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Wagner Piazza Gaglianone

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. 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.

    Cited by:

    1. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    2. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    3. Marta Baltar Moreira Areosa & Wagner Piazza Gaglianone, 2023. "Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models," Working Papers Series 574, Central Bank of Brazil, Research Department.
    4. 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).
    5. Seyed Farshid Ghorashi & Maziyar Bahri & Atousa Goodarzi, 2024. "Developing and comparing machine learning approaches for predicting insurance penetration rates based on each country," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-29, December.
    6. Panpan Zhu & Qingjie Zhou & Yinpeng Zhang, 2024. "Investor attention and consumer price index inflation rate: Evidence from the United States," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    7. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    8. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    9. Vadim Grishchenko & Ivan Krylov, 2024. "New Approaches to Measuring, Analysing, and Forecasting Prices: A Review of the Bank of Russia, NES, and HSE University Workshop," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 92-111, June.
    10. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    11. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.

  2. 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.

    Cited by:

    1. Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
    2. Claudia ANTAL-VAIDA, 2021. "Basic Hyperparameters Tuning Methods for Classification Algorithms," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 25(2), pages 64-74.
    3. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).
    4. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    5. 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).
    6. Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner Piazza Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Artur Brasil Fialho Rodrigues, 2023. "Predicting Recessions in (almost) Real Time in a Big-data Setting," Working Papers Series 587, Central Bank of Brazil, Research Department.
    7. Abdou, Hussein A. & Elamer, Ahmed A. & Abedin, Mohammad Zoynul & Ibrahim, Bassam A., 2024. "The impact of oil and global markets on Saudi stock market predictability: A machine learning approach," Energy Economics, Elsevier, vol. 132(C).
    8. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    9. Wang, Xuerui & Li, Xiangyu & Li, Shaoting, 2022. "Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm," Applied Energy, Elsevier, vol. 328(C).
    10. Gnekpe, Christian & Tchuente, Dieudonné & Nyawa, Serge & Dey, Prasanta Kumar, 2024. "Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches," Journal of Business Research, Elsevier, vol. 183(C).
    11. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).

  3. Angelo Mont’Alverne Duarte & Wagner Piazza Gaglianone & Osmani Teixeira de Carvalho Guillén & João Victor Issler, 2020. "Commodity Prices and Global Economic Activity: a derived-demand approach," Working Papers Series 539, Central Bank of Brazil, Research Department.

    Cited by:

    1. Ernest Owusu Boakye & Kari Heimonen & Juha Junttila, 2024. "Commodity markets and the global macroeconomy: evidence from machine learning and GVAR," Empirical Economics, Springer, vol. 67(5), pages 1919-1965, November.
    2. Ahmet Tunc, 2024. "Reevaluating the Time-varying Safe Haven Status of Precious Metals: Novel Insights from Economic Policy Uncertainties in the USA and China," Politická ekonomie, Prague University of Economics and Business, vol. 2024(6), pages 958-984.
    3. Rabeh KHALFAOUI & Giray Gozgor & Larisa Yarovaya, 2023. "Global supply chain pressure and commodity markets: Evidence from multiple wavelet and quantile connectedness analyses," Post-Print hal-04144035, HAL.
    4. Ouyang, Ruolan & Pei, Tiancheng & Fang, Yi & Zhao, Yang, 2024. "Commodity systemic risk and macroeconomic predictions," Energy Economics, Elsevier, vol. 138(C).
    5. Afees A. Salisu & Dinci J. Penzin & Xuan Vinh Vo, 2024. "Global economic contraction, climate change and the gold market volatility: A GARCH‐MIDAS approach," Australian Economic Papers, Wiley Blackwell, vol. 63(4), pages 712-728, December.
    6. 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).
    7. Awaworyi-Churchill, Sefa & Inekwe, John & Ivanovski, Kris & Smyth, Russell, 2022. "Breaks, trends and correlations in commodity prices in the very long-run," Energy Economics, Elsevier, vol. 108(C).
    8. Souza, Rodrigo da Silva & Fry-McKibbin, Renée, 2021. "Global liquidity and commodity market interactions: Macroeconomic effects on a commodity exporting emerging market," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 781-800.
    9. Aktham Maghyereh & Hussein Abdoh, 2022. "Can news-based economic sentiment predict bubbles in precious metal markets?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    10. Mehmet Balcilar & Ojonugwa Usman & Busra Agan, 2024. "On the connectedness of commodity markets: A critical and selective survey of empirical studies and bibliometric analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 38(1), pages 97-136, February.

  4. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2019. "Expectations Anchoring Indexes for Brazil using Kalman Filter: exploring signals of inflation anchoring in the long term," Working Papers Series 497, Central Bank of Brazil, Research Department.

    Cited by:

    1. Helder Ferreira de Mendonça & João Pedro Neves Maia, 2022. "Interest rate expectations based on Taylor rule versus central bank’s survey: which performs better in a large emerging economy?," Applied Economics, Taylor & Francis Journals, vol. 54(39), pages 4532-4544, August.
    2. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    3. Marta Baltar Moreira Areosa & Wagner Piazza Gaglianone, 2023. "Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models," Working Papers Series 574, Central Bank of Brazil, Research Department.
    4. Bicchal, Motilal, 2022. "Central bank credibility and its effect on stabilization," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 73-94.
    5. de Mendonça, Helder Ferreira & Díaz, Raime Rolando Rodríguez, 2023. "Can ignorance about the interest rate and macroeconomic surprises affect the stock market return? Evidence from a large emerging economy," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    6. Helder Ferreira de Mendonça & Pedro Mendes Garcia & José Valentim Machado Vicente, 2021. "Rationality and anchoring of inflation expectations: An assessment from survey‐based and market‐based measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1027-1053, September.

  5. Wagner Piazza Gaglianone & Raffaella Giacomini & João Victor Issler & Vasiliki Skreta, 2018. "Incentive-driven Inattention," Working Papers Series 485, Central Bank of Brazil, Research Department.

