Piotr Fiszeder : Citation Profile

Are you Piotr Fiszeder?

Uniwersytet Mikolaja Kopernika w Toruniu

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29

Citations

RESEARCH PRODUCTION:

15

Articles

1

Papers

5

Chapters

RESEARCH ACTIVITY:

   16 years (2004 - 2020). See details.
   Cites by year: 1
   Journals where Piotr Fiszeder has often published
   Relations with other researchers
   Recent citing documents: 14.    Total self citations: 6 (17.14 %)

MORE DETAILS IN:
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   Permalink: http://citec.repec.org/pfi197
   Updated: 2022-07-02    RAS profile: 2022-05-14    
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Relations with other researchers

Works with:

Faldzinski, Marcin (3)

Authors registered in RePEc who have co-authored more than one work in the last five years with Piotr Fiszeder.

Is cited by:

Lyócsa, Štefan (6)

Výrost, Tomᚠ(6)

Molnár, Peter (5)

Kliber, Agata (2)

Pierdzioch, Christian (2)

GUPTA, RANGAN (2)

Będowska-Sójka, Barbara (2)

Roca, Eduardo (1)

Bouri, Elie (1)

Lin, Boqiang (1)

Balcilar, Mehmet (1)

Cites to:

Bollerslev, Tim (20)

Engle, Robert (18)

Hansen, Peter (15)

Chou, Ray (13)

Diebold, Francis (12)

Lunde, Asger (8)

Jagannathan, Ravi (7)

Andersen, Torben (7)

Granger, Clive (7)

McAleer, Michael (6)

Molnár, Peter (6)

Main data

Where Piotr Fiszeder has published?

Journals with more than one article published# docs
Dynamic Econometric Models6

Recent works citing Piotr Fiszeder (2022 and 2021)

YearTitle of citing document
2021Predicting risk in energy markets: Low-frequency data still matter. (2021). Výrost, Tomᚠ; Vrost, Toma ; Todorova, Neda ; Lyocsa, Tefan. In: Applied Energy. RePEc:eee:appene:v:282:y:2021:i:pa:s0306261920315567.

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2021Stock market volatility forecasting: Do we need high-frequency data?. (2021). Molnár, Peter ; Lyócsa, Štefan ; Vrost, Toma ; Molnar, Peter ; Lyocsa, Tefan. In: International Journal of Forecasting. RePEc:eee:intfor:v:37:y:2021:i:3:p:1092-1110.

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2022Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models. (2022). Hou, Chenghan ; Ji, Qiang ; Klein, Tony ; Luo, Jiawen. In: International Journal of Forecasting. RePEc:eee:intfor:v:38:y:2022:i:1:p:51-73.

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2022Large dynamic covariance matrices: Enhancements based on intraday data. (2022). Wolf, Michael ; Ledoit, Olivier ; Engle, Robert F ; de Nard, Gianluca. In: Journal of Banking & Finance. RePEc:eee:jbfina:v:138:y:2022:i:c:s0378426622000267.

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2021Information content of liquidity and volatility measures. (2021). Będowska-Sójka, Barbara ; Bdowska-Sojka, Barbara ; Kliber, Agata. In: Physica A: Statistical Mechanics and its Applications. RePEc:eee:phsmap:v:563:y:2021:i:c:s0378437120307627.

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2021The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method. (2021). Bieszk-Stolorz, Beata ; Landmesser, Joanna ; Dmytrow, Krzysztof. In: Energies. RePEc:gam:jeners:v:14:y:2021:i:13:p:4024-:d:588218.

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2021Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic. (2021). Just, Magorzata ; Echaust, Krzysztof. In: Energies. RePEc:gam:jeners:v:14:y:2021:i:14:p:4147-:d:591470.

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2021Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting. (2021). Orzeszko, Witold. In: Energies. RePEc:gam:jeners:v:14:y:2021:i:19:p:6043-:d:640970.

