Guilherme Valle Moura

Personal Details

First Name:	Guilherme
Middle Name:	Valle
Last Name:	Moura
Suffix:
RePEc Short-ID:	pmo897
	https://sites.google.com/site/guilhermevallemoura/

Affiliation

Centro Sócio-Econômico
Universidade Federal de Santa Catarina

Florianópolis, Brazil
http://www.cse.ufsc.br/
RePEc:edi:csufsbr (more details at EDIRC)

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. Fabricio Tourrucôo & João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2016. "Forecasting The Yield Curve With The Arbitrage-Free Dynamic Nelson-Siegel Model: Brazilian Evidence," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 028, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
   • João F. Caldeira & Guilherme V. Moura & , Fabricio Tourrucôo, 2016. "Forecasting the yield curve with the arbitrage-free dynamic Nelson-Siegel model: Brazilian evidence," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 17(2), pages 221-237.

   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. Amira Abid & Fathi Abid, 2024. "Sovereign Credit Risk in Saudi Arabia, Morocco and Egypt," JRFM, MDPI, vol. 17(7), pages 1-20, July.
   3. Eduardo Mineo & Airlane Pereira Alencar & Marcelo Moura & Antonio Elias Fabris, 2020. "Forecasting the Term Structure of Interest Rates with Dynamic Constrained Smoothing B-Splines," JRFM, MDPI, vol. 13(4), pages 1-14, April.

2. Guilherme Valle Moura & João Frois Caldeira & André Santos, 2014. "Seleção De Carteiras Utilizando O Modelofama-French-Carhart," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 117, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
   • Caldeira, João F & Moura, Guilherme Valle & Santos, André Alves Portela, 2013. "Seleção de carteiras utilizando o modelo Fama-French-Carhart," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(1), April.

   Cited by:

   1. Paulo Ferreira Naibert & João F. Caldeira, 2016. "Seleção De Carteiras Com Restrição Das Normas Das Posições: Uma Comparação Empírica Entre Diferentes Níveis De Restrição De Exposição Para Dados Da Bm&Fbovespa," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 132, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
   2. Ziegelmann, Flávio Augusto & Borges, Bruna & Caldeira, João F., 2015. "Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.

3. Sidney Martins Caetano & Guilherme Valle Moura, 2011. "Reajuste Informacionalno Brasil: uma aplicação da curva de Phillips sobrigidez de informação," Anais do XXXVII Encontro Nacional de Economia [Proceedings of the 37th Brazilian Economics Meeting] 54, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].

   Cited by:

   1. Machado, Vicente da Gama & Portugal, Marcelo Savino, 2014. "Measuring inflation persistence in Brazil using a multivariate model," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(2), June.
      • Vicente da Gama Machado & Marcelo Savino Portugal, 2013. "Measuring Inflation Persistence in Brazil Using a Multivariate Model," Working Papers Series 331, Central Bank of Brazil, Research Department.

4. Morales-Arias, Leonardo & Moura, Guilherme V., 2010. "A conditionally heteroskedastic global inflation model," Kiel Working Papers 1666, Kiel Institute for the World Economy (IfW Kiel).
   • Leonardo Morales‐Arias & Guilherme V. Moura, 2013. "A conditionally heteroskedastic global inflation model," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 40(4), pages 572-596, August.

   Cited by:

   1. Pablo Pincheira Brown & Nicolás Hardy, 2024. "Correlation‐based tests of predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1835-1858, September.
      • Pincheira, Pablo & Hardy, Nicolas, 2022. "Correlation Based Tests of Predictability," MPRA Paper 112014, University Library of Munich, Germany.
   2. Pincheira, Pablo, 2017. "A Power Booster Factor for Out-of-Sample Tests of Predictability," MPRA Paper 77027, University Library of Munich, Germany.
      • Pablo Pincheira Brown, 2022. "A Power Booster Factor for Out-of-Sample Tests of Predictability," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 45(89), pages 150-183.
   3. Gijsbert Suren & Guilherme Moura, 2012. "Heteroskedastic Dynamic Factor Models: A Monte Carlo Study," Economics Bulletin, AccessEcon, vol. 32(4), pages 2884-2898.
   4. Carlos A. Medel & Michael Pedersen & Pablo M. Pincheira, 2016. "The Elusive Predictive Ability of Global Inflation," International Finance, Wiley Blackwell, vol. 19(2), pages 120-146, June.
      • Carlos Medel & Michael Pedersen & Pablo Pincheira, 2014. "The Elusive Predictive Ability of Global Inflation," Working Papers Central Bank of Chile 725, Central Bank of Chile.

5. Morales-Arias, Leonardo & Moura, Guilherme V., 2010. "A conditionally heteroskedastic global inflation model," Kiel Working Papers 1666, Kiel Institute for the World Economy (IfW Kiel).
   • Leonardo Morales‐Arias & Guilherme V. Moura, 2013. "A conditionally heteroskedastic global inflation model," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 40(4), pages 572-596, August.