    Cited by:

    1. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    2. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," SciencePo Working papers Main hal-03878692, HAL.
    3. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    4. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.
    5. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    6. Roc Armenter & Michèle Müller-Itten & Zachary Strangebye, 2021. "Rational Inattention via Ignorance Equivalence," Working Papers 21-29, Federal Reserve Bank of Philadelphia.
    7. Marta Baltar Moreira Areosa & Wagner Piazza Gaglianone, 2023. "Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models," Working Papers Series 574, Central Bank of Brazil, Research Department.
    8. Zidong An & Salem Abo‐Zaid & Xuguang Simon Sheng, 2023. "Inattention and the impact of monetary policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 623-643, June.
    9. Roc Armenter & Michèle Müller-Itten & Zachary Strangebye, 2021. "Geometric Methods for Finite Rational Inattention," Working Papers 21-30, Federal Reserve Bank of Philadelphia.
    10. 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).

  6. Alessandra Pasqualina Viola & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo Pinto & Wagner Piazza Gaglianone, 2017. "Predicting Exchange Rate Volatility in Brazil: an approach using quantile autoregression," Working Papers Series 466, Central Bank of Brazil, Research Department.

    Cited by:

  7. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.

    Cited by:

    1. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2020. "Expectations anchoring indexes for Brazil using Kalman filter: Exploring signals of inflation anchoring in the long term," International Economics, CEPII research center, issue 163, pages 72-91.
    2. Juan Camilo Anzoategui-Zapata & Juan Camilo Galvis-Ciro, 2021. "Effects of fiscal credibility on inflation expectations: evidence from an emerging economy," Public Sector Economics, Institute of Public Finance, vol. 45(1), pages 125-148.

  8. Flávio de Freitas Val & Wagner Piazza Gaglianone & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo Pinto, 2017. "Estimating the Credibility of Brazilian Monetary Policy using Forward Measures and a State-Space Model," Working Papers Series 463, Central Bank of Brazil, Research Department.

    Cited by:

    1. Ribeiro, Gustavo & Teles, Vladmir & Costa-Filho, João, 2023. "The Spending Cap and Monetary Policy Effectiveness," MPRA Paper 116148, University Library of Munich, Germany.
    2. Cem Cakmakli & Selva Demiralp, 2020. "A Dynamic Evaluation of Central Bank Credibility," Koç University-TUSIAD Economic Research Forum Working Papers 2015, Koc University-TUSIAD Economic Research Forum.
    3. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.
    4. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2020. "Expectations anchoring indexes for Brazil using Kalman filter: Exploring signals of inflation anchoring in the long term," International Economics, CEPII research center, issue 163, pages 72-91.
    5. Dizioli, Allan & Wang, Hou, 2024. "How do adaptive learning expectations rationalize stronger monetary policy response in Brazil?," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(1).

  9. Wagner Piazza Gaglianone & Waldyr Dutra Areosa, 2016. "Financial Conditions Indicators for Brazil," Working Papers Series 435, Central Bank of Brazil, Research Department.

    Cited by:

    1. Carrillo Julio A. & García Ana Laura, 2021. "The COVID-19 Economic Crisis in Mexico through the Lens of a Financial Conditions Index," Working Papers 2021-23, Banco de México.
    2. Simone Auer, 2017. "A Financial Conditions Index for the CEE economies," Temi di discussione (Economic working papers) 1145, Bank of Italy, Economic Research and International Relations Area.

  10. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2016. "Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation," Working Papers Series 436, Central Bank of Brazil, Research Department.

    Cited by:

    1. 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).

  11. 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.

    Cited by:

    1. Dimitrios Sarris & Evangelos Spiliotis & Vassilios Assimakopoulos, 2020. "Exploiting resampling techniques for model selection in forecasting: an empirical evaluation using out-of-sample tests," Operational Research, Springer, vol. 20(2), pages 701-721, June.
    2. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
    3. Aristidou, Chrystalleni & Lee, Kevin & Shields, Kalvinder, 2022. "Fundamentals, regimes and exchange rate forecasts: Insights from a meta exchange rate model," Journal of International Money and Finance, Elsevier, vol. 123(C).
    4. Iddrisu, Abdul-Aziz & Alagidede, Imhotep Paul, 2020. "Monetary policy and food inflation in South Africa: A quantile regression analysis," Food Policy, Elsevier, vol. 91(C).
    5. Jaqueline Terra Moura Marins, 2024. "Predictability of Exchange Rate Density Forecasts for Emerging Economies in the Short Run," Working Papers Series 588, Central Bank of Brazil, Research Department.
    6. José Valentim Machado Vicente & Jaqueline Terra Moura Marins & Wagner Piazza Gaglianone, 2021. "Impacts of the Monetary Policy Committee Decisions on the Foreign Exchange Rate in Brazil," Working Papers Series 552, Central Bank of Brazil, Research Department.

  12. Yara de Almeida Campos Cordeiro & Wagner Piazza Gaglianone & João Victor Issler, 2015. "Inattention in Individual Expectations," Working Papers Series 395, Central Bank of Brazil, Research Department.
    • Yara de Almeida Campos Cordeiro & Wagner Piazza Gaglianone & João Victor Issler, 2017. "Inattention in individual expectations," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 17(1), pages 40-59.

    Cited by:

    1. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.
    2. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    3. Waldyr D Areosa, 2016. "What drives inflation expectations in Brazil? Public versus private information," BIS Working Papers 544, Bank for International Settlements.

  13. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.

    Cited by:

    1. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    2. 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).
    3. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).

  14. Luiz Renato Regis de Oliveira Lima & Wagner Piazza Gaglianone, 2012. "Constructing Optimal Density Forecasts from Point Forecast Combinations," Série Textos para Discussão (Working Papers) 5, Programa de Pós-Graduação em Economia - PPGE, Universidade Federal da Paraíba.

