IDEAS home Printed from https://ideas.repec.org/f/pfa290.html
   My authors  Follow this author

Marcin Faldzinski

Personal Details

First Name:Marcin
Middle Name:
Last Name:Faldzinski
Suffix:
RePEc Short-ID:pfa290
http://www.marf.com.pl

Affiliation

Wydział Nauk Ekonomicznych i Zarządzania
Uniwersytet Mikolaja Kopernika w Toruniu

Toruń, Poland
http://www.econ.uni.torun.pl/
RePEc:edi:wntorpl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Tomas Meluzin & Marek Zinecker & Michal Bernard Pietrzak & Marcin Faldzinski & Adam P. Balcerzak, 2016. "Interdependence among Capital Markets of Germany, Poland and Baltic States," Working Papers 36/2016, Institute of Economic Research, revised Sep 2016.
  2. Tomas Meluzin & Marek Zinecker & Michal Bernard Pietrzak & Marcin Faldzinski & Adam P. Balcerzak, 2016. "Value-at-Risk with Application of DCC-GARCH Model," Working Papers 35/2016, Institute of Economic Research, revised Sep 2016.
  3. Marek Zinecker & Adam P. Balcerzak & Marcin Faldzinski & Michal Bernad Pietrzak & Tomáš Meluzin, 2016. "Application of DCC-GARCH Model for Analysis of Interrelations Among Capital Markets of Poland, Czech Republic and Germany," Working Papers 4/2016, Institute of Economic Research, revised Feb 2016.
  4. Marcin Faldzinski & Adam P. Balcerzak & Tomas Meluzin & Michal Bernard Pietrzak & Marek Zinecker, 2016. "Cointegration of Interdependencies Among Capital Markets of Chosen Visegrad Countries and Germany," Working Papers 21/2016, Institute of Economic Research, revised May 2016.
  5. Adam P. Balcerzak & Marcin Faldzinski & Michal Bernard Pietrzak & Tomas Meluzin & Marek Zineker, 2015. "Analiza powiazan pomiedzy rynkami kapitalowymi wybranych krajow grupy wyszehradzkiej," Working Papers 167/2015, Institute of Economic Research, revised Dec 2015.
  6. Marcin Faldzinski & Michal Bernard Pietrzak, "undated". "The Multivariate DCC-GARCH Model with Interdependence among Markets in Conditional Variances’ Equations," Working Papers 164/2015, Institute of Economic Research, revised Nov 2015.

Articles

  1. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
  2. Jerzy Boehlke & Marcin Faldzinski & Maciej Galecki & Magdalena Osinska, 2020. "Searching for Factors of Accelerated Economic Growth: The Case of Ireland and Turkey," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 292-304.
  3. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2019. "Range-based DCC models for covariance and value-at-risk forecasting," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 58-76.
  4. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
  5. Marcin Faldzinski & Magdalena Osinska, 2016. "Volatility estimators in econometric analysis of risk transfer on capital markets," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 16, pages 21-35.
  6. Marcin Fałdziński & Magdalena Osińska & Tomasz Zdanowicz, 2012. "Detecting Risk Transfer in Financial Markets using Different Risk Measures," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(1), pages 45-64, March.
  7. Marcin Faldzinski, 2009. "Estimation Of The Probable Maximum Loss Based On Extreme Value Theory For Stock Returns," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 2(1), pages 51-59, June.
  8. Marcin Faldzinski, 2009. "Application of Modified POT Method with Volatility Model for Estimation of Risk Measures," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 119-128.
  9. Magdalena Osinska & Marcin Faldzinski, 2008. "GARCH and SV Models with Application of Extreme Value Theory," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 8, pages 45-52.

Chapters

  1. Marcin Faldzinski & Adam P. Balcerzak & Tomas Meluzin & Michal Bernard Pietrzak & Marek Zinecker, 2016. "Cointegration of Interdependencies Among Capital Markets of Chosen Visegrad Countries and Germany," Chapters, in: 34th International Conference Mathematical Methods in Economics MME 2016 Conference Proceedings, edition 1, volume 1, chapter 33, pages 189-194, Institute of Economic Research.
  2. Marek Zinecker & Adam P. Balcerzak & Marcin Faldzinski & Tomas Meluzin & Michal Bernard Pietrzak, 2016. "Application of DCC-GARCH model for analysis of Interrelations among Capital Markets of Poland, Czech Republic and Germany," Chapters, in: Proceedings of the International Scientific Conference Quantitative Methods in Economics Multiple Criteria Decision Making XVIII, edition 1, volume 1, chapter 67, pages 418-423, Institute of Economic Research.

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. Marek Zinecker & Adam P. Balcerzak & Marcin Faldzinski & Michal Bernad Pietrzak & Tomáš Meluzin, 2016. "Application of DCC-GARCH Model for Analysis of Interrelations Among Capital Markets of Poland, Czech Republic and Germany," Working Papers 4/2016, Institute of Economic Research, revised Feb 2016.