   Cited by:

   1. Pablo Pincheira Brown & Nicolás Hardy, 2024. "Correlation‐based tests of predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1835-1858, September.
      • Pincheira, Pablo & Hardy, Nicolas, 2022. "Correlation Based Tests of Predictability," MPRA Paper 112014, University Library of Munich, Germany.
   2. Pincheira, Pablo, 2017. "A Power Booster Factor for Out-of-Sample Tests of Predictability," MPRA Paper 77027, University Library of Munich, Germany.
      • Pablo Pincheira Brown, 2022. "A Power Booster Factor for Out-of-Sample Tests of Predictability," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 45(89), pages 150-183.
   3. Gijsbert Suren & Guilherme Moura, 2012. "Heteroskedastic Dynamic Factor Models: A Monte Carlo Study," Economics Bulletin, AccessEcon, vol. 32(4), pages 2884-2898.
   4. Carlos A. Medel & Michael Pedersen & Pablo M. Pincheira, 2016. "The Elusive Predictive Ability of Global Inflation," International Finance, Wiley Blackwell, vol. 19(2), pages 120-146, June.
      • Carlos Medel & Michael Pedersen & Pablo Pincheira, 2014. "The Elusive Predictive Ability of Global Inflation," Working Papers Central Bank of Chile 725, Central Bank of Chile.

6. Liesenfeld, Roman & Moura, Guilherme V. & Richard, Jean-François, 2009. "Determinants and dynamics of current account reversals: an empirical analysis," Economics Working Papers 2009-04, Christian-Albrechts-University of Kiel, Department of Economics.
   • Roman Liesenfeld & Guilherme Valle Moura & Jean‐François Richard, 2010. "Determinants and Dynamics of Current Account Reversals: An Empirical Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 486-517, August.

   Cited by:

   1. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.
   2. Martin Bijsterbosch & Tatjana Dahlhaus, 2015. "Key features and determinants of credit-less recoveries," Empirical Economics, Springer, vol. 49(4), pages 1245-1269, December.
   3. Yin-Wong Cheung & Sven Steinkamp & Frank Westermann, 2020. "A Tale of Two Surplus Countries: China and Germany," Open Economies Review, Springer, vol. 31(1), pages 131-158, February.
      • Yin-Wong Cheung & Sven Steinkamp & Frank Westermann, 2019. "A tale of two surplus countries: China and Germany," CESifo Working Paper Series 7669, CESifo.
      • Yin-Wong Cheung & Sven Steinkamp & Frank Westermann, 2019. "A Tale of Two Surplus Countries: China and Germany," IEER Working Papers 114, Institute of Empirical Economic Research, Osnabrueck University.
      • Yin-Wong Cheung & Sven Steinkamp & Frank Westermann, 2019. "A Tale of Two Surplus Countries: China and Germany," GRU Working Paper Series GRU_2019_010, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
   4. Theofilakou, Nancy & Stournaras, Yannis, 2012. "Current account adjustments in OECD countries revisited: The role of the fiscal stance," Journal of Policy Modeling, Elsevier, vol. 34(5), pages 719-734.
   5. Vieira, Fabrício de Assis Campos & Farias, Eliene de Sá & Mattos, Leonardo Bornacki de, 2022. "Commodity prices and capital movement phenomena in emerging economies," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
   6. Geert Mesters & Siem Jan Koopman, 2012. "Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time," Tinbergen Institute Discussion Papers 12-009/4, Tinbergen Institute, revised 18 Mar 2014.
      • Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
   7. Bijsterbosch, Martin & Dahlhaus, Tatjana, 2011. "Determinants of credit-less recoveries," Working Paper Series 1358, European Central Bank.
   8. Gonzalo Castañeda & Luis Castro Peñarrieta, 2024. "Give me a U, give me a V, give me an L!: How effective are countercyclical policies in shaping the output dynamic during recessions," Metroeconomica, Wiley Blackwell, vol. 75(1), pages 107-133, February.

7. DeJong, David Neil & Dharmarajan, Hariharan & Liesenfeld, Roman & Moura, Guilherme V. & Richard, Jean-François, 2009. "Efficient likelihood evaluation of state-space representations," Economics Working Papers 2009-02, Christian-Albrechts-University of Kiel, Department of Economics.
   • Roman Liesenfeld & Guilherme V. Moura & Jean-François Richard & Hariharan Dharmarajan, 2013. "Efficient Likelihood Evaluation of State-Space Representations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(2), pages 538-567.
   • David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Guilherme Moura & Jean-Francois Richard, 2009. "Efficient Likelihood Evaluation of State-Space Representations," Working Papers 2009/15, Czech National Bank.

   Cited by:

   1. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.
   2. Moura, Guilherme V. & Turatti, Douglas Eduardo, 2014. "Efficient estimation of conditionally linear and Gaussian state space models," Economics Letters, Elsevier, vol. 124(3), pages 494-499.
   3. Ozturk, Serda Selin & Richard, Jean-Francois, 2015. "Stochastic volatility and leverage: Application to a panel of S&P500 stocks," Finance Research Letters, Elsevier, vol. 12(C), pages 67-76.
   4. Andreasen, Martin M., 2011. "Non-linear DSGE models and the optimized central difference particle filter," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1671-1695, October.
   5. Steffen Henzel & Malte Rengel, 2013. "Dimensions of macroeconomic uncertainty: A common factor analysis," ifo Working Paper Series 167, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
      • Steffen Henzel & Malte Rengel, 2014. "Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis," CESifo Working Paper Series 4991, CESifo.
      • Henzel, Steffen R. & Rengel, Malte, 2017. "Dimensions of macroeconomic uncertainty: a common factor analysis," Munich Reprints in Economics 49932, University of Munich, Department of Economics.
      • Steffen R. Henzel & Malte Rengel, 2017. "Dimensions Of Macroeconomic Uncertainty: A Common Factor Analysis," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 843-877, April.
   6. Hall, Jamie & Pitt, Michael K. & Kohn, Robert, 2014. "Bayesian inference for nonlinear structural time series models," Journal of Econometrics, Elsevier, vol. 179(2), pages 99-111.
   7. Kleppe, Tore Selland & Oglend, Atle, 2017. "Estimating the competitive storage model: A simulated likelihood approach," Econometrics and Statistics, Elsevier, vol. 4(C), pages 39-56.
   8. Yang, Yuan & Wang, Lu, 2015. "An Improved Auxiliary Particle Filter for Nonlinear Dynamic Equilibrium Models," Dynare Working Papers 47, CEPREMAP.
   9. Marta Boczoń & Jean-François Richard, 2020. "Balanced Growth Approach to Tracking Recessions," Econometrics, MDPI, vol. 8(2), pages 1-35, April.
   10. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.