    Cited by:

    1. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2021. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Working Papers 2021-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. 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.
    3. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    4. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    5. Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.
    6. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    7. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    8. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
    9. Giovanni Bonaccolto & Massimiliano Caporin & Rangan Gupta, 2015. "The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk," Working Papers 201564, University of Pretoria, Department of Economics.
    10. Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2023. "Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model," CEPR Discussion Papers 18549, C.E.P.R. Discussion Papers.
    11. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    12. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
    13. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
    14. Wagner Piazza Gaglianone & Waldyr Dutra Areosa, 2016. "Financial Conditions Indicators for Brazil," Working Papers Series 435, Central Bank of Brazil, Research Department.
    15. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    16. 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.
    17. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," MPRA Paper 64341, University Library of Munich, Germany.
    18. Fernando Eguren-Martin & Andrej Sokol, 2022. "Attention to the Tail(s): Global Financial Conditions and Exchange Rate Risks," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(3), pages 487-519, September.
    19. 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.
    20. Verena Monschang & Bernd Wilfling, 2022. "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers 9722, Center for Quantitative Economics (CQE), University of Muenster.
    21. Luiz Renato Lima & Lucas Lúcio Godeiro, 2023. "Equity‐premium prediction: Attention is all you need," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 105-122, January.
    22. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    23. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    24. Iddrisu, Abdul-Aziz & Alagidede, Imhotep Paul, 2020. "Monetary policy and food inflation in South Africa: A quantile regression analysis," Food Policy, Elsevier, vol. 91(C).
    25. Laurent Ferrara & Joseph Yapi, 2020. "Measuring exchange rate risks during periods of uncertainty," CAMA Working Papers 2020-60, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    26. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
    27. Fabio Busetti & Michele Caivano & Lisa Rodano, 2015. "On the conditional distribution of euro area inflation forecast," Temi di discussione (Economic working papers) 1027, Bank of Italy, Economic Research and International Relations Area.
    28. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    29. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.
    30. Vegard Høghaug Larsen & Nicolò Maffei-Faccioli & Laura Pagenhardt, 2023. "Where do they care? The ECB in the media and inflation expectations," Working Papers No 04/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    31. González-Ordiano, Jorge Ángel & Mühlpfordt, Tillmann & Braun, Eric & Liu, Jianlei & Çakmak, Hüseyin & Kühnapfel, Uwe & Düpmeier, Clemens & Waczowicz, Simon & Faulwasser, Timm & Mikut, Ralf & Hagenmeye, 2021. "Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow," Applied Energy, Elsevier, vol. 302(C).
    32. James Mitchell & Saeed Zaman, 2023. "The Distributional Predictive Content of Measures of Inflation Expectations," Working Papers 23-31, Federal Reserve Bank of Cleveland.

  15. Luiz Awazu Pereira da Silva & Ricardo Eyer Harris, 2012. "Financial Stability in Brazil," Working Papers Series 289, Central Bank of Brazil, Research Department.

    Cited by:

    1. Pierre-Richard Agénor & Luiz A. Pereira da Silva, 2013. "Inflation Targeting and Financial Stability: A Perspective from the Developing World," Working Papers Series 324, Central Bank of Brazil, Research Department.
    2. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
    3. Wagner Piazza Gaglianone & Waldyr Dutra Areosa, 2016. "Financial Conditions Indicators for Brazil," Working Papers Series 435, Central Bank of Brazil, Research Department.
    4. Patrick Fontaine Reis De Araujo & André De Melo Modenesi & Norberto Montani Martins & Ruy Lyrio Modenesi, 2016. "Restructuring The Economic Policy Framework In Brazil: Genuine Or Gattopardo Change?," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 014, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    5. Bruno Martins, 2012. "Local Market Structure and Bank Competition: evidence from the Brazilian auto loan market," Working Papers Series 299, Central Bank of Brazil, Research Department.
    6. International Monetary Fund, 2013. "Brazil: Technical Note on Macroprudential Policy Framework," IMF Staff Country Reports 2013/148, International Monetary Fund.
    7. Fabia A. de Carvalho & Marcos R. de Castro, 2015. "Macroprudential and Monetary Policy Interaction: a Brazilian perspective," Working Papers Series 405, Central Bank of Brazil, Research Department.

  16. Ricardo Schechtman & Wagner Piazza Gaglianone, 2011. "Macro Stress Testing of Credit Risk Focused on the Tails," Working Papers Series 241, Central Bank of Brazil, Research Department.

    Cited by:

