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Ingmar Nolte

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.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2011. "An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 669-707, June.

    Mentioned in:

    1. An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics (Journal of Applied Econometrics 2011) in ReplicationWiki ()

Working papers

  1. Vasios, Michalis & Payne, Richard & Nolte, Ingmar, 2015. "Profiting from Mimicking Strategies in Non-Anonymous Markets," MPRA Paper 61710, University Library of Munich, Germany.

    Cited by:

    1. Benos, Evangelos & Payne, Richard & Vasios, Michalis, 2016. "Centralized trading, transparency and interest rate swap market liquidity: evidence from the implementation of the Dodd-Frank Act," Bank of England working papers 580, Bank of England.

  2. Ingmar Nolte & Valeri Voev, 2009. "Least Squares Inference on Integrated Volatility and the Relationship between Efficient Prices and Noise," CREATES Research Papers 2009-16, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Yuta Koike, 2013. "Limit Theorems for the Pre-averaged Hayashi-Yoshida Estimator with Random Sampling," Global COE Hi-Stat Discussion Paper Series gd12-276, Institute of Economic Research, Hitotsubashi University.
    2. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
    3. Selma Chaker, 2013. "Volatility and Liquidity Costs," Staff Working Papers 13-29, Bank of Canada.
    4. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    5. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
    6. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.

  3. Ingmar Nolte & Valeri Voev, 2008. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CREATES Research Papers 2008-31, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    2. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    3. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
    4. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    5. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    6. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
    7. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.

  4. Bien, Katarzyna & Nolte, Ingmar & Pohlmeier, Winfried, 2007. "An inflated Multivariate Integer Count Hurdle model: An application to bid and ask quote dynamics," CoFE Discussion Papers 07/04, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Gloria Gonzalez‐Rivera & Yun Luo & Esther Ruiz, 2020. "Prediction regions for interval‐valued time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 373-390, June.
    2. Eugenio Miravete, 2014. "Testing for complementarities among countable strategies," Empirical Economics, Springer, vol. 46(4), pages 1521-1544, June.
    3. Yang Lu, 2021. "The predictive distributions of thinning‐based count processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 42-67, March.
    4. Ruben Loaiza-Maya & Michael Stanley Smith, 2017. "Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series," Papers 1712.09150, arXiv.org, revised Jul 2018.
    5. Serge Darolles & Gaëlle Le Fol & Yang Lu & Ran Sun, 2018. "Bivariate integer-autoregressive process with an application to mutual fund flows," Post-Print hal-04590149, HAL.
    6. Pravin Trivedi & David Zimmer, 2017. "A Note on Identification of Bivariate Copulas for Discrete Count Data," Econometrics, MDPI, vol. 5(1), pages 1-11, February.
    7. Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2019. "Dynamic discrete mixtures for high frequency prices," Discussion Papers 19/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    8. Sucarrat, Genaro & Grønneberg, Steffen, 2016. "Models of Financial Return With Time-Varying Zero Probability," MPRA Paper 68931, University Library of Munich, Germany.
    9. Deb P & Trivedi PK & Zimmer DM, 2009. "Dynamic Cost-offsets of Prescription Drug Expenditures: Panel Data Analysis Using a Copula-based Hurdle Model," Health, Econometrics and Data Group (HEDG) Working Papers 09/15, HEDG, c/o Department of Economics, University of York.
    10. González-Rivera, Gloria & Luo, Yun, 2019. "Prediction regions for interval-valued time series," DES - Working Papers. Statistics and Econometrics. WS 29054, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Gunther Wuyts, 2012. "The impact of aggressive orders in an order-driven market: a simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 18(10), pages 1015-1038, November.
    12. Katarzyna Bień-Barkowska, 2012. "A Bivariate Copula-based Model for a Mixed Binary-Continuous Distribution: A Time Series Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(2), pages 117-142, June.
    13. Darolles, Serge & Fol, Gaëlle Le & Lu, Yang & Sun, Ran, 2019. "Bivariate integer-autoregressive process with an application to mutual fund flows," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 181-203.

  5. Lechner, Sandra & Nolte, Ingmar, 2007. "Customer trading in the foreign exchange market empirical evidence from an internet trading platform," CoFE Discussion Papers 07/03, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Michael R. King & Carol Osler & Dagfinn Rime, 2011. "Foreign exchange market structure, players and evolution," Working Paper 2011/10, Norges Bank.
    2. Peter Gomber & Jascha-Alexander Koch & Michael Siering, 2017. "Digital Finance and FinTech: current research and future research directions," Journal of Business Economics, Springer, vol. 87(5), pages 537-580, July.
    3. Nolte, Ingmar & Voev, Valeri, 2007. "Panel intensity models with latent factors: An application to the trading dynamics on the foreign exchange market," CoFE Discussion Papers 07/02, University of Konstanz, Center of Finance and Econometrics (CoFE).
    4. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    5. Carol Osler & Xuhang Wang, 2012. "The Microstructure of Currency Markets," Working Papers 49, Brandeis University, Department of Economics and International Business School.

  6. Bien, Katarzyna & Nolte, Ingmar & Pohlmeier, Winfried, 2006. "A Multivariate Integer Count Hurdle model: Theory and application to exchange rate dynamics," CoFE Discussion Papers 06/06, University of Konstanz, Center of Finance and Econometrics (CoFE).