8. Moura, Guilherme V. & Richard, Jean-François & Liesenfeld, Roman, 2007. "Dynamic Panel Probit Models for Current Account Reversals and their Efficient Estimation," Economics Working Papers 2007-11, Christian-Albrechts-University of Kiel, Department of Economics.
   • Guilherme Valle Moura & Roman Liesenfeld & Jean-Francois Richard, 2008. "Dynamic Panel Probit Models for Current Account Reversals and their Efficient Estimation," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807141048250, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].

   Cited by:

   1. Luiz de Mello & Pier Carlo Padoan & Linda Rousová, 2012. "Are Global Imbalances Sustainable? Shedding Further Light on the Causes of Current Account Reversals," Review of International Economics, Wiley Blackwell, vol. 20(3), pages 489-516, August.
      • Luiz de Mello & Pier Carlo Padoan & Linda Rousová, 2010. "Are Global Imbalances Sustainable?: Shedding Further Light on the Causes of Current Account Reversals," OECD Economics Department Working Papers 813, OECD Publishing.
   2. Alfonso Camba-Crespo & José García-Solanes & Fernando Torrejón-Flores, 2021. "Current-account breaks and stability spells in a global perspective," Applied Economic Analysis, Emerald Group Publishing Limited, vol. 30(88), pages 1-17, July.
   3. Rodrigo Zeidan, 2014. "Voluntary corporate governance with an empirical application," Applied Financial Economics, Taylor & Francis Journals, vol. 24(12), pages 837-851, June.

9. Guilherme Moura & Sergio Da Silva, 2005. "Testing the Equilibrium Exchange Rate Model," International Finance 0505018, University Library of Munich, Germany.

   Cited by:

   1. Guilherme, Moura & Sergio, Da Silva, 2006. "Testing the Equilibrium Exchange Rate Model - Updated," MPRA Paper 1871, University Library of Munich, Germany.

10. Guilherme Moura & Sergio Da Silva, 2005. "Is There a Brazilian J-Curve?," International Finance 0505001, University Library of Munich, Germany.
    • Sergio Da Silva & Guilherme Moura, 2005. "Is There a Brazilian J-Curve?," Economics Bulletin, AccessEcon, vol. 6(10), pages 1-17.

    Cited by:

    1. Elano Ferreira Arruda & Antônio Clécio de Brito & Pablo Urano de Carvalho Castelar, 2022. "Exchange Rate and Trade Balances in Brazil: A Disaggregated Analysis by Major Economic Categories," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 14(6), pages 1-62, June.
    2. Raifu, Isiaka Akande & Aminu, Alarudeen & Adeniyi, Oluwatosin Ademola, 2019. "What nexus exists between exchange rate and trade balance? The case of Nigeria vis-à-vis UK, US and Hong Kong," MPRA Paper 92976, University Library of Munich, Germany.
    3. Guttler, Caio & Meurer, Roberto & Da Silva, Sergio, 2006. "Informational inefficiency of the Brazilian stockmarket," MPRA Paper 1980, University Library of Munich, Germany.
    4. Kris Ivanovski & Sefa Awaworyi Churchill & Ahmed Salim Nuhu, 2020. "Modelling the Australian J‐Curve: An ARDL Cointegration Approach," Economic Papers, The Economic Society of Australia, vol. 39(2), pages 167-184, June.
    5. Sergio Da Silva & Annibal Figueiredo & Iram Gleria & Raul Matsushita, 2007. "Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market," Economics Bulletin, AccessEcon, vol. 7(1), pages 1-11.
    6. Jamilov, Rustam, 2013. "Capital mobility in the Caucasus," Economic Systems, Elsevier, vol. 37(2), pages 155-170.
       • Rustam Jamilov, 2013. "J-Curve Dynamics and the Marshall–Lerner Condition: Evidence from Azerbaijan," Transition Studies Review, Springer;Central Eastern European University Network (CEEUN), vol. 19(3), pages 313-323, February.
    7. Peguero, Anadel G. & Cruz-Rodríguez, Alexis, 2016. "Condición Marshall-Lerner y el efecto Curva J: Evidencias para la República Dominicana [Marshall-Lerner Condition and J-Curve Effect: Evidence for the Dominican Republic]," MPRA Paper 71535, University Library of Munich, Germany.
    8. Mohsen Bahmani‐Oskooee & Niloy Bose & Yun Zhang, 2019. "An asymmetric analysis of the J‐curve effect in the commodity trade between China and the US," The World Economy, Wiley Blackwell, vol. 42(10), pages 2854-2899, October.
    9. Bustamante, Rafael & Morales, Fedor, 2009. "Probando la condición de Marshall-Lerner y el efecto Curva-J: Evidencia empírica para el caso peruano," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 16, pages 103-126.
    10. Jamilov, Rustam, 2011. "J-Curve Dynamics and the Marshall-Lerner Condition: Evidence from Azerbaijan," MPRA Paper 36799, University Library of Munich, Germany, revised Feb 2012.
    11. Mohsen Bahmani-Oskooee & Muhammad Aftab, 2017. "Malaysia–Korea Commodity Trade: Are there Asymmetric Responses to Exchange Rate Changes?," Economic Papers, The Economic Society of Australia, vol. 36(2), pages 198-222, June.
    12. Luciana Reis & Roberto Meurer & Sergio Da Silva, 2010. "Stock returns and foreign investment in Brazil," Applied Financial Economics, Taylor & Francis Journals, vol. 20(17), pages 1351-1361.
        • Reis, Luciana & Meurer, Roberto & Da Silva, Sergio, 2008. "Stock returns and foreign investment in Brazil," MPRA Paper 23028, University Library of Munich, Germany.
    13. Bahmani-Oskooee, Mohsen & Aftab, Muhammad & Harvey, Hanafiah, 2016. "Asymmetry cointegration and the J-curve: New evidence from Malaysia-Singapore commodity trade," The Journal of Economic Asymmetries, Elsevier, vol. 14(PB), pages 211-226.
    14. Mohsen Bahmani-Oskooee & Muhammad Aftab, 2017. "Asymmetric Effects of Exchange Rate Changes and the J-curve: New Evidence from 61 Malaysia–Thailand Industries," Review of Development Economics, Wiley Blackwell, vol. 21(4), pages 30-46, November.
    15. Halicioglu, Ferda, 2007. "The Bilateral J-curve: Turkey versus her 13 Trading Partners," MPRA Paper 3564, University Library of Munich, Germany.
        • Halicioglu, Ferda, 2008. "The bilateral J-curve: Turkey versus her 13 trading partners," Journal of Asian Economics, Elsevier, vol. 19(3), pages 236-243, June.
    16. Mohsen Bahmani-Oskooee & Niloy Bose & Yun Zhang, 2018. "Asymmetric Cointegration, Nonlinear ARDL, and the J-Curve: A Bilateral Analysis of China and Its 21 Trading Partners," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(13), pages 3131-3151, October.