    1. Benjamin M. Tabak & M. Takami & J. M. C. Rocha & Daniel O. Cajueiro, 2011. "Directed Clustering Coefficient as a Measure of Systemic Risk in Complex Banking Networks," Working Papers Series 249, Central Bank of Brazil, Research Department.
    2. Ruja, Catalin, 2014. "Macro Stress-Testing Credit Risk in Romanian Banking System," MPRA Paper 58244, University Library of Munich, Germany.
    3. Miguel Ángel Morales Mosquera & Wilmar Cabrera & Laura Capera & Dairo Estrada, 2012. "Un Mapa de Riesgo de Crédito para el Sistema Financiero Colombiano," Temas de Estabilidad Financiera 068, Banco de la Republica de Colombia.
    4. Moreno, Ramón, 2011. "La formulación de políticas desde una perspectiva macroprudencial en economías emergentes," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 22, pages 21-40.
    5. José Renato Haas Ornelas & José Santiago Fajardo Barbachan & Aquiles Rocha de Farias, 2012. "Estimating Relative Risk Aversion, Risk-Neutral and Real-World Densities using Brazilian Real Currency Options," Working Papers Series 269, Central Bank of Brazil, Research Department.
    6. Adam Gersl & Petr Jakubik & Tomas Konecny & Jakub Seidler, 2013. "Dynamic Stress Testing: The Framework for Assessing the Resilience of the Banking Sector Used by the Czech National Bank," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(6), pages 505-536, December.
    7. Buncic, Daniel & Melecky, Martin, 2013. "Macroprudential stress testing of credit risk: A practical approach for policy makers," Journal of Financial Stability, Elsevier, vol. 9(3), pages 347-370.
    8. Aline B. Schuh & Pascoal José Marion Filho & Daniel Arruda Coronel, 2019. "Determinants of the Default Rate of Individual Clients in Brazil and the Role of Payroll Loans," Economics Bulletin, AccessEcon, vol. 39(1), pages 395-408.
    9. Abildgren, Kim, 2014. "Far out in the tails – The historical distributions of macro-financial risk factors in Denmark," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2014(1), pages 1-31.
    10. Francisco Covas & Ben Rump & Egon Zakrajšek, 2013. "Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach," Finance and Economics Discussion Series 2013-55, Board of Governors of the Federal Reserve System (U.S.).
    11. Kanas, Angelos & Molyneux, Philip, 2018. "Macro stress testing the U.S. banking system," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 204-227.
    12. Grundke, Peter & Pliszka, Kamil & Tuchscherer, Michael, 2019. "Model and estimation risk in credit risk stress tests," Discussion Papers 09/2019, Deutsche Bundesbank.
    13. Pliszka, Kamil, 2021. "System-wide and banks' internal stress tests: Regulatory requirements and literature review," Discussion Papers 19/2021, Deutsche Bundesbank.
    14. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    15. Wong, Alfred Y-T. & Fong, Tom Pak Wing, 2011. "Analysing interconnectivity among economies," Emerging Markets Review, Elsevier, vol. 12(4), pages 432-442.
    16. Reserve Bank of India RBI, 2012. "Financial Stability Report Issue No. 5," Working Papers id:5123, eSocialSciences.
    17. Siemsen, Thomas & Vilsmeier, Johannes, 2018. "On a quest for robustness: About model risk, randomness and discretion in credit risk stress tests," Discussion Papers 31/2018, Deutsche Bundesbank.
    18. Luiz Awazu Pereira da Silva & Ricardo Eyer Harris, 2012. "Financial Stability in Brazil," Working Papers Series 289, Central Bank of Brazil, Research Department.
    19. Saidane, Dhafer & Sène, Babacar & Désiré Kanga, Kouamé, 2021. "Pan-African banks, banking interconnectivity: A new systemic risk measure in the WAEMU," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    20. Matthias Fischer & Daniel Kraus & Marius Pfeuffer & Claudia Czado, 2017. "Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression," Risks, MDPI, vol. 5(3), pages 1-13, July.
    21. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.

  17. Wagner P. Gaglianone & Luiz Renato Lima & Oliver Linton, 2008. "Evaluating Value-at-Risk Models via Quantile Regressions," Working Papers Series 161, Central Bank of Brazil, Research Department.

    Cited by:

