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Piotr Fiszeder

Personal Details

First Name:Piotr
Middle Name:
Last Name:Fiszeder
Suffix:
RePEc Short-ID:pfi197

Affiliation

Wydział Nauk Ekonomicznych i Zarządzania
Uniwersytet Mikolaja Kopernika w Toruniu

Toruń, Poland
http://www.econ.uni.torun.pl/

: +48 (56) 621-46-08

ul. Gagarina 13a , 87-100 Toruń
RePEc:edi:wntorpl (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Fiszeder, Piotr & Perczak, Grzegorz, 2016. "Low and high prices can improve volatility forecasts during periods of turmoil," International Journal of Forecasting, Elsevier, vol. 32(2), pages 398-410.
  2. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.
  3. Piotr Fiszeder & Witold Orzeszko, 2012. "Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(5), pages 430-449, November.
  4. Piotr Fiszeder, 2011. "Minimum Variance Portfolio Selection for Large Number of Stocks – Application of Time-Varying Covariance Matrices," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 87-98.
  5. Piotr Fiszeder, 2008. "How to Increase Accuracy of Volatility Forecasts Based on GARCH Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 8, pages 111-118.
  6. Piotr Fiszeder & Juliusz Pres, 2008. "Pricing of Weather Options for Berlin Quoted on the Chicago Mercantile Exchange," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 8, pages 163-170.
  7. Piotr Fiszeder, 2006. "Conformable Models for GARCH Processes," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 7, pages 143-150.
  8. Piotr Fiszeder, 2006. "Modelling Financial Processes with Long Memory in Mean and Variance," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 7, pages 133-142.
  9. Piotr Fiszeder, 2004. "Dynamic Hedging Portfolios - Application of Bivariate GARCH Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 6, pages 203-212.

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.

Articles

  1. Fiszeder, Piotr & Perczak, Grzegorz, 2016. "Low and high prices can improve volatility forecasts during periods of turmoil," International Journal of Forecasting, Elsevier, vol. 32(2), pages 398-410.

    Cited by:

    1. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.

  2. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.

    Cited by:

    1. Aneta Wlodarczyk & Iwona Otola, 2016. "Analysis of the Relationship between Market Volatility and Firms Volatility on the Polish Capital Market," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 16, pages 87-116.
    2. Fiszeder, Piotr & Perczak, Grzegorz, 2016. "Low and high prices can improve volatility forecasts during periods of turmoil," International Journal of Forecasting, Elsevier, vol. 32(2), pages 398-410.
    3. Milton Abdul Thorlie & Lixin Song & Muhammad Amin & Xiaoguang Wang, 2015. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 329-356, August.

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