11. Roberto Meurer & Guilherme Moura & Sergio Da Silva, 2005. "Travel Hysteresis in the Brazilian Current Account," International Trade 0509007, University Library of Munich, Germany.
    • Sergio Da Silva & Guilherme Moura & Roberto Meurer, 2005. "Travel hysteresis in the Brazilian current account," Economics Bulletin, AccessEcon, vol. 6(24), pages 1-17.

    Cited by:

    1. Sergio Da Silva & Guilherme Moura & Roberto Meurer, 2005. "Travel hysteresis in the US current account after the mid-1980s," Economics Bulletin, AccessEcon, vol. 14(2), pages 1-10.
       • Roberto Meurer & Guilherme Moura & Sergio Da Silva, 2005. "Travel Hysteresis in the US Current Account After the Mid-1980s," Economic History 0511002, University Library of Munich, Germany.

12. Sidney Caetano & Guilherme Moura & Sergio Da Silva, 2004. "Big Mac Parity, Income, and Trade," International Finance 0407011, University Library of Munich, Germany.
    • Sergio Da Silva & Guilherme Moura & Sidney Caetano, 2004. "Big Mac parity, income, and trade," Economics Bulletin, AccessEcon, vol. 6(12), pages 1-8.

    Cited by:

    1. Duc Hong Vo & Anh The Vo, 2017. "Currency evaluation using a big mac index for Thailand – lessons for Vietnam," Economics Bulletin, AccessEcon, vol. 37(2), pages 999-1011.

Articles

1. Moura, Guilherme V. & Noriller, Mateus R., 2019. "Maximum likelihood estimation of a TVP-VAR," Economics Letters, Elsevier, vol. 174(C), pages 78-83.

   Cited by:

   1. Moura, Guilherme V. & Santos, André A. P., 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.
   2. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," GRIPS Discussion Papers 23-07, National Graduate Institute for Policy Studies.
      • Roberto Leon-Gonzalez & Blessings Majon, 2024. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," GRIPS Discussion Papers 24-03, National Graduate Institute for Policy Studies.
      • Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," Working Paper series 23-11, Rimini Centre for Economic Analysis.
   3. Jan Patrick Hartkopf, 2023. "Composite forecasting of vast-dimensional realized covariance matrices using factor state-space models," Empirical Economics, Springer, vol. 64(1), pages 393-436, January.
   4. Qiao, Xingzhi & Zhu, Huiming & Zhang, Zhongqingyang & Mao, Weifang, 2022. "Time-frequency transmission mechanism of EPU, investor sentiment and financial assets: A multiscale TVP-VAR connectedness analysis," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
   5. Moura, Guilherme V. & Santos, André A.P. & Ruiz, Esther, 2020. "Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 118(C).

2. João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2018. "Yield curve forecast combinations based on bond portfolio performance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 64-82, January.

   Cited by:

   1. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
      • João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
   2. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.

3. 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.

   Cited by:

   1. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org, revised Jan 2025.
   2. 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.
   3. Dudley Gilder & Leonidas Tsiaras, 2020. "Volatility forecasts embedded in the prices of crude‐oil options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1127-1159, July.
   4. 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, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de Estadística.
   5. Robiyanto Robiyanto & Bayu Adi Nugroho & Andrian Dolfriandra Huruta & Budi Frensidy & Suyanto Suyanto, 2021. "Identifying the Role of Gold on Sustainable Investment in Indonesia: The DCC-GARCH Approach," Economies, MDPI, vol. 9(3), pages 1-14, August.
   6. Adam Clements & Mark Bernard Doolan, 2020. "Combining multivariate volatility forecasts using weighted losses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 628-641, July.
      • A Clements & M Doolan, 2018. "Combining Multivariate Volatility Forecasts using Weighted Losses," NCER Working Paper Series 119, National Centre for Econometric Research.
   7. 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.
   8. Panos K. Pouliasis & Ilias D. Visvikis & Nikos C. Papapostolou & Alexander A. Kryukov, 2020. "A novel risk management framework for natural gas markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(3), pages 430-459, March.
   9. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
   10. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.

4. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.

   Cited by:

   1. Wang, Ce & Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2018. "Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models," Energy, Elsevier, vol. 162(C), pages 272-281.
   2. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
      • João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
   3. Hofert, Marius & Prasad, Avinash & Zhu, Mu, 2022. "Multivariate time-series modeling with generative neural networks," Econometrics and Statistics, Elsevier, vol. 23(C), pages 147-164.
   4. Almaguer, F-Javier & Amezcua, Omar González & Morales-Castillo, Javier & Soto-Villalobos, Roberto, 2018. "Riemann and Weierstrass walks revisited," Applied Mathematics and Computation, Elsevier, vol. 319(C), pages 518-526.
   5. João F. Caldeira, 2020. "Investigating the expectation hypothesis and the risk premium dynamics: new evidence for Brazil," Empirical Economics, Springer, vol. 59(1), pages 395-412, July.
   6. Ausloos, Marcel & Cerqueti, Roy & Bartolacci, Francesca & Castellano, Nicola G., 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 754-765.
      • Marcel Ausloos & Roy Cerqueti & Francesca Bartolacci & Nicola G. Castellano, 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Papers 1807.09583, arXiv.org.
   7. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.
   8. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
   9. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
      • Costantini, Mauro & Kunst, Robert M., 2018. "On Using Predictive-ability Tests in the Selection of Time-series Prediction Models: A Monte Carlo Evaluation," Economics Series 341, Institute for Advanced Studies.
   10. Malgorzata Solarz & Jacek Adamek, 2021. "Factors Affecting Mobile Banking Adoption in Poland: An Empirical Study," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1018-1046.
   11. Simpson, Michael C. & Chatzopoulou, Maria Anna & Oyewunmi, Oyeniyi A. & Le Brun, Niccolo & Sapin, Paul & Markides, Christos N., 2019. "Technoeconomic analysis of internal combustion engine – organic Rankine cycle systems for combined heat and power in energy-intensive buildings," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
   12. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
   13. Stona, Filipe & Caldeira, João F., 2019. "Do U.S. factors impact the Brazilian yield curve? Evidence from a dynamic factor model," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 76-89.
   14. Wali ULLAH & Khadija Malik BARI, 2018. "The Term Structure of Government Bond Yields in an Emerging Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-28, September.

5. João F. Caldeira & Guilherme V. Moura & , Fabricio Tourrucôo, 2016. "Forecasting the yield curve with the arbitrage-free dynamic Nelson-Siegel model: Brazilian evidence," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 17(2), pages 221-237.
   • Fabricio Tourrucôo & João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2016. "Forecasting The Yield Curve With The Arbitrage-Free Dynamic Nelson-Siegel Model: Brazilian Evidence," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 028, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
   See citations under working paper version above.
6. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.

   Cited by:

   1. Potjagailo, Galina & Wolters, Maik H., 2019. "Global financial cycles since 1880," IMFS Working Paper Series 132, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
      • Potjagailo, Galina & Wolters, Maik H., 2019. "Global financial cycles since 1880," Kiel Working Papers 2122, Kiel Institute for the World Economy (IfW Kiel).
      • Potjagailo, Galina & Wolters, Maik H, 2020. "Global financial cycles since 1880," Bank of England working papers 867, Bank of England.
      • Potjagailo, Galina & Wolters, Maik H., 2023. "Global financial cycles since 1880," Journal of International Money and Finance, Elsevier, vol. 131(C).
   2. Candelon, Bertrand & Luisi , Angelo & Roccazzella, Francesco, 2022. "Fragmentation in the European Monetary Union: Is it really over?," LIDAM Reprints LFIN 2022001, Université catholique de Louvain, Louvain Finance (LFIN).
      • Candelon, Bertrand & Luisi , Angelo & Roccazzella, Francesco, 2021. "Fragmentation in the European Monetary Union: Is it really over?," LIDAM Discussion Papers LFIN 2021015, Université catholique de Louvain, Louvain Finance (LFIN).
      • Candelon, Bertrand & Luisi, Angelo & Roccazzella, Francesco, 2022. "Fragmentation in the European Monetary Union: Is it really over?," Journal of International Money and Finance, Elsevier, vol. 122(C).
      • Bertrand Candelon & Angelo Luisi & Francesco Roccazzella, 2021. "Fragmentation in the European Monetary Union: Is it really over?," GRU Working Paper Series GRU_2021_016, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
   3. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
      • João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
   4. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
   5. Awe Olushina Olawale & Adepoju Abosede Adedayo, 2020. "Change-point detection in CO2 emission-energy consumption nexus using a recursive Bayesian estimation approach," Statistics in Transition New Series, Statistics Poland, vol. 21(1), pages 123-136, March.
   6. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
   7. Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
      • George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure," SERIES 03-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.
   8. Rui Wang, 2019. "Unconventional Monetary Policy in Japan: Empirical Evidence from Estimated Shadow Rate DSGE Model," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-29, June.
   9. Ma, Shuai & Ma, Xiaoteng & Xia, Li, 2023. "A unified algorithm framework for mean-variance optimization in discounted Markov decision processes," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1057-1067.
   10. Santos, André A.P. & Moura, Guilherme V., 2014. "Dynamic factor multivariate GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 606-617.
   11. Hamill, Philip A. & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A. & Waterworth, James, 2021. "Was a deterioration in ‘connectedness’ a leading indicator of the European sovereign debt crisis?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
   12. Mouloud El Hafidi & Marouane Daoui, 2019. "Chocs de la politique monétaire et croissance économique au Maroc : une approche en terme de modèles FAVAR," Post-Print hal-03311354, HAL.
   13. Choi, Ahjin & Kang, Kyu Ho, 2023. "Modeling the time-varying dynamic term structure of interest rates," Journal of Banking & Finance, Elsevier, vol. 153(C).
   14. Hsiang-Hsi Liu & Chien-Kuo Tseng, 2022. "Common Components in Co-integrated System and Its Estimation and Application: Evidence from Five Stock Markets in Asia-Pacific Chinese Region," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 101-121.
   15. Aysu Celgin & Mahmut Gunay, 2020. "Weekly Economic Conditions Index for Turkey," CBT Research Notes in Economics 2018, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
   16. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
   17. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
   18. Rueben Ellul & Germano Ruisi, 2022. "Nowcasting the Maltese economy with a dynamic factor model," CBM Working Papers WP/02/2022, Central Bank of Malta.
   19. Wulan Anggraeni & Sudradjat Supian & Sukono & Nurfadhlina Abdul Halim, 2023. "Catastrophe Bond Diversification Strategy Using Probabilistic–Possibilistic Bijective Transformation and Credibility Measures in Fuzzy Environment," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
   20. Konstantinos Bisiotis & Stelios Psarakis & Athanasios N. Yannacopoulos, 2022. "Affine Term Structure Models: Applications in Portfolio Optimization and Change Point Detection," Mathematics, MDPI, vol. 10(21), pages 1-33, November.
   21. Jiahe Lin & George Michailidis, 2019. "Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models," Papers 1912.04146, arXiv.org, revised May 2020.