    1. Elena-Ivona DUMITRESCU, 2011. "Backesting Value-at-Risk: From DQ (Dynamic Quantile) to DB (Dynamic Binary) Tests," LEO Working Papers / DR LEO 262, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    2. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    3. Nupur Moni Das & Bhabani Sankar Rout, 2024. "Equity Price Risk of Commercial Banks in India," Arthaniti: Journal of Economic Theory and Practice, , vol. 23(2), pages 179-201, December.
    4. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    5. Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
    6. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    7. Steven Kou & Xianhua Peng, 2014. "On the Measurement of Economic Tail Risk," Papers 1401.4787, arXiv.org, revised Aug 2015.
    8. So Yeon Chun & Alexander Shapiro & Stan Uryasev, 2012. "Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics," Operations Research, INFORMS, vol. 60(4), pages 739-756, August.
    9. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    10. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2015. "Systemic risk and asymmetric responses in the financial industry," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 471-485.
    11. Jenq-Tzong Shiau & Jia-Wei Lin, 2016. "Clustering Quantile Regression-Based Drought Trends in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1053-1069, February.
    12. Sullivan Hu'e & Christophe Hurlin & Yang Lu, 2024. "Backtesting Expected Shortfall: Accounting for both duration and severity with bivariate orthogonal polynomials," Papers 2405.02012, arXiv.org, revised May 2024.
    13. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    14. Jenq-Tzong Shiau & Ting-Ju Chen, 2015. "Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2805-2818, June.
    15. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    16. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    17. Wang, Keli & Liu, Xiaoquan & Ye, Wuyi, 2023. "Intraday VaR: A copula-based approach," Journal of Empirical Finance, Elsevier, vol. 74(C).
    18. Tjeerd de Vries, 2021. "A Tale of Two Tails: A Model-free Approach to Estimating Disaster Risk Premia and Testing Asset Pricing Models," Papers 2105.08208, arXiv.org, revised Oct 2023.
    19. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
    20. Sebastian Bayer & Timo Dimitriadis, 2018. "Regression Based Expected Shortfall Backtesting," Papers 1801.04112, arXiv.org, revised Sep 2019.
    21. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    22. Emese Lazar & Ning Zhang, 2017. "Model Risk of Expected Shortfall," ICMA Centre Discussion Papers in Finance icma-dp2017-10, Henley Business School, University of Reading.
    23. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    24. Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019. "Network quantile autoregression," Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
    25. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    26. Bonga-Bonga, Lumengo & Manguzvane, Mathias Mandla, 2018. "Assessing the extent of contagion of sovereign credit risk among BRICS countries," MPRA Paper 89200, University Library of Munich, Germany.
    27. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    28. De Rezende, Rafael B., 2015. "Risks in macroeconomic fundamentals and excess bond returns predictability," Working Paper Series 295, Sveriges Riksbank (Central Bank of Sweden).
    29. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
    30. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
    31. 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.
    32. Ophélie Couperier & Jérémy Leymarie, 2020. "Backtesting Expected Shortfall via Multi-Quantile Regression," Working Papers halshs-01909375, HAL.
    33. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    34. Hotta, Luiz Koodi & Trucíos Maza, Carlos César & Pereira, Pedro L. Valls & Zevallos Herencia, Mauricio Henrique, 2024. "Forecasting VaR and ES through Markov-switching GARCH models: does the specication matter?," Textos para discussão 567, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    35. David Kohns & Tibor Szendrei, 2020. "Horseshoe Prior Bayesian Quantile Regression," Papers 2006.07655, arXiv.org, revised Mar 2021.
    36. Marius Galabe Sampid & Haslifah M Hasim & Hongsheng Dai, 2018. "Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-33, June.
    37. Liu, Xiaochun, 2017. "An integrated macro-financial risk-based approach to the stressed capital requirement," Review of Financial Economics, Elsevier, vol. 34(C), pages 86-98.
    38. Gilbert Colletaz & Christophe Hurlin & Christophe Pérignon, 2012. "The Risk Map: A New Tool for Validating Risk Models," Working Papers halshs-00746273, HAL.
    39. 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.
    40. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    41. Bruno Ferreira Frascaroli & Wellington Charles Lacerda Nobrega, 2019. "Inflation Targeting and Inflation Risk in Latin America," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(11), pages 2389-2408, September.
    42. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Working Papers halshs-00671658, HAL.
    43. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    44. Karmakar, Madhusudan & Paul, Samit, 2019. "Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 699-709.
    45. Fengler, Matthias R. & Herwartz, Helmut, 2015. "Measuring spot variance spillovers when (co)variances are time-varying – the case of multivariate GARCH models," Economics Working Paper Series 1517, University of St. Gallen, School of Economics and Political Science.
    46. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
    47. 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.
    48. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jul 2024.
    49. Liu Xiaochun & Luger Richard, 2018. "Markov-switching quantile autoregression: a Gibbs sampling approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(2), pages 1, April.
    50. Taylor, James W., 2022. "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, vol. 140(C).
    51. Hayette Gatfaoui, 2017. "Equity market information and credit risk signaling: A quantile cointegrating regression approach," Post-Print hal-01745285, HAL.
    52. Pradhan, Ashis Kumar & Tiwari, Aviral Kumar, 2021. "Estimating the market risk of clean energy technologies companies using the expected shortfall approach," Renewable Energy, Elsevier, vol. 177(C), pages 95-100.
    53. Daniel Mariño Ustacara & Luis Fernando Melo Velandia, 2016. "Regresión Cuantílica Dinámica para la Medición del Valor en Riesgo: una Aplicación a Datos Colombianos," Borradores de Economia 939, Banco de la Republica de Colombia.
    54. 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.
    55. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    56. Aramonte, Sirio & Giudice Rodriguez, Marius del & Wu, Jason, 2013. "Dynamic factor Value-at-Risk for large heteroskedastic portfolios," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4299-4309.
    57. Steven Kou & Xianhua Peng, 2016. "On the Measurement of Economic Tail Risk," Operations Research, INFORMS, vol. 64(5), pages 1056-1072, October.
    58. Fresoli, Diego Eduardo, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," DES - Working Papers. Statistics and Econometrics. WS ws140202, Universidad Carlos III de Madrid. Departamento de Estadística.
    59. Katherine Uylangco & Siqiwen Li, 2016. "An evaluation of the effectiveness of Value-at-Risk (VaR) models for Australian banks under Basel III," Australian Journal of Management, Australian School of Business, vol. 41(4), pages 699-718, November.
    60. Chao Wang & Qian Chen & Richard Gerlach, 2017. "Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution," Papers 1707.03715, arXiv.org.
    61. Iqbal, Javed, 2017. "Does gold hedge stock market, inflation and exchange rate risks? An econometric investigation," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 1-17.
    62. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    63. Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
    64. Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    65. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    66. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    67. Sebastian Bayer & Timo Dimitriadis, 2022. "Regression-Based Expected Shortfall Backtesting [Backtesting Expected Shortfall]," Journal of Financial Econometrics, Oxford University Press, vol. 20(3), pages 437-471.
    68. Storti, Giuseppe & Wang, Chao, 2022. "A multivariate semi-parametric portfolio risk optimization and forecasting framework," MPRA Paper 115266, University Library of Munich, Germany.
    69. Chao Wang & Richard Gerlach & Qian Chen, 2018. "A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework," Papers 1807.02422, arXiv.org, revised Jan 2021.
    70. Lúcio Godeiro, Lucas, 2012. "Estimando o VaR (Value-at-Risk) de carteiras via modelos da família GARCH e via Simulação de Monte Carlo [Estimating the VaR (Value-at-Risk) of portfolios via GARCH family models and via Monte Carl," MPRA Paper 45146, University Library of Munich, Germany.
    71. Gerlach, Richard & Abeywardana, Sachin, 2016. "Variational Bayes for assessment of dynamic quantile forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1385-1402.
    72. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
    73. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    74. Timo Dimitriadis & Sebastian Bayer, 2017. "A Joint Quantile and Expected Shortfall Regression Framework," Papers 1704.02213, arXiv.org, revised Aug 2017.
    75. Armstrong, Christopher S. & Blouin, Jennifer L. & Jagolinzer, Alan D. & Larcker, David F., 2015. "Corporate governance, incentives, and tax avoidance," Journal of Accounting and Economics, Elsevier, vol. 60(1), pages 1-17.
    76. Chan Jennifer So Kuen & Ng Kok-Haur & Nitithumbundit Thanakorn & Peiris Shelton, 2019. "Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-22, April.
    77. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    78. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    79. Giessing, Alexander & He, Xuming, 2019. "On the predictive risk in misspecified quantile regression," Journal of Econometrics, Elsevier, vol. 213(1), pages 235-260.
    80. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
    81. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.
    82. Richard Gerlach & Chao Wang, 2018. "Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures," Papers 1805.08653, arXiv.org.
    83. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.

  18. Carlos Enrique Carrasco Gutierrez & Wagner Piazza Gaglianone, 2008. "Evaluating Asset Pricing Models in a Fama-French Framework," Working Papers Series 175, Central Bank of Brazil, Research Department.

