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Esben Høg
(Esben Hoeg)

Personal Details

First Name:Esben
Middle Name:
Last Name:Hoeg
Suffix:
RePEc Short-ID:phg3

Affiliation

Aalborg Universitet - Institut for Matematiske Fag (Aalborg University - Department of Mathematical Sciences)

http://www.math.aau.dk
Denmark, Aalborg

Research output

as
Jump to: Working papers Articles

Working papers

  1. Høg, Esben, 2008. "Volatility and realized quadratic variation of differenced returns : A wavelet method approach," Finance Research Group Working Papers F-2008-06, University of Aarhus, Aarhus School of Business, Department of Business Studies.
  2. Høg, Esben & Frederiksen, Per & Schiemert, Daniel, 2008. "On the Generalized Brownian Motion and its Applications in Finance," Finance Research Group Working Papers F-2008-07, University of Aarhus, Aarhus School of Business, Department of Business Studies.
  3. Esben Hoeg & Per Frederiksen, 2006. "The Fractional OU Process: Term Structure Theory and Application," Computing in Economics and Finance 2006 194, Society for Computational Economics.
  4. Esben Hoeg, 2005. "Volatility and realized quadratic variation of differenced returns," Computing in Economics and Finance 2005 333, Society for Computational Economics.
  5. Asger Lunde & Esben Hoeg, 2003. "Wavelet Estimation of Integrated Volatility," Computing in Economics and Finance 2003 274, Society for Computational Economics.
  6. Nielsen, Steen & Høg, Esben, 2001. "Dynamic Cost Managenent: Combining ABC, the Theory of the Firm and Cubic Splines," Working Papers 01-9, University of Aarhus, Aarhus School of Business, Department of Business Studies.
  7. Esben Hoeg, 2001. "Estimation of Diffusions using Wavelet scaling methods," Computing in Economics and Finance 2001 255, Society for Computational Economics.

Articles

  1. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
  2. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
  3. Esben Høg & Leonidas Tsiaras, 2011. "Density forecasts of crude‐oil prices using option‐implied and ARCH‐type models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(8), pages 727-754, August.
  4. Aabo, Tom & Høg, Esben & Kuhn, Jochen, 2010. "Integrated foreign exchange risk management: The role of import in medium-sized manufacturing firms," Journal of Multinational Financial Management, Elsevier, vol. 20(4-5), pages 235-250, December.

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. Asger Lunde & Esben Hoeg, 2003. "Wavelet Estimation of Integrated Volatility," Computing in Economics and Finance 2003 274, Society for Computational Economics.

    Cited by:

    1. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    2. Jozef Barunik & Lukas Vacha, 2015. "Realized wavelet-based estimation of integrated variance and jumps in the presence of noise," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1347-1364, August.
    3. Barunik, Jozef & Krehlik, Tomas & Vacha, Lukas, 2016. "Modeling and forecasting exchange rate volatility in time-frequency domain," European Journal of Operational Research, Elsevier, vol. 251(1), pages 329-340.
    4. Wang, Fangfang, 2014. "Optimal design of Fourier estimator in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 708-722.
    5. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Papers 2005-W16, Economics Group, Nuffield College, University of Oxford.
    6. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    7. Winkelmann, Lars, 2013. "Quantitative forward guidance and the predictability of monetary policy: A wavelet based jump detection approach," SFB 649 Discussion Papers 2013-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Lars Winkelmann, 2013. "Quantitative forward guidance and the predictability of monetary policy - A wavelet based jump detection approach -," SFB 649 Discussion Papers SFB649DP2013-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    10. Høg, Esben, 2008. "Volatility and realized quadratic variation of differenced returns : A wavelet method approach," Finance Research Group Working Papers F-2008-06, University of Aarhus, Aarhus School of Business, Department of Business Studies.

Articles

  1. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.

    Cited by:

    1. Thakur, Jagruti & Hesamzadeh, Mohammad Reza & Date, Paresh & Bunn, Derek, 2023. "Pricing and hedging wind power prediction risk with binary option contracts," Energy Economics, Elsevier, vol. 126(C).
    2. Wang, Haiying & Yuan, Ying & Li, Yiou & Wang, Xunhong, 2021. "Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory," Economic Modelling, Elsevier, vol. 94(C), pages 401-414.
    3. Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.
    4. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    5. Spodniak, Petr & Bertsch, Valentin, 2020. "Is flexible and dispatchable generation capacity rewarded in electricity futures markets? A multinational impact analysis," Energy, Elsevier, vol. 196(C).

  2. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.