    Cited by:

    1. Robiyanto Robiyanto & Michael Alexander Santoso & Apriani Dorkas Rambu Atahau & Harijono Harijono, 2019. "The Indonesia Stock Exchange and Its Dynamics: An Analysis of the Effect of Macroeconomic Variables," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 15(4), pages 59-73.
    2. Tomas Meluzin & Marek Zinecker, 2016. "Trends In Ipos: The Evidence From Cee Capital Markets," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(2), pages 327-341, June.
    3. Robiyanto Robiyanto & Rihfenti Ernayani & Rendi Susiswo Ismail, 2019. "Formulation Of A Dynamic Portfolio With Stocks And Fixed-Income Instruments In The Indonesian Capital Market," Organizations and Markets in Emerging Economies, Faculty of Economics, Vilnius University, vol. 10(1).

  2. Marcin Faldzinski & Adam P. Balcerzak & Tomas Meluzin & Michal Bernard Pietrzak & Marek Zinecker, 2016. "Cointegration of Interdependencies Among Capital Markets of Chosen Visegrad Countries and Germany," Working Papers 21/2016, Institute of Economic Research, revised May 2016.

    Cited by:

    1. Tomas Meluzin & Marek Zinecker, 2016. "Trends In Ipos: The Evidence From Cee Capital Markets," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(2), pages 327-341, June.
    2. Máté Csiki & Gábor Dávid Kiss, 2018. "Capital Market Contagion in the Stock Markets of Visegrád Countries Based on the Heckman Selection Model," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 17(4), pages 23-52.

  3. Marcin Faldzinski & Michal Bernard Pietrzak, "undated". "The Multivariate DCC-GARCH Model with Interdependence among Markets in Conditional Variances’ Equations," Working Papers 164/2015, Institute of Economic Research, revised Nov 2015.

    Cited by:

    1. Ruiwen Yang & Pathairat Pastpipatkul & Chaiwat Nimanussornkul, 2020. "Dynamic Volatility Spillover Among Chinese Black Series Futures Under Structural Breaks," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 6(5), pages 236-246.

Articles

  1. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.

    Cited by:

    1. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Working Papers 202138, University of Pretoria, Department of Economics.
    2. Witold Orzeszko, 2021. "Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting," Energies, MDPI, vol. 14(19), pages 1-16, September.
    3. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Working Papers 202175, University of Pretoria, Department of Economics.
    4. Krzysztof Dmytrów & Joanna Landmesser & Beata Bieszk-Stolorz, 2021. "The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method," Energies, MDPI, vol. 14(13), pages 1-23, July.
    5. Krzysztof Echaust & Małgorzata Just, 2021. "Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic," Energies, MDPI, vol. 14(14), pages 1-21, July.
    6. Arthur Jin Lin, 2023. "Volatility Contagion from Bulk Shipping and Petrochemical Industries to Oil Futures Market during the Economic Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-19, August.

  2. Jerzy Boehlke & Marcin Faldzinski & Maciej Galecki & Magdalena Osinska, 2020. "Searching for Factors of Accelerated Economic Growth: The Case of Ireland and Turkey," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 292-304.

    Cited by:

    1. Katarzyna Liczmańska-Kopcewicz & Paula Pypłacz & Agnieszka Wiśniewska, 2020. "Resonance of Investments in Renewable Energy Sources in Industrial Enterprises in the Food Industry," Energies, MDPI, vol. 13(17), pages 1-20, August.
    2. Bogdan Klepacki & Barbara Kusto & Piotr Bórawski & Aneta Bełdycka-Bórawska & Konrad Michalski & Aleksandra Perkowska & Tomasz Rokicki, 2021. "Investments in Renewable Energy Sources in Basic Units of Local Government in Rural Areas," Energies, MDPI, vol. 14(11), pages 1-17, May.
    3. Maciej Ryczkowski, 2021. "Money and inflation in inflation-targeting regimes – new evidence from time–frequency analysis," Journal of Applied Economics, Taylor & Francis Journals, vol. 24(1), pages 17-44, January.
    4. Elyas Abdulahi Mohamued & Masood Ahmed & Paula Pypłacz & Katarzyna Liczmańska-Kopcewicz & Muhammad Asif Khan, 2021. "Global Oil Price and Innovation for Sustainability: The Impact of R&D Spending, Oil Price and Oil Price Volatility on GHG Emissions," Energies, MDPI, vol. 14(6), pages 1-18, March.
    5. Karol Kujawa, 2021. "Security Challenges in Relations between Turkey and the European Union," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 4), pages 98-111.

  3. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2019. "Range-based DCC models for covariance and value-at-risk forecasting," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 58-76.