    Cited by:

    1. Edgardo Cayón, 2014. "The Effects of Contagion During the Global Financial Crisis in Government-Regulated and Sponsored Assets in Emerging Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2014, January-A.

  19. Sin, Hui Lok & Gaglianone, Wagner Piazza, 2006. "Stochastic simulation of a DSGE model for Brazil," MPRA Paper 20853, University Library of Munich, Germany.

    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.
    2. Andrzej Cieślik & Jan Teresiński, 2020. "Comparing business cycles in the Eurozone and in Poland: a Bayesian DSGE approach," Bank i Kredyt, Narodowy Bank Polski, vol. 51(4), pages 317-366.
    3. 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).
    4. Aneta Hryckiewicz & Lukasz Kozlowski, 2020. "Should we be afraid of powerful banks? The trade-off between bank power and liquidity buffer," Bank i Kredyt, Narodowy Bank Polski, vol. 51(4), pages 437-466.

  20. Lima, Luiz Renato Regis de Oliveira & Sampaio, Raquel Menezes Bezerra & Gaglianone, Wagner Piazza, 2006. "Debt ceiling and fiscal sustainability in Brazil: a quantile autoregression approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 631, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    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. Mário Jorge Mendonça & Cláudio H. dos Santos, 2008. "Revisitando a Função de Reação Fiscal no Brasil Pós-Real: Uma Abordagem de Mudanças de Regime," Discussion Papers 1337, Instituto de Pesquisa Econômica Aplicada - IPEA.
    3. María del Carmen Ramos-Herrera & Simón Sosvilla-Rivero, 2020. "Fiscal Sustainability in Aging Societies: Evidence from Euro Area Countries," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    4. Tilak Abeysinghe & Ananda Jayawickrama, 2013. "A segmented trend model to assess fiscal sustainability: The US experience 1929–2009," Empirical Economics, Springer, vol. 44(3), pages 1129-1141, June.
    5. Mário Jorge Cardoso de Mendonça & Cláudio Hamilton Matos dos Santos, 2008. "Revisitando a Função de Reação Fiscal no Brasil Pós-Real: Uma Abordagem de Mudanças de Regime," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807171729460, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    6. Boengiu, Tudor & Morar Triandafil, Cristina & Morar Triandafil, Adrian, 2011. "Debt Ceiling and External Debt Sustainability in Romania: A Quantile Autoregression Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 15-29, December.
    7. Abderrahim Chibi & Sidi Mohamed Chekouri & Mohamed Benbouziane, 2019. "The dynamics of fiscal policy in Algeria: sustainability and structural change," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-27, December.
    8. 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.
    9. Chang, Chun-Ping & Lee, Chien-Chiang & Hsieh, Meng-Chi, 2015. "Does globalization promote real output? Evidence from quantile cointegration regression," Economic Modelling, Elsevier, vol. 44(C), pages 25-36.
    10. Qing Xu & Terry Childs, 2013. "Evaluating forecast performances of the quantile autoregression models in the present global crisis in international equity markets," Applied Financial Economics, Taylor & Francis Journals, vol. 23(2), pages 105-117, January.
    11. Johann Bröthaler & Michael Getzner & Gottfried Haber, 2015. "Sustainability of local government debt: a case study of Austrian municipalities," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 521-546, August.
    12. Abel Cadenillas & Ricardo Huamán-Aguilar, 2016. "Explicit formula for the optimal government debt ceiling," Annals of Operations Research, Springer, vol. 247(2), pages 415-449, December.
    13. Lee, Chien-Chiang & Lee, Cheng-Feng & Lee, Chi-Chuan, 2014. "Asymmetric dynamics in REIT prices: Further evidence based on quantile regression analysis," Economic Modelling, Elsevier, vol. 42(C), pages 29-37.
    14. Lee, Cheng-Feng & Hu, Te-Chung & Li, Ping-Cheng & Tsong, Ching-Chuan, 2013. "Asymmetric behavior of unemployment rates: Evidence from the quantile covariate unit root test," Japan and the World Economy, Elsevier, vol. 28(C), pages 72-84.
    15. Ricardo Ramalhete Moreira, 2017. "Pro-cyclical fiscal policy in Brazil: long- and short-term relationships using cointegration and error correction model (2005-2015)," International Journal of Economic Policy in Emerging Economies, Inderscience Enterprises Ltd, vol. 10(2), pages 171-184.
    16. Lee, Chien-Chiang & Zeng, Jhih-Hong, 2011. "Revisiting the relationship between spot and futures oil prices: Evidence from quantile cointegrating regression," Energy Economics, Elsevier, vol. 33(5), pages 924-935, September.
    17. Lin, Wen-Yuan & Tsai, I-Chun, 2019. "Black swan events in China's stock markets: Intraday price behaviors on days of volatility," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 395-411.
    18. Zhu, Hui-Ming & Li, ZhaoLai & You, WanHai & Zeng, Zhaofa, 2015. "Revisiting the asymmetric dynamic dependence of stock returns: Evidence from a quantile autoregression model," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 142-153.
    19. Sidi Mohammed Chekouri & Abderrahim Chibi & Mohamed Benbouziane, 2024. "Public debt dynamics and fiscal sustainability in selected North African countries: new evidence from recurrent explosive behavior tests and quantile unit root analysis," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-27, April.