    Cited by:

    1. Sahamkhadam, Maziar & Stephan, Andreas & Östermark, Ralf, 2018. "Portfolio optimization based on GARCH-EVT-Copula forecasting models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 497-506.
    2. Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020. "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, vol. 85(C).
    3. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    4. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Papers 1801.01093, arXiv.org, revised Nov 2019.
    5. Johannes Kaufmann & Philipp Artur Kienscherf & Wolfgang Ketter, 2020. "Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios," Energies, MDPI, vol. 13(14), pages 1-19, July.
    6. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2018. "A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin," Applied Energy, Elsevier, vol. 210(C), pages 60-74.
    7. Li, Xiafei & Wei, Yu, 2018. "The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method," Energy Economics, Elsevier, vol. 74(C), pages 565-581.
    8. Guo, Peng & Chen, Si & Chu, Jingchun & Infield, David, 2020. "Wind direction fluctuation analysis for wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1026-1035.
    9. Zeng, Sheng & Liu, Xinchun & Li, Xiafei & Wei, Qi & Shang, Yue, 2019. "Information dominance among hedging assets: Evidence from return and volatility directional spillovers in time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    10. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2018. "Joint and conditional dependence modeling of peak district heating demand and outdoor temperature: a copula-based approach," BEMPS - Bozen Economics & Management Paper Series BEMPS53, Faculty of Economics and Management at the Free University of Bozen.
    11. Nguyen, Quynh Nga & Bedoui, Rihab & Majdoub, Najemeddine & Guesmi, Khaled & Chevallier, Julien, 2020. "Hedging and safe-haven characteristics of Gold against currencies: An investigation based on multivariate dynamic copula theory," Resources Policy, Elsevier, vol. 68(C).
    12. M. Akhtaruzzaman & A.K. Banerjee & S. Boubaker & F. Moussa, 2023. "Does Green Improve Portfolio Optimisation?," Post-Print hal-04435509, HAL.
    13. Fabrizio Durante & Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2022. "A Multivariate Dependence Analysis for Electricity Prices, Demand and Renewable Energy Sources," Papers 2201.01132, arXiv.org.
    14. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Dependence structure in the Australian electricity markets: New evidence from regular vine copulae," Energy Economics, Elsevier, vol. 90(C).
    15. Anne Opschoor & Dewi Peerlings & Luca Rossini & Andre Lucas, 2024. "Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution," Tinbergen Institute Discussion Papers 24-049/III, Tinbergen Institute.
    16. Fugui Dong & Xiaohui Ding & Lei Shi, 2019. "Wind Power Pricing Game Strategy under the China’s Market Trading Mechanism," Energies, MDPI, vol. 12(18), pages 1-17, September.

  3. Esben Høg & Leonidas Tsiaras, 2011. "Density forecasts of crude‐oil prices using option‐implied and ARCH‐type models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(8), pages 727-754, August.

    Cited by:

    1. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
    2. Andres Trujillo-Barrera & Philip Garcia & Mindy L Mallory, 2018. "Short-term price density forecasts in the lean hog futures market," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(1), pages 121-142.
    3. Adjemian, Michael K. & Bruno, Valentina G. & Robe, Michel A., 2016. "Forward‐Looking USDA Price Forecasts," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235931, Agricultural and Applied Economics Association.
    4. Peter Christoffersen & Kris Jacobs & Bo Young Chang, 2011. "Forecasting with Option Implied Information," CREATES Research Papers 2011-46, Department of Economics and Business Economics, Aarhus University.
    5. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
    6. Ricardo Crisostomo & Lorena Couso, 2018. "Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes," Papers 1801.08007, arXiv.org, revised May 2018.
    7. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    8. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    9. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.

  4. Aabo, Tom & Høg, Esben & Kuhn, Jochen, 2010. "Integrated foreign exchange risk management: The role of import in medium-sized manufacturing firms," Journal of Multinational Financial Management, Elsevier, vol. 20(4-5), pages 235-250, December.

    Cited by:

    1. Aabo, Tom & Ploeen, Rasmus, 2014. "The German humpback: Internationalization and foreign exchange hedging," Journal of Multinational Financial Management, Elsevier, vol. 27(C), pages 114-129.
    2. Arnold, Matthias M. & Rathgeber, Andreas W. & Stöckl, Stefan, 2014. "Determinants of corporate hedging: A (statistical) meta-analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(4), pages 443-458.
    3. Geyer-Klingeberg, Jerome & Hang, Markus & Rathgeber, Andreas W., 2019. "What drives financial hedging? A meta-regression analysis of corporate hedging determinants," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 203-221.
    4. Aabo, Tom & Pantzalis, Christos & Sørensen, Helle & Toustrup, Malene Teilmann, 2016. "Corporate risk and external sourcing: A study of Scandinavian multinational firms," International Business Review, Elsevier, vol. 25(6), pages 1297-1308.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 4 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-ETS: Econometric Time Series (3) 2001-05-02 2003-10-20 2006-07-15
  2. NEP-ECM: Econometrics (2) 2003-10-20 2009-04-25
  3. NEP-FMK: Financial Markets (1) 2009-04-25
  4. NEP-MAC: Macroeconomics (1) 2006-07-15
  5. NEP-MST: Market Microstructure (1) 2009-04-25

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