    Cited by:

    1. Magdalena Osinska & Andrzej Dobrzynski & Yochanan Shachmurove, 2016. "Performance Of American And Russian Joint Stock Companies On Financial Market. A Microstructure Perspective," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 819-851, December.
    2. Katarzyna Bień-Barkowska, 2012. "A Bivariate Copula-based Model for a Mixed Binary-Continuous Distribution: A Time Series Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(2), pages 117-142, June.

Articles

  1. Seok Young Hong & Ingmar Nolte & Stephen J Taylor & Xiaolu Zhao, 2023. "Volatility Estimation and Forecasts Based on Price Durations," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 106-144.

    Cited by:

    1. Roman V. Ivanov, 2023. "On the Stochastic Volatility in the Generalized Black-Scholes-Merton Model," Risks, MDPI, vol. 11(6), pages 1-23, June.
    2. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    3. Zhao, X. & Hong, S. Y. & Linton, O. B., 2024. "Jumps Versus Bursts: Dissection and Origins via a New Endogenous Thresholding Approach," Janeway Institute Working Papers 2423, Faculty of Economics, University of Cambridge.
    4. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    5. Zhao, X. & Hong, S. Y. & Linton, O. B., 2024. "Jumps Versus Bursts: Dissection and Origins via a New Endogenous Thresholding Approach," Cambridge Working Papers in Economics 2449, Faculty of Economics, University of Cambridge.

  2. Rodrigo Hizmeri & Marwan Izzeldin & Ingmar Nolte & Vasileios Pappas, 2022. "A generalized heterogeneous autoregressive model using market information," Quantitative Finance, Taylor & Francis Journals, vol. 22(8), pages 1513-1534, August.

    Cited by:

    1. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).

  3. Torben Andersen & Ilya Archakov & Leon Grund & Nikolaus Hautsch & Yifan Li & Sergey Nasekin & Ingmar Nolte & Manh Cuong Pham & Stephen Taylor & Viktor Todorov, 2021. "A Descriptive Study of High-Frequency Trade and Quote Option Data [Stealth Trading in Options Markets]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 128-177.

    Cited by:

    1. Kanne, Stefan & Korn, Olaf & Uhrig-Homburg, Marliese, 2023. "Stock illiquidity and option returns," Journal of Financial Markets, Elsevier, vol. 63(C).

  4. Li, Yifan & Nolte, Ingmar & Nolte, Sandra, 2021. "High-frequency volatility modeling: A Markov-Switching Autoregressive Conditional Intensity model," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).

    Cited by:

    1. Greeshma Balabhadra & El Mehdi Ainasse & Pawel Polak, 2023. "High-Frequency Volatility Estimation with Fast Multiple Change Points Detection," Papers 2303.10550, arXiv.org, revised Jun 2024.
    2. Endres, Sylvia & Stübinger, Johannes, 2018. "A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns," FAU Discussion Papers in Economics 07/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

  5. David Happersberger & Harald Lohre & Ingmar Nolte, 2020. "Estimating portfolio risk for tail risk protection strategies," European Financial Management, European Financial Management Association, vol. 26(4), pages 1107-1146, September.

    Cited by:

    1. Bruno Spilak & Wolfgang Karl Hardle, 2020. "Tail-risk protection: Machine Learning meets modern Econometrics," Papers 2010.03315, arXiv.org, revised Aug 2021.
    2. Walid Mensi & Mariya Gubareva & Hee-Un Ko & Xuan Vinh Vo & Sang Hoon Kang, 2023. "Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    3. Guohui Guan & Lin He & Zongxia Liang & Litian Zhang, 2024. "Optimal VPPI strategy under Omega ratio with stochastic benchmark," Papers 2403.13388, arXiv.org.
    4. Spilak, Bruno & Härdle, Wolfgang Karl, 2020. "Tail-risk protection: Machine Learning meets modern Econometrics," IRTG 1792 Discussion Papers 2020-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Maysam Khodayari Gharanchaei & Reza Babazadeh, 2024. "Crisis Alpha: A High-Performance Trading Algorithm Tested in Market Downturns," Papers 2409.14510, arXiv.org.
    6. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    7. Pham, Linh & Nguyen, Canh Phuc, 2021. "Asymmetric tail dependence between green bonds and other asset classes," Global Finance Journal, Elsevier, vol. 50(C).
    8. Yang, Yao & Karali, Berna, 2022. "How far is too far for volatility transmission?," Journal of Commodity Markets, Elsevier, vol. 26(C).
    9. Wentao Hu & Cuixia Chen & Yufeng Shi & Ze Chen, 2022. "A Tail Measure With Variable Risk Tolerance: Application in Dynamic Portfolio Insurance Strategy," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 831-874, June.
    10. Hammadi Zouari, 2022. "On the Effectiveness of Stock Index Futures for Tail Risk Protection," International Journal of Economics and Financial Issues, Econjournals, vol. 12(3), pages 38-52, May.

  6. Nolte, Ingmar & Nolte, Sandra & Pohlmeier, Winfried, 2019. "What determines forecasters’ forecasting errors?," International Journal of Forecasting, Elsevier, vol. 35(1), pages 11-24.

    Cited by:

    1. Brückbauer, Frank & Schröder, Michael, 2021. "Data resource profile: The ZEW FMS dataset," ZEW Discussion Papers 21-100, ZEW - Leibniz Centre for European Economic Research.

  7. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.

    Cited by:

    1. Emilia Tomczyk & Barbara Kowalczyk, 2023. "Consensus in Business Tendency Surveys: Comparison of Alternative Measures," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 17-29.
    2. Oliver Grothe & Fabian Kachele & Fabian Kruger, 2022. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Papers 2204.10154, arXiv.org.
    3