  21. Lima, Luiz Renato Regis de Oliveira & Sampaio, Raquel Menezes Bezerra & Gaglianone, Wagner Piazza, 2005. "Limite de endividamento e sustentabilidade fiscal no Brasil: uma abordagem via modelo quantílico auto-regressivo (QAR)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 602, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Aloisio Araujo & Mário R. Páscoa & Juan Pablo Torres-Martínez, 2006. "Bubbles, Collateral and Monetary Equilibrium," Levine's Working Paper Archive 122247000000001055, David K. Levine.
    2. Monteiro, Paulo Klinger, 2009. "First-price auction symmetric equilibria with a general distribution," Games and Economic Behavior, Elsevier, vol. 65(1), pages 256-269, January.
    3. Cysne, Rubens Penha, 2006. "Income inequality in a job-search model with heterogeneous discount factors: (revised version, forthcoming 2006, Revista Economia)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 611, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    4. Renato G. Flôres & Maria Paula Fontoura & Rogério Guerra Santos, 2007. "Foreign Direct Investment Spillovers in Portugal: Additional Lessons from a Country Study," The European Journal of Development Research, Taylor and Francis Journals, vol. 19(3), pages 372-390.
    5. Cysne, Rubens Penha, 2006. "An intra-household approach to the welfare costs of inflation (Revised Version, Forthcoming 2006, Estudos Econômicos)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 612, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    6. Cavalcanti, Ricardo de Oliveira & Wallace, Neil, 2006. "New models of old(?) payment questions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 619, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    7. Flôres Junior, Renato Galvão, 2006. "Dois ensaios sobre diversidade cultural e o comércio de serviços," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 622, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    8. Flôres Junior, Renato Galvão, 2006. "The diversity of diversity: further methodological considerations on the use of the concept in cultural economics," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 626, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    9. Flôres Junior, Renato Galvão & Watanuki, Masakazu, 2006. "Integration options for mercosul - an investigation Uusing the AMIDA Model," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 610, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

Articles

  1. 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).
    See citations under working paper version above.
  2. Gaglianone, Wagner Piazza & Giacomini, Raffaella & Issler, João Victor & Skreta, Vasiliki, 2022. "Incentive-driven inattention," Journal of Econometrics, Elsevier, vol. 231(1), pages 188-212.
    See citations under working paper version above.
  3. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).
    See citations under working paper version above.
  4. 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).
    See citations under working paper version above.
  5. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2020. "Expectations anchoring indexes for Brazil using Kalman filter: Exploring signals of inflation anchoring in the long term," International Economics, CEPII research center, issue 163, pages 72-91.
    See citations under working paper version above.
  6. 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.

    Cited by:

    1. Oloko, Tirimisiyu F. & Ogbonna, Ahamuefula E. & Adedeji, Abdulfatai A. & Lakhani, Noman, 2021. "Fractional cointegration between gold price and inflation rate: Implication for inflation rate persistence," Resources Policy, Elsevier, vol. 74(C).
    2. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2020. "Expectations anchoring indexes for Brazil using Kalman filter: Exploring signals of inflation anchoring in the long term," International Economics, CEPII research center, issue 163, pages 72-91.
    3. Yadavindu Ajit & Taniya Ghosh, 2024. "A Historical perspective on India's inflation persistence: A Quantile analysis," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2024-015, Indira Gandhi Institute of Development Research, Mumbai, India.
    4. Granville, Brigitte & Zeng, Ning, 2019. "Time variation in inflation persistence: New evidence from modelling US inflation," Economic Modelling, Elsevier, vol. 81(C), pages 30-39.
    5. Zhenxin Wang & Shaoping Wang & Yayi Yan, 2024. "Sieve Bootstrap for Fixed-b Phillips–Perron Unit Root Test," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3181-3205, December.
    6. Canepa, Alessandra, 2024. "Inflation dynamics and persistence: The importance of the uncertainty channel," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    7. Oluwasegun B. Adekoya, 2021. "Persistence and efficiency of OECD stock markets: linear and nonlinear fractional integration approaches," Empirical Economics, Springer, vol. 61(3), pages 1415-1433, September.
    8. 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).
    9. Devpura, Neluka & Sharma, Susan Sunila & Harischandra, P.K.G. & Pathberiya, Lasitha R.C., 2021. "Is inflation persistent? Evidence from a time-varying unit root model," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    10. Oloko, Tirimisiyu F. & Ogbonna, Ahamuefula E. & Adedeji, Abdulfatai A. & Lakhani, Noman, 2021. "Oil price shocks and inflation rate persistence: A Fractional Cointegration VAR approach," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 259-275.
    11. Sidi Mohammed Chekouri & Abderrahim Chibi & Mohamed Benbouziane, 2024. "Public debt dynamics and fiscal sustainability in selected North African countries: new evidence from recurrent explosive behavior tests and quantile unit root analysis," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-27, April.
    12. de Oliveira, Guilherme, 2023. "On the utilization controversy in the demand-led growth literature: A quantile unit root approach," Economic Modelling, Elsevier, vol. 126(C).

  7. de Freitas Val, Flávio & Klotzle, Marcelo Cabus & Pinto, Antonio Carlos Figueiredo & Gaglianone, Wagner Piazza, 2017. "Estimating the credibility of Brazilian monetary policy using a Kalman filter approach," Research in International Business and Finance, Elsevier, vol. 41(C), pages 37-53.

    Cited by:

    1. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2020. "Expectations anchoring indexes for Brazil using Kalman filter: Exploring signals of inflation anchoring in the long term," International Economics, CEPII research center, issue 163, pages 72-91.
    2. Marta Baltar Moreira Areosa & Wagner Piazza Gaglianone, 2023. "Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models," Working Papers Series 574, Central Bank of Brazil, Research Department.

  8. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    See citations under working paper version above.
  9. Yara de Almeida Campos Cordeiro & Wagner Piazza Gaglianone & João Victor Issler, 2017. "Inattention in individual expectations," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 17(1), pages 40-59.
    See citations under working paper version above.
  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.
    See citations under working paper version above.
  11. Wagner Piazza Gaglianone & Luiz Renato Lima, 2014. "Constructing Optimal Density Forecasts From Point Forecast Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 736-757, August.
    See citations under working paper version above.
  12. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.

