & Liang, Hueimei & Lin, Wei-Fu, 2009. "Estimating value at risk of portfolio by conditional copula-GARCH method," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 315-324, December.
    10. Nader Trabelsi & Aviral Kumar Tiwari, 2019. "Market-Risk Optimization among the Developed and Emerging Markets with CVaR Measure and Copula Simulation," Risks, MDPI, vol. 7(3), pages 1-20, July.
    11. Beatriz de la Flor & Javier Ojea-Ferreiro & Eva Ferreira, 2022. "The Hedging Cost of Forgetting the Exchange Rate," Documentos de Trabajo del ICAE 2022-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    12. Alexandru Stanga, 2008. "Measuring market risk: a copula and extreme value approach," Advances in Economic and Financial Research - DOFIN Working Paper Series 13, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.

13. Luiz Hotta & Pedro Pereira & Rissa Ota, 2004. "Effect of outliers on forecasting temporally aggregated flow variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 371-402, December.

    Cited by:

    1. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
       • SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

14. Pereira, Pedro L. Valls & Hotta, Luiz K. & Souza, Luiz Alvares R. de & Almeida, Nuno Miguel C. G. de, 1999. "Alternative Models To Extract Asset Volatility: A Comparative Study," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 19(1), May.
    • Pedro L. Valls Pereira & Hotta, L.K. & Souza, L.A.R., 1999. "Alternative Models to extract asset volatility: a comparative study," Finance Lab Working Papers flwp_14, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    See citations under working paper version above.
15. Luiz K. Hotta & Klaus L. Vasconcellos, 1999. "Aggregation and Disaggregation of Structural Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(2), pages 155-171, March.

    Cited by:

    1. Baoline Chen, 2007. "An Empirical Comparison of Methods for Temporal Distribution and Interpolation at the National Accounts," BEA Papers 0077, Bureau of Economic Analysis.
    2. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 93-107, March.
       • Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Post-Print hal-00809247, HAL.
    3. Bu Hyoung Lee, 2022. "Bootstrap Prediction Intervals of Temporal Disaggregation," Stats, MDPI, vol. 5(1), pages 1-13, February.

16. L. K. Hotta & J. Cardosc Neto, 1993. "The Effect Of Aggregation On Prediction In Autoregressive Integrated Moving‐Average Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(3), pages 261-269, May.

    Cited by:

    1. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
       • SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," Working Papers hal-04141198, HAL.
       • Ferrara, L. & Marsilli, C. & Ortega, J-P., 2013. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Working papers 454, Banque de France.
       • Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, vol. 36(C), pages 44-50.
       • Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Post-Print hal-01385941, HAL.
       • Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," EconomiX Working Papers 2013-19, University of Paris Nanterre, EconomiX.
    3. Luiz Hotta & Pedro Pereira & Rissa Ota, 2004. "Effect of outliers on forecasting temporally aggregated flow variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 371-402, December.
    4. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Fotios Petropoulos, 2015. "Forecasting with Temporal Hierarchies," Monash Econometrics and Business Statistics Working Papers 16/15, Monash University, Department of Econometrics and Business Statistics.
       • Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2015. "Forecasting with Temporal Hierarchies," MPRA Paper 66362, University Library of Munich, Germany.
       • Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
    5. Souza, Leonardo Rocha, 2003. "The aliasing effect, the Fejer Kernel and temporally aggregated long memory processes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 470, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    6. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    7. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    8. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.

17. Hotta, Luiz Koodi, 1993. "The effect of additive outliers on the estimates from aggregated and disaggregated ARIMA models," International Journal of Forecasting, Elsevier, vol. 9(1), pages 85-93, April.

    Cited by:

    1. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Fotios Petropoulos, 2015. "Forecasting with Temporal Hierarchies," Monash Econometrics and Business Statistics Working Papers 16/15, Monash University, Department of Econometrics and Business Statistics.
       • Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2015. "Forecasting with Temporal Hierarchies," MPRA Paper 66362, University Library of Munich, Germany.
       • Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
    2. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
       • PREMINGER, Arie & FRANCK, Raphael, 2005. "Forecasting exchange rates: a robust regression approach," LIDAM Discussion Papers CORE 2005025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
       • PREMINGER, Arie & FRANCK, Raphael, 2007. "Forecasting exchange rates: a robust regression approach," LIDAM Reprints CORE 1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    4. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
       • Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    5. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    6. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.