7. Geoffrey M. Steeves & Francis Carlo Petterini & Guilherme V. Moura, 2015. "The interiorization of Brazilian violence, policing, and economic growth," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 16(3), pages 359-375.

   Cited by:

   1. Barros, Pedro Henrique Batista de & Baggio, Isadora Salvalaggio & Stege, Alysson Luiz & Hilgemberg, Cleise Maria de Almeida Tupich, 2019. "Economic development and crime in Brazil: a multivariate and spatial analysis," Revista Brasileira de Estudos Regionais e Urbanos, Associação Brasileira de Estudos Regionais e Urbanos (ABER), vol. 13(1), pages 1-22, June.
   2. Francis Petterini & Akauã Flores, 2021. "Copula econometrics to simulate effects of private policing on crime," Economics Bulletin, AccessEcon, vol. 41(3), pages 1241-1254.

8. João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.

   Cited by:

   1. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
   2. Schlütter, Sebastian, 2017. "Scenario-based capital requirements for the interest rate risk of insurance companies," ICIR Working Paper Series 28/17, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
   3. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
   4. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
   5. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
   6. Makushkin, Mikhail & Lapshin, Victor, 2023. "Dynamic Nelson–Siegel model for market risk estimation of bonds: Practical implementation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 69, pages 5-27.
   7. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2016. "What derives the bond portfolio value-at-risk: Information roles of macroeconomic and financial stress factors," SFB 649 Discussion Papers 2016-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
   8. Konstantinos Bisiotis & Stelios Psarakis & Athanasios N. Yannacopoulos, 2022. "Affine Term Structure Models: Applications in Portfolio Optimization and Change Point Detection," Mathematics, MDPI, vol. 10(21), pages 1-33, November.

9. Moura, Guilherme Valle, 2015. "Multiplicadores Fiscais e Investimento em Infraestrutura," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 69(1), March.

   Cited by:

   1. Grudter, Vanessa & Aragon, Edilean Kleber, 2017. "Multiplicador dos gastos do governo em períodos de expansão e recessão: evidências empíricas para o Brasil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 71(3), September.
   2. Alejandro C. Garcia-Cintado & Celso Jose Costa Junior (celso.costa@fgv.br) & Armando Vaz Sampaio (avsampaio@ufpr.br), 2016. "Post-2008 Brazilian Fiscal Policy: an Interpretation through the Analysis of Fiscal Multipliers," EcoMod2016 9528, EcoMod.
   3. Mora, Jose U Mora & Acevedo, Rafael A, 2019. "Fiscal Policy Effects and Capital Mobility in Latin American Countries," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 34(1), pages 159-188.
   4. Delalibera, Bruno R. & Serrano-Quintero, Rafael & Zimmermann, Guilherme G., 2023. "Reforms in the natural gas sector and economic development," Economic Modelling, Elsevier, vol. 125(C).
   5. Kazakova, O. B. & Kuzminykh, N. A., 2017. "The multiplier accelerator theory in the study of municipal-level investment," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 3(2), pages 82-89.

10. Santos, André A.P. & Moura, Guilherme V., 2014. "Dynamic factor multivariate GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 606-617.