    Cited by:

    1. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2021. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Working Papers 2021-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. 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.
    3. Mihail Yanchev, 2023. "Uncertainty - Definition and Classification for the Task of Economic Forecasting," Bulgarian Economic Papers bep-2023-03, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St Kliment Ohridski, revised Mar 2023.
    4. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    5. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    6. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.
    8. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    9. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    10. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
    11. Giovanni Bonaccolto & Massimiliano Caporin & Rangan Gupta, 2015. "The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk," Working Papers 201564, University of Pretoria, Department of Economics.
    12. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    13. Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2023. "Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model," CEPR Discussion Papers 18549, C.E.P.R. Discussion Papers.
    14. Francisco Covas & Ben Rump & Egon Zakrajšek, 2013. "Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach," Finance and Economics Discussion Series 2013-55, Board of Governors of the Federal Reserve System (U.S.).
    15. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    16. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
    17. De Rezende, Rafael B., 2015. "Risks in macroeconomic fundamentals and excess bond returns predictability," Working Paper Series 295, Sveriges Riksbank (Central Bank of Sweden).
    18. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
    19. Wagner Piazza Gaglianone & Waldyr Dutra Areosa, 2016. "Financial Conditions Indicators for Brazil," Working Papers Series 435, Central Bank of Brazil, Research Department.
    20. Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    21. 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.
    22. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," MPRA Paper 64341, University Library of Munich, Germany.
    23. Fernando Eguren-Martin & Andrej Sokol, 2022. "Attention to the Tail(s): Global Financial Conditions and Exchange Rate Risks," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(3), pages 487-519, September.
    24. 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.
    25. Dimitris Korobilis & Maximilian Schröder, 2023. "Probabilistic Quantile Factor Analysis," Working Papers No 05/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    26. Milan Szabo, 2020. "Growth-at-Risk: Bayesian Approach," Working Papers 2020/3, Czech National Bank.
    27. 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.
    28. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    29. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    30. Iddrisu, Abdul-Aziz & Alagidede, Imhotep Paul, 2020. "Monetary policy and food inflation in South Africa: A quantile regression analysis," Food Policy, Elsevier, vol. 91(C).
    31. Laurent Ferrara & Joseph Yapi, 2020. "Measuring exchange rate risks during periods of uncertainty," CAMA Working Papers 2020-60, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    32. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
    33. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    34. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.
    35. Vegard Høghaug Larsen & Nicolò Maffei-Faccioli & Laura Pagenhardt, 2023. "Where do they care? The ECB in the media and inflation expectations," Working Papers No 04/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    36. González-Ordiano, Jorge Ángel & Mühlpfordt, Tillmann & Braun, Eric & Liu, Jianlei & Çakmak, Hüseyin & Kühnapfel, Uwe & Düpmeier, Clemens & Waczowicz, Simon & Faulwasser, Timm & Mikut, Ralf & Hagenmeye, 2021. "Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow," Applied Energy, Elsevier, vol. 302(C).
    37. Yuzhi Cai & Guodong Li, 2018. "A novel approach to modelling the distribution of financial returns," Working Papers 2018-22, Swansea University, School of Management.
    38. James Mitchell & Saeed Zaman, 2023. "The Distributional Predictive Content of Measures of Inflation Expectations," Working Papers 23-31, Federal Reserve Bank of Cleveland.

  13. Schechtman, Ricardo & Gaglianone, Wagner Piazza, 2012. "Macro stress testing of credit risk focused on the tails," Journal of Financial Stability, Elsevier, vol. 8(3), pages 174-192.
    See citations under working paper version above.
  14. Carlos Enrique Carrasco-Gutierrez & Wagner Piazza Gaglianone, 2012. "Evaluating Asset Pricing Models in a Simulated Multifactor Approach," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(4), pages 425-460.

    Cited by:

    1. Carrasco Gutierrez, Carlos Enrique & Peixoto Messias, Iasmin Emillyn, 2022. "Macroeconomic factors and value and growth strategies: evidence from Brazil," MPRA Paper 114875, University Library of Munich, Germany.

  15. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    See citations under working paper version above.
  16. Lima, Luiz Renato & Gaglianone, Wagner Piazza & Sampaio, Raquel M.B., 2008. "Debt ceiling and fiscal sustainability in Brazil: A quantile autoregression approach," Journal of Development Economics, Elsevier, vol. 86(2), pages 313-335, June.
    See citations under working paper version above.

Chapters

  1. Luiz Awazu Pereira da Silva & Adriana Soares Sales & Wagner Piazza Gaglianone, 2013. "Financial stability in Brazil," Chapters, in: Andreas Dombret & Otto Lucius (ed.), Stability of the Financial System, chapter 4, Edward Elgar Publishing.
    See citations under working paper version above.
  2. Carlos Hamilton V Araujo & Wagner P Gaglianone, 2010. "Survey-based inflation expectations in Brazil," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 107-114, Bank for International Settlements.

    Cited by:

    1. Leilane de Freitas Rocha Cambara & Roberto Meurer, Gilberto Tadeu Lima, 2019. "Deviating from Perfect Foresight but not from Theoretical Consistency: The Behavior of Inflation Expectations in Brazil," Working Papers, Department of Economics 2019_36, University of São Paulo (FEA-USP).
    2. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.
    3. Ramon Moreno & Agustin Villar, 2010. "Inflation expectations, persistence and monetary policy," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 77-92, Bank for International Settlements.
    4. Cambara, Leilane de Freitas Rocha & Meurer, Roberto & Lima, Gilberto Tadeu, 2022. "Deviating from full rationality but not from theoretical consistency: The behavior of inflation expectations in Brazil," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 492-501.
    5. Vicente da Gama Machado & Marcelo Savino Portugal, 2014. "Phillips curve in Brazil: an unobserved components approach," Working Papers Series 354, Central Bank of Brazil, Research Department.
    6. Juan Camilo Anzoategui-Zapata & Juan Camilo Galvis-Ciro, 2021. "Effects of fiscal credibility on inflation expectations: evidence from an emerging economy," Public Sector Economics, Institute of Public Finance, vol. 45(1), pages 125-148.

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