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2021Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment. (2021). Pierdzioch, Christian ; GUPTA, RANGAN. In: Energies. RePEc:gam:jeners:v:14:y:2021:i:23:p:8085-:d:693917.

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2022Modelling Energy Transition in Germany: An Analysis through Ordinary Differential Equations and System Dynamics. (2022). Guidolin, Mariangela ; de Giovanni, Luigi ; Savio, Andrea. In: Forecasting. RePEc:gam:jforec:v:4:y:2022:i:2:p:25-455:d:790008.

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2022Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns. (2022). della Posta, Pompeo ; Manfredi, Piero ; Guidolin, Mariangela ; Bunea, Anita M. In: Forecasting. RePEc:gam:jforec:v:4:y:2022:i:2:p:26-476:d:798344.

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2022Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach. (2022). Fianu, Emmanuel Senyo. In: Forecasting. RePEc:gam:jforec:v:4:y:2022:i:2:p:30-564:d:836532.

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2022.

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2021El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements. (2021). Pierdzioch, Christian ; GUPTA, RANGAN ; Balcilar, Mehmet ; Bouri, Elie. In: Sustainability. RePEc:gam:jsusta:v:13:y:2021:i:14:p:7987-:d:596011.

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Works by Piotr Fiszeder:

YearTitleTypeCited
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2022Forecasting: theory and practice In: Papers.
[Full Text][Citation analysis]		paper3
2013A new look at variance estimation based on low, high and closing prices taking into account the drift In: Statistica Neerlandica.
[Full Text][Citation analysis]		article4
2011Minimum Variance Portfolio Selection for Large Number of Stocks – Application of Time-Varying Covariance Matrices In: Dynamic Econometric Models.
[Full Text][Citation analysis]		article0
2004Dynamic Hedging Portfolios - Application of Bivariate GARCH Models In: Dynamic Econometric Models.
[Full Text][Citation analysis]		article0
2006Modelling Financial Processes with Long Memory in Mean and Variance In: Dynamic Econometric Models.
[Full Text][Citation analysis]		article0
2006Conformable Models for GARCH Processes In: Dynamic Econometric Models.
[Full Text][Citation analysis]		article0
2008How to Increase Accuracy of Volatility Forecasts Based on GARCH Models In: Dynamic Econometric Models.
[Full Text][Citation analysis]		article0
2008Pricing of Weather Options for Berlin Quoted on the Chicago Mercantile Exchange In: Dynamic Econometric Models.
[Full Text][Citation analysis]		article0
2019Improving forecasts with the co-range dynamic conditional correlation model In: Journal of Economic Dynamics and Control.
[Full Text][Citation analysis]		article4
2019Range-based DCC models for covariance and value-at-risk forecasting In: Journal of Empirical Finance.
[Full Text][Citation analysis]		article5
2016Low and high prices can improve volatility forecasts during periods of turmoil In: International Journal of Forecasting.
[Full Text][Citation analysis]		article6
2012Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices In: Czech Journal of Economics and Finance (Finance a uver).
[Full Text][Citation analysis]		article0
2020Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression In: Energies.
[Full Text][Citation analysis]		article5
2018Exchange Rate Covariance Modelling by Means of Minimum and Maximum Prices (Modelowanie kowariancji kursow walutowych z zastosowaniem cen minimalnych i maksymalnych) In: Problemy Zarzadzania.
[Full Text][Citation analysis]		article0
2018Monetary policy in steering the EONIA and POLONIA rates in the Eurosystem and Poland: a comparative analysis In: Empirical Economics.
[Full Text][Citation analysis]		article1
2018Low and high prices can improve covariance forecasts: The evidence based on currency rates In: Journal of Forecasting.
[Full Text][Citation analysis]		article1

CitEc is a RePEc service, providing citation data for Economics since 2001. Sponsored by INOMICS. Last updated July, 1st 2022. Contact: CitEc Team