    Cited by:

    1. João Caldeira & Guilherme Moura & André A.P. Santos, 2012. "Portfolio optimization using a parsimonious multivariate GARCH model: application to the Brazilian stock market," Economics Bulletin, AccessEcon, vol. 32(3), pages 1848-1857.
    2. 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.
    3. 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.
       • 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.
       • 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).
    4. Antonio Díaz & Carlos Esparcia, 2021. "Dynamic optimal portfolio choice under time-varying risk aversion," International Economics, CEPII research center, issue 166, pages 1-22.
       • Díaz, Antonio & Esparcia, Carlos, 2021. "Dynamic optimal portfolio choice under time-varying risk aversion," International Economics, Elsevier, vol. 166(C), pages 1-22.
    5. Guilherme Valle Moura & João Frois Caldeira & André Santos, 2014. "Seleção De Carteiras Utilizando O Modelofama-French-Carhart," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 117, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
       • Caldeira, João F & Moura, Guilherme Valle & Santos, André Alves Portela, 2013. "Seleção de carteiras utilizando o modelo Fama-French-Carhart," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(1), April.
    6. Francq, Christian & Sucarrat, Genaro, 2017. "An equation-by-equation estimator of a multivariate log-GARCH-X model of financial returns," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 16-32.
       • Francq, Christian & Sucarrat, Genaro, 2015. "Equation-by-Equation Estimation of a Multivariate Log-GARCH-X Model of Financial Returns," MPRA Paper 67140, University Library of Munich, Germany.
    7. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    8. João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.
    9. Gijsbert Suren & Guilherme Moura, 2012. "Heteroskedastic Dynamic Factor Models: A Monte Carlo Study," Economics Bulletin, AccessEcon, vol. 32(4), pages 2884-2898.
    10. Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2020. "Portfolio selection: shrinking the time-varying inverse conditional covariance matrix," Statistical Papers, Springer, vol. 61(6), pages 2583-2604, December.
    11. Serdar Neslihanoglu & Stelios Bekiros & John McColl & Duncan Lee, 2021. "Multivariate time-varying parameter modelling for stock markets," Empirical Economics, Springer, vol. 61(2), pages 947-972, August.
    12. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
        • Audrino, Francesco, 2011. "Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks," Economics Working Paper Series 1112, University of St. Gallen, School of Economics and Political Science.
    13. 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.
    14. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021. "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, vol. 125(C).
    15. Mengheng Li & Marcel Scharth, 2022. "Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 285-301, January.
        • Mengheng Li & Marcel Scharth, 2018. "Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model," Working Paper Series 49, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    16. Esparcia, Carlos & Diaz, Antonio & Alonso, Daniel, 2023. "How important is green awareness in energy investment decisions? An environmentally-based rebalancing portfolio study," Energy Economics, Elsevier, vol. 128(C).
    17. Abdul Aziz, Nor Syahilla & Vrontos, Spyridon & M. Hasim, Haslifah, 2019. "Evaluation of multivariate GARCH models in an optimal asset allocation framework," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 568-596.
    18. Díaz, Antonio & Esparcia, Carlos & López, Raquel, 2022. "The diversifying role of socially responsible investments during the COVID-19 crisis: A risk management and portfolio performance analysis," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 39-60.

11. Moura, Guilherme V. & Turatti, Douglas Eduardo, 2014. "Efficient estimation of conditionally linear and Gaussian state space models," Economics Letters, Elsevier, vol. 124(3), pages 494-499.

    Cited by:

    1. Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
       • Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    3. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.

12. Roman Liesenfeld & Guilherme V. Moura & Jean-François Richard & Hariharan Dharmarajan, 2013. "Efficient Likelihood Evaluation of State-Space Representations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(2), pages 538-567.
    • DeJong, David Neil & Dharmarajan, Hariharan & Liesenfeld, Roman & Moura, Guilherme V. & Richard, Jean-François, 2009. "Efficient likelihood evaluation of state-space representations," Economics Working Papers 2009-02, Christian-Albrechts-University of Kiel, Department of Economics.
    • David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Guilherme Moura & Jean-Francois Richard, 2009. "Efficient Likelihood Evaluation of State-Space Representations," Working Papers 2009/15, Czech National Bank.
    See citations under working paper version above.
13. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 493-509.
    • Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Research Paper Series 285, Quantitative Finance Research Centre, University of Technology, Sydney.

    Cited by:

    1. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    2. Raheem, Ibrahim, 2020. "Global financial cycles and exchange rate forecast: A factor analysis," MPRA Paper 105358, University Library of Munich, Germany.
    3. Raheem, Ibrahim & Vo, Xuan Vinh, 2020. "A new approach to exchange rate forecast: The role of global financial cycle and time-varying parameters," MPRA Paper 105359, University Library of Munich, Germany.
       • Ibrahim D. Raheem & Xuan Vinh Vo, 2022. "A new approach to exchange rate forecast: The role of global financial cycle and time‐varying parameters," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2836-2848, July.
    4. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    5. Kouwenberg, Roy & Markiewicz, Agnieszka & Verhoeks, Ralph & Zwinkels, Remco C. J., 2017. "Model Uncertainty and Exchange Rate Forecasting," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(1), pages 341-363, February.
    6. Michał Chojnowski & Piotr Dybka, 2017. "Is Exchange Rate Moody? Forecasting Exchange Rate with Google Trends Data," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(1), pages 1-21, June.
    7. Wagner Piazza Gaglianone & Osmani Teixeira de Carvalho Guillén & Francisco Marcos Rodrigues Figueiredo, 2015. "Local Unit Root and Inflationary Inertia in Brazil," Working Papers Series 406, Central Bank of Brazil, Research Department.
    8. Ren, Yu & Liang, Xuanxuan & Wang, Qin, 2021. "Short-term exchange rate forecasting: A panel combination approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    9. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
       • Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner P. Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Yihao Lin, 2021. "Machine Learning and Oil Price Point and Density Forecasting," Working Papers Series 544, Central Bank of Brazil, Research Department.
    10. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
        • Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2016. "Evaluation of Exchange Rate Point and Density Forecasts: an application to Brazil," Working Papers Series 446, Central Bank of Brazil, Research Department.
    11. Kharrat, Sabrine & Hammami, Yacine & Fatnassi, Ibrahim, 2020. "On the cross-sectional relation between exchange rates and future fundamentals," Economic Modelling, Elsevier, vol. 89(C), pages 484-501.
    12. Alexandros Pasiouras & Theodoros Daglis, 2020. "The Dollar Exchange Rates in the Covid-19 Era: Evidence from 5 Currencies," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 352-361.
    13. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
        • Gustavo Silva Araujo & Wagner Piazza Gaglianone, 2022. "Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models," Working Papers Series 561, Central Bank of Brazil, Research Department.
    14. Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2014. "Risk Assessment of the Brazilian FX Rate," Working Papers Series 344, Central Bank of Brazil, Research Department.
    15. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    16. Konstantinos N. Konstantakis & Ioannis G. Melissaropoulos & Theodoros Daglis & Panayotis G. Michaelides, 2023. "The euro to dollar exchange rate in the Covid‐19 era: Evidence from spectral causality and Markov‐switching estimation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2037-2055, April.

