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Bezirgen Veliyev

Personal Details

First Name:Bezirgen
Middle Name:
Last Name:Veliyev
Suffix:
RePEc Short-ID:pve315
[This author has chosen not to make the email address public]
https://sites.google.com/site/bezirgenveliyev

Affiliation

Center for Research in Econometric Analysis of Time Series (CREATES)
Institut for Økonomi
Aarhus Universitet

Aarhus, Denmark
http://www.creates.au.dk/
RePEc:edi:creaudk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Timmermann, Allan & Christensen, Kim & Veliyev, Bezirgen, 2023. "Warp Speed Price Moves: Jumps after Earnings Announcements," CEPR Discussion Papers 18032, C.E.P.R. Discussion Papers.
  2. Bent Jesper Christensen & Mads Markvart Kjær & Bezirgen Veliyev, 2021. "The incremental information in the yield curve about future interest rate risk," CREATES Research Papers 2021-11, Department of Economics and Business Economics, Aarhus University.
  3. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
  4. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "A GMM approach to estimate the roughness of stochastic volatility," Papers 2010.04610, arXiv.org, revised Apr 2022.
  5. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.
  6. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "Roughness in spot variance? A GMM approach for estimation of fractional log-normal stochastic volatility models using realized measures," CREATES Research Papers 2020-12, Department of Economics and Business Economics, Aarhus University.
  7. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Functional Sequential Treatment Allocation with Covariates," Papers 2001.10996, arXiv.org.
  8. Kim Christensen & Martin Thyrsgaard & Bezirgen Veliyev, 2018. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," CREATES Research Papers 2018-19, Department of Economics and Business Economics, Aarhus University.
  9. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2018. "Functional Sequential Treatment Allocation," Papers 1812.09408, arXiv.org, revised Aug 2020.
  10. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2018. "Edgeworth expansion for Euler approximation of continuous diffusion processes," CREATES Research Papers 2018-28, Department of Economics and Business Economics, Aarhus University.
  11. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
  12. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.
  13. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2015. "Edgeworth expansion for the pre-averaging estimator," CREATES Research Papers 2015-60, Department of Economics and Business Economics, Aarhus University.
  14. Christian Bayer & Bezirgen Veliyev, 2012. "Utility Maximization in a Binomial Model with transaction costs: a Duality Approach Based on the Shadow Price Process," Papers 1209.5175, arXiv.org.
  15. Mathias Beiglbock & Walter Schachermayer & Bezirgen Veliyev, 2010. "A Direct Proof of the Bichteler--Dellacherie Theorem and Connections to Arbitrage," Papers 1004.5559, arXiv.org.

Articles

  1. Bolko, Anine E. & Christensen, Kim & Pakkanen, Mikko S. & Veliyev, Bezirgen, 2023. "A GMM approach to estimate the roughness of stochastic volatility," Journal of Econometrics, Elsevier, vol. 235(2), pages 745-778.
  2. Christensen, Bent Jesper & Kjær, Mads Markvart & Veliyev, Bezirgen, 2023. "The incremental information in the yield curve about future interest rate risk," Journal of Banking & Finance, Elsevier, vol. 155(C).
  3. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2023. "Treatment recommendation with distributional targets," Journal of Econometrics, Elsevier, vol. 234(2), pages 624-646.
  4. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
  5. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2022. "Functional Sequential Treatment Allocation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1311-1323, September.
  6. Christensen, Kim & Thyrsgaard, Martin & Veliyev, Bezirgen, 2019. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," Journal of Econometrics, Elsevier, vol. 212(2), pages 556-583.
  7. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
  8. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
  9. Ulrich Hounyo & Bezirgen Veliyev, 2016. "Validity of Edgeworth expansions for realized volatility estimators," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
  10. Christian Bayer & Bezirgen Veliyev, 2014. "Utility Maximization In A Binomial Model With Transaction Costs: A Duality Approach Based On The Shadow Price Process," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-27.
  11. Beiglböck, Mathias & Schachermayer, Walter & Veliyev, Bezirgen, 2012. "A short proof of the Doob–Meyer theorem," Stochastic Processes and their Applications, Elsevier, vol. 122(4), pages 1204-1209.

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.