    Cited by:

    1. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    2. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    3. Enoksen, F.A. & Landsnes, Ch.J. & Lučivjanská, K. & Molnár, P., 2020. "Understanding risk of bubbles in cryptocurrencies," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 129-144.
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Piotr Fiszeder & Marta Ma³ecka, 2022. "Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(4), pages 939-967, December.
    6. Li, Jingpeng & Umar, Muhammad & Huo, Jiale, 2023. "The spillover effect between Chinese crude oil futures market and Chinese green energy stock market," Energy Economics, Elsevier, vol. 119(C).
    7. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
    8. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
    9. Ana Alzate-Ortega & Natalia Garzón & Jesús Molina-Muñoz, 2024. "Volatility Spillovers in Emerging Markets: Oil Shocks, Energy, Stocks, and Gold," Energies, MDPI, vol. 17(2), pages 1-19, January.
    10. Lai, Yu-Sheng, 2022. "Improving hedging performance by using high–low range," Finance Research Letters, Elsevier, vol. 48(C).
    11. Gianluca De Nard & Robert F. Engle & Olivier Ledoit & Michael Wolf, 2020. "Large dynamic covariance matrices: enhancements based on intraday data," ECON - Working Papers 356, Department of Economics - University of Zurich, revised Jan 2022.
    12. Datta, Susanta & Hatekar, Neeraj, 2022. "Range Volatility Spillover across Sectoral Stock Indices during COVID-19 Pandemic: Evidence from Indian Stock Market," MPRA Paper 117285, University Library of Munich, Germany.
    13. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Attention to oil prices and its impact on the oil, gold and stock markets and their covariance," Energy Economics, Elsevier, vol. 120(C).
    14. Ozkan Haykir & Ibrahim Yagli, 2022. "Speculative bubbles and herding in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-33, December.
    15. Wang, Pengfei & Li, Xiao & Shen, Dehua & Zhang, Wei, 2020. "How does economic policy uncertainty affect the bitcoin market?," Research in International Business and Finance, Elsevier, vol. 53(C).
    16. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    17. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
    18. Apostolos Ampountolas, 2022. "Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models," IJFS, MDPI, vol. 10(3), pages 1-22, July.
    19. Fałdziński, Marcin & Fiszeder, Piotr & Molnár, Peter, 2024. "Improving volatility forecasts: Evidence from range-based models," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    20. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

  4. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).

    Cited by:

    1. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Enoksen, F.A. & Landsnes, Ch.J. & Lučivjanská, K. & Molnár, P., 2020. "Understanding risk of bubbles in cryptocurrencies," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 129-144.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Piotr Fiszeder & Marta Ma³ecka, 2022. "Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(4), pages 939-967, December.
    5. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    6. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
    7. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
    8. Lai, Yu-Sheng, 2022. "Improving hedging performance by using high–low range," Finance Research Letters, Elsevier, vol. 48(C).
    9. Paravee Maneejuk & Woraphon Yamaka, 2019. "Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends," Mathematics, MDPI, vol. 7(11), pages 1-29, November.
    10. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Attention to oil prices and its impact on the oil, gold and stock markets and their covariance," Energy Economics, Elsevier, vol. 120(C).
    11. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
    12. Fałdziński, Marcin & Fiszeder, Piotr & Molnár, Peter, 2024. "Improving volatility forecasts: Evidence from range-based models," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    13. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

  5. Marcin Fałdziński & Magdalena Osińska & Tomasz Zdanowicz, 2012. "Detecting Risk Transfer in Financial Markets using Different Risk Measures," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(1), pages 45-64, March.

    Cited by:

    1. Mario Brandtner, 2016. "Spektrale Risikomaße: Konzeption, betriebswirtschaftliche Anwendungen und Fallstricke," Management Review Quarterly, Springer, vol. 66(2), pages 75-115, April.

Chapters

  1. Marcin Faldzinski & Adam P. Balcerzak & Tomas Meluzin & Michal Bernard Pietrzak & Marek Zinecker, 2016. "Cointegration of Interdependencies Among Capital Markets of Chosen Visegrad Countries and Germany," Chapters, in: 34th International Conference Mathematical Methods in Economics MME 2016 Conference Proceedings, edition 1, volume 1, chapter 33, pages 189-194, Institute of Economic Research.
    See citations under working paper version above.
  2. Marek Zinecker & Adam P. Balcerzak & Marcin Faldzinski & Tomas Meluzin & Michal Bernard Pietrzak, 2016. "Application of DCC-GARCH model for analysis of Interrelations among Capital Markets of Poland, Czech Republic and Germany," Chapters, in: Proceedings of the International Scientific Conference Quantitative Methods in Economics Multiple Criteria Decision Making XVIII, edition 1, volume 1, chapter 67, pages 418-423, Institute of Economic Research.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

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 6 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-TRA: Transition Economics (4) 2015-12-20 2016-03-17 2016-05-21 2016-09-11
  2. NEP-ETS: Econometric Time Series (2) 2015-12-01 2016-09-11
  3. NEP-RMG: Risk Management (2) 2016-03-17 2016-09-11
  4. NEP-ECM: Econometrics (1) 2015-12-01

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Marcin Faldzinski should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.