18. Hotta, Luiz K. & Morettin, Pedro A. & Pereira, Pedro L. Valls, 1992. "The Effect of Overlapping Aggregation on Time Series Models: An Application to the Unemployment Rate in Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 12(2), November.

    Cited by:

    1. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.

19. Luiz Koodi Hotta, 1989. "Identification Of Unobserved Components Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 259-270, May.

    Cited by:

    1. Trenkler, Carsten & Weber, Enzo, 2015. "On the identification of multivariate correlated unobserved components models," Working Papers 15-12, University of Mannheim, Department of Economics.
       • Trenkler, Carsten & Weber, Enzo, 2016. "On the identification of multivariate correlated unobserved components models," Economics Letters, Elsevier, vol. 138(C), pages 15-18.
    2. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
       • Gabriele Fiorentini & Enrique Sentana, 2014. "Neglected Serial Correlation Tests in UCARIMA Models," Working Papers wp2014_1406, CEMFI.
    3. Zirogiannis, Nikolaos & Tripodis, Yorghos, 2013. "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Paper Series 142752, University of Massachusetts, Amherst, Department of Resource Economics.
    4. Antonis Demos, 2002. "Moments and dynamic structure of a time-varying parameter stochastic volatility in mean model," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 345-357, June.
    5. Nikolaos Zirogiannis & Yorghos Tripodis, 2018. "Dynamic factor analysis for short panels: estimating performance trajectories for water utilities," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 131-150, March.
    6. Wolff, Christian C. P., 2000. "Measuring the forward foreign exchange risk premium: multi-country evidence from unobserved components models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(1), pages 1-8, January.
    7. Zirogiannis, Nikolaos & Tripodis, Yorghos, 2014. "Dynamic Factor Analysis for Short Panels: Estimating Performance Trajectories for Water Utilities," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170592, Agricultural and Applied Economics Association.
    8. Dante Amengual & Xinyue Bei & Enrique Sentana, 2023. "Highly Irregular Serial Correlation Tests," Working Papers wp2023_2302, CEMFI.
    9. Eric Ghysels & Clive W.J. Granger & Pierre L. Siklos, 1995. "Is Seasonal Adjustment a Linear or Nonlinear Data Filtering Process?," CIRANO Working Papers 95s-19, CIRANO.
       • Ghysels, E. & Granger, C.W.J. & Siklos, P.L., 1995. "Is Seasonal Adjustment a Linear or Nonlinear Data Filtring Process," Cahiers de recherche 9517, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
       • Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-386, July.
       • Ghysels, E. & Granger, C.W.J. & Siklos, P.L., 1995. "Is Seasonal Adjustment a Linear or Nonlinear Data Filtring Process," Cahiers de recherche 9517, Universite de Montreal, Departement de sciences economiques.
    10. Nikolaos Zirogiannis & Yorghos Tripodis, 2013. "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Papers 2013-1, University of Massachusetts Amherst, Department of Resource Economics.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 11 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-ECM: Econometrics (5) 2007-05-26 2011-03-26 2015-08-25 2019-06-10 2020-01-06. Author is listed
2. NEP-ETS: Econometric Time Series (5) 2015-08-25 2015-12-01 2018-04-16 2019-06-10 2020-01-06. Author is listed
3. NEP-FOR: Forecasting (5) 2011-03-26 2015-12-01 2018-04-16 2020-01-06 2020-02-24. Author is listed
4. NEP-ORE: Operations Research (2) 2020-01-06 2020-02-24
5. NEP-IAS: Insurance Economics (1) 2009-07-28
6. NEP-IFN: International Finance (1) 2008-08-06
7. NEP-MAC: Macroeconomics (1) 2008-08-06
8. NEP-MON: Monetary Economics (1) 2008-08-06
9. NEP-RMG: Risk Management (1) 2019-07-22

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