    Mentioned in:

    1. Machine Learning for Realized Volatility Forecasting
      by Francis Diebold in No Hesitations on 2021-02-01 12:16:00

Working papers

  1. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    2. Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022. "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers 2205.07719, arXiv.org.
    3. Timothé Gronier & William Maréchal & Christophe Geissler & Stéphane Gibout, 2022. "Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control," Energies, MDPI, vol. 16(1), pages 1-17, December.
    4. Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
    5. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    6. M. Shabani & M. Magris & George Tzagkarakis & J. Kanniainen & A. Iosifidis, 2023. "Predicting the state of synchronization of financial time series using cross recurrence plots," Post-Print hal-04415269, HAL.
    7. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.
    8. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
    9. Robert Stok & Paul Bilokon, 2023. "From Deep Filtering to Deep Econometrics," Papers 2311.06256, arXiv.org.
    10. Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022. "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, vol. 60(C).
    11. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).

  2. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "A GMM approach to estimate the roughness of stochastic volatility," Papers 2010.04610, arXiv.org, revised Apr 2022.

    Cited by:

    1. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    2. Julien Guyon & Jordan Lekeufack, 2023. "Volatility is (mostly) path-dependent," Quantitative Finance, Taylor & Francis Journals, vol. 23(9), pages 1221-1258, September.
    3. Angelini, Daniele & Bianchi, Sergio, 2023. "Nonlinear biases in the roughness of a Fractional Stochastic Regularity Model," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    4. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
    5. Mikkel Bennedsen & Kim Christensen & Peter Christensen, 2024. "Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility," Papers 2403.12653, arXiv.org.
    6. Li, Yicun & Teng, Yuanyang, 2023. "Statistical inference in discretely observed fractional Ornstein–Uhlenbeck processes," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    7. Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Minimax Theory," Papers 2210.01214, arXiv.org, revised Feb 2024.
    8. Peter Christensen, 2024. "Roughness Signature Functions," Papers 2401.02819, arXiv.org.
    9. Xiyue Han & Alexander Schied, 2023. "Estimating the roughness exponent of stochastic volatility from discrete observations of the realized variance," Papers 2307.02582, arXiv.org, revised Aug 2023.
    10. Ofelia Bonesini & Antoine Jacquier & Alexandre Pannier, 2023. "Rough volatility, path-dependent PDEs and weak rates of convergence," Papers 2304.03042, arXiv.org.
    11. Alexandre Pannier, 2023. "Path-dependent PDEs for volatility derivatives," Papers 2311.08289, arXiv.org, revised Jan 2024.
    12. Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Central limit theorems," Papers 2210.01216, arXiv.org, revised Jul 2023.
    13. Saad Mouti, 2023. "Rough volatility: evidence from range volatility estimators," Papers 2312.01426, arXiv.org.

  3. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.

    Cited by:

    1. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
    2. Anders Bredahl Kock & David Preinerstorfer, 2024. "Regularizing Discrimination in Optimal Policy Learning with Distributional Targets," Papers 2401.17909, arXiv.org.

  4. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "Roughness in spot variance? A GMM approach for estimation of fractional log-normal stochastic volatility models using realized measures," CREATES Research Papers 2020-12, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Blanka Horvath & Josef Teichmann & Žan Žurič, 2021. "Deep Hedging under Rough Volatility," Risks, MDPI, vol. 9(7), pages 1-20, July.
    2. Mathieu Rosenbaum & Jianfei Zhang, 2022. "On the universality of the volatility formation process: when machine learning and rough volatility agree," Papers 2206.14114, arXiv.org.
    3. Blanka Horvath & Josef Teichmann & Zan Zuric, 2021. "Deep Hedging under Rough Volatility," Papers 2102.01962, arXiv.org.
    4. Wu, Peng & Muzy, Jean-François & Bacry, Emmanuel, 2022. "From rough to multifractal volatility: The log S-fBM model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

  5. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Functional Sequential Treatment Allocation with Covariates," Papers 2001.10996, arXiv.org.

    Cited by:

    1. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org.
    2. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    3. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.
    4. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  6. Kim Christensen & Martin Thyrsgaard & Bezirgen Veliyev, 2018. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," CREATES Research Papers 2018-19, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Sigurd Emil Rømer & Rolf Poulsen, 2020. "How Does the Volatility of Volatility Depend on Volatility?," Risks, MDPI, vol. 8(2), pages 1-18, June.
    2. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "A GMM approach to estimate the roughness of stochastic volatility," Papers 2010.04610, arXiv.org, revised Apr 2022.
    3. Viktor Todorov & Yang Zhang, 2022. "Information gains from using short‐dated options for measuring and forecasting volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 368-391, March.
    4. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    5. Yinfen Tang & Tao Su & Zhiyuan Zhang, 2022. "Distribution-free specification test for volatility function based on high-frequency data with microstructure noise," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(8), pages 977-1022, November.