14. Leonardo Morales‐Arias & Guilherme V. Moura, 2013. "A conditionally heteroskedastic global inflation model," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 40(4), pages 572-596, August.
    • Morales-Arias, Leonardo & Moura, Guilherme V., 2010. "A conditionally heteroskedastic global inflation model," Kiel Working Papers 1666, Kiel Institute for the World Economy (IfW Kiel).
    See citations under working paper version above.
15. Caldeira, João F & Moura, Guilherme Valle & Santos, André Alves Portela, 2013. "Seleção de carteiras utilizando o modelo Fama-French-Carhart," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(1), April.
    • Guilherme Valle Moura & João Frois Caldeira & André Santos, 2014. "Seleção De Carteiras Utilizando O Modelofama-French-Carhart," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 117, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    See citations under working paper version above.
16. Roman Liesenfeld & Guilherme Valle Moura & Jean‐François Richard, 2010. "Determinants and Dynamics of Current Account Reversals: An Empirical Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 486-517, August.
    • Liesenfeld, Roman & Moura, Guilherme V. & Richard, Jean-François, 2009. "Determinants and dynamics of current account reversals: an empirical analysis," Economics Working Papers 2009-04, Christian-Albrechts-University of Kiel, Department of Economics.
    See citations under working paper version above.
17. Ricardo Azevedo Araujo & Guilherme V. Moura & Marcelo S. Portugal, 2010. "Efficient Yield Curve Estimation and Forecasting in Brazil," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 11(1), pages 27-51.

    Cited by:

    1. Piero C. Kauffmann & Hellinton H. Takada & Ana T. Terada & Julio M. Stern, 2022. "Learning Forecast-Efficient Yield Curve Factor Decompositions with Neural Networks," Econometrics, MDPI, vol. 10(2), pages 1-15, March.
    2. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    3. 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.
    4. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.

18. Sergio Da Silva & Guilherme Moura, 2005. "Is There a Brazilian J-Curve?," Economics Bulletin, AccessEcon, vol. 6(10), pages 1-17.
    • Guilherme Moura & Sergio Da Silva, 2005. "Is There a Brazilian J-Curve?," International Finance 0505001, University Library of Munich, Germany.
    See citations under working paper version above.
19. Sergio Da Silva & Guilherme Moura & Roberto Meurer, 2005. "Travel hysteresis in the Brazilian current account," Economics Bulletin, AccessEcon, vol. 6(24), pages 1-17.
    • Roberto Meurer & Guilherme Moura & Sergio Da Silva, 2005. "Travel Hysteresis in the Brazilian Current Account," International Trade 0509007, University Library of Munich, Germany.
    See citations under working paper version above.
20. Sergio Da Silva & Guilherme Moura & Sidney Caetano, 2004. "Big Mac parity, income, and trade," Economics Bulletin, AccessEcon, vol. 6(12), pages 1-8.
    • Sidney Caetano & Guilherme Moura & Sergio Da Silva, 2004. "Big Mac Parity, Income, and Trade," International Finance 0407011, University Library of Munich, Germany.
    See citations under working paper version above.

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 10 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 (4) 2007-06-30 2009-09-19 2019-09-09 2019-12-09
2. NEP-CBA: Central Banking (3) 2007-06-30 2009-09-19 2010-12-18
3. NEP-ETS: Econometric Time Series (3) 2009-09-19 2019-09-09 2019-12-09
4. NEP-FOR: Forecasting (3) 2010-12-18 2011-11-07 2019-12-09
5. NEP-IFN: International Finance (3) 2007-02-24 2007-06-30 2007-07-13
6. NEP-DCM: Discrete Choice Models (2) 2007-06-30 2009-09-19
7. NEP-DGE: Dynamic General Equilibrium (2) 2009-09-19 2014-03-15
8. NEP-MAC: Macroeconomics (1) 2010-12-18
9. NEP-MON: Monetary Economics (1) 2010-12-18
10. NEP-OPM: Open Economy Macroeconomics (1) 2009-09-19
11. NEP-ORE: Operations Research (1) 2019-12-09
12. NEP-RMG: Risk Management (1) 2019-12-09

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