  7. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2018. "Functional Sequential Treatment Allocation," Papers 1812.09408, arXiv.org, revised Aug 2020.

    Cited by:

    1. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org.
    2. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
    3. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    4. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
    5. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    6. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.
    7. Maximilian Kasy & Anja Sautmann, 2019. "Adaptive Treatment Assignment in Experiments for Policy Choice," CESifo Working Paper Series 7778, CESifo.
    8. Toru Kitagawa & Guanyi Wang, 2020. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP59/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Anders Bredahl Kock & David Preinerstorfer, 2024. "Regularizing Discrimination in Optimal Policy Learning with Distributional Targets," Papers 2401.17909, arXiv.org.
    10. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Functional Sequential Treatment Allocation with Covariates," Papers 2001.10996, arXiv.org.

  8. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2018. "Edgeworth expansion for Euler approximation of continuous diffusion processes," CREATES Research Papers 2018-28, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Ciprian A. Tudor & Nakahiro Yoshida, 2020. "Asymptotic expansion of the quadratic variation of a mixed fractional Brownian motion," Statistical Inference for Stochastic Processes, Springer, vol. 23(2), pages 435-463, July.
    2. Elisa Alòs & Masaaki Fukasawa, 2021. "The asymptotic expansion of the regular discretization error of Itô integrals," Mathematical Finance, Wiley Blackwell, vol. 31(1), pages 323-365, January.

  9. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. He, Lidan & Liu, Qiang & Liu, Zhi, 2020. "Edgeworth corrections for spot volatility estimator," Statistics & Probability Letters, Elsevier, vol. 164(C).
    2. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    3. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2015. "Edgeworth expansion for the pre-averaging estimator," CREATES Research Papers 2015-60, Department of Economics and Business Economics, Aarhus University.
    4. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    5. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.

  10. Kim Christensen & Mark Podolskij & Nopporn Thamrongrat & Bezirgen Veliyev, 2015. "Inference from high-frequency data: A subsampling approach," CREATES Research Papers 2015-45, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    2. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    3. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    4. Takaki Hayashi & Yuta Koike, 2017. "Multi-scale analysis of lead-lag relationships in high-frequency financial markets," Papers 1708.03992, arXiv.org, revised May 2020.
    5. Mathias Vetter, 2021. "A universal approach to estimate the conditional variance in semimartingale limit theorems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(6), pages 1089-1125, December.
    6. Fabian Mies & Ansgar Steland, 2019. "Nonparametric Gaussian inference for stable processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(3), pages 525-555, October.
    7. Kim Christensen & Mikkel Slot Nielsen & Mark Podolskij, 2023. "High-dimensional estimation of quadratic variation based on penalized realized variance," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 331-359, July.
    8. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.

  11. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2015. "Edgeworth expansion for the pre-averaging estimator," CREATES Research Papers 2015-60, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Mark Podolskij & Bezirgen Veliyev & Nakahiro Yoshida, 2018. "Edgeworth expansion for Euler approximation of continuous diffusion processes," CREATES Research Papers 2018-28, Department of Economics and Business Economics, Aarhus University.
    2. Yamagishi, Hayate & Yoshida, Nakahiro, 2023. "Order estimate of functionals related to fractional Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 161(C), pages 490-543.
    3. Ciprian A. Tudor & Nakahiro Yoshida, 2020. "Asymptotic expansion of the quadratic variation of a mixed fractional Brownian motion," Statistical Inference for Stochastic Processes, Springer, vol. 23(2), pages 435-463, July.
    4. Yoshida, Nakahiro, 2023. "Asymptotic expansion and estimates of Wiener functionals," Stochastic Processes and their Applications, Elsevier, vol. 157(C), pages 176-248.
    5. Tudor, Ciprian A. & Yoshida, Nakahiro, 2023. "High order asymptotic expansion for Wiener functionals," Stochastic Processes and their Applications, Elsevier, vol. 164(C), pages 443-492.

  12. Mathias Beiglbock & Walter Schachermayer & Bezirgen Veliyev, 2010. "A Direct Proof of the Bichteler--Dellacherie Theorem and Connections to Arbitrage," Papers 1004.5559, arXiv.org.

    Cited by:

    1. Christoph Kuhn & Bjorn Ulbricht, 2013. "Modeling capital gains taxes for trading strategies of infinite variation," Papers 1309.7368, arXiv.org, revised Jun 2015.
    2. Constantinos Kardaras, 2011. "On the closure in the Emery topology of semimartingale wealth-process sets," Papers 1108.0945, arXiv.org, revised Jul 2013.
    3. Dániel Ágoston Bálint & Martin Schweizer, 2019. "Properly Discounted Asset Prices Are Semimartingales," Swiss Finance Institute Research Paper Series 19-53, Swiss Finance Institute.
    4. Vladimir Vovk, 2012. "Continuous-time trading and the emergence of probability," Finance and Stochastics, Springer, vol. 16(4), pages 561-609, October.
    5. Kardaras, Constantinos, 2013. "On the closure in the Emery topology of semimartingale wealth-process sets," LSE Research Online Documents on Economics 44996, London School of Economics and Political Science, LSE Library.
    6. Dániel Ágoston Bálint & Martin Schweizer, 2018. "Making No-Arbitrage Discounting-Invariant: A New FTAP Beyond NFLVR and NUPBR," Swiss Finance Institute Research Paper Series 18-23, Swiss Finance Institute, revised Mar 2018.
    7. Christoph Kuhn & Alexander Molitor, 2020. "Semimartingale price systems in models with transaction costs beyond efficient friction," Papers 2001.03190, arXiv.org, revised Aug 2021.

Articles

  1. Bolko, Anine E. & Christensen, Kim & Pakkanen, Mikko S. & Veliyev, Bezirgen, 2023. "A GMM approach to estimate the roughness of stochastic volatility," Journal of Econometrics, Elsevier, vol. 235(2), pages 745-778.
    See citations under working paper version above.
  2. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2023. "Treatment recommendation with distributional targets," Journal of Econometrics, Elsevier, vol. 234(2), pages 624-646.
    See citations under working paper version above.
  3. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
    See citations under working paper version above.
  4. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2022. "Functional Sequential Treatment Allocation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1311-1323, September.
    See citations under working paper version above.
  5. Christensen, Kim & Thyrsgaard, Martin & Veliyev, Bezirgen, 2019. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," Journal of Econometrics, Elsevier, vol. 212(2), pages 556-583.
    See citations under working paper version above.
  6. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    See citations under working paper version above.
  7. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
    See citations under working paper version above.
  8. Ulrich Hounyo & Bezirgen Veliyev, 2016. "Validity of Edgeworth expansions for realized volatility estimators," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    See citations under working paper version above.
  9. Beiglböck, Mathias & Schachermayer, Walter & Veliyev, Bezirgen, 2012. "A short proof of the Doob–Meyer theorem," Stochastic Processes and their Applications, Elsevier, vol. 122(4), pages 1204-1209.

    Cited by:

    1. Neufeld, Ariel & Nutz, Marcel, 2014. "Measurability of semimartingale characteristics with respect to the probability law," Stochastic Processes and their Applications, Elsevier, vol. 124(11), pages 3819-3845.
    2. Christoph Kuhn, 2023. "The fundamental theorem of asset pricing with and without transaction costs," Papers 2307.00571, arXiv.org.
    3. Beiglböck, M. & Siorpaes, P., 2014. "Riemann-integration and a new proof of the Bichteler–Dellacherie theorem," Stochastic Processes and their Applications, Elsevier, vol. 124(3), pages 1226-1235.
    4. Oleksii Mostovyi, 2015. "Necessary and sufficient conditions in the problem of optimal investment with intermediate consumption," Finance and Stochastics, Springer, vol. 19(1), pages 135-159, January.
    5. Oleksii Mostovyi, 2011. "Necessary and sufficient conditions in the problem of optimal investment with intermediate consumption," Papers 1107.5852, arXiv.org, revised Jul 2012.

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 8 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-ECM: Econometrics (4) 2015-05-16 2015-10-04 2015-12-20 2020-11-02
  2. NEP-ETS: Econometric Time Series (3) 2015-05-16 2015-10-04 2020-11-02
  3. NEP-FOR: Forecasting (2) 2021-01-25 2021-07-12
  4. NEP-ORE: Operations Research (2) 2021-01-25 2021-07-12
  5. NEP-BIG: Big Data (1) 2021-01-25
  6. NEP-CMP: Computational Economics (1) 2021-01-25
  7. NEP-EXP: Experimental Economics (1) 2020-06-29
  8. NEP-ICT: Information and Communication Technologies (1) 2015-10-04
  9. NEP-MAC: Macroeconomics (1) 2021-07-12
  10. NEP-MIC: Microeconomics (1) 2012-10-06
  11. NEP-MST: Market Microstructure (1) 2015-05-16
  12. NEP-RMG: Risk Management (1) 2021-07-12
  13. NEP-UPT: Utility Models and Prospect Theory (1) 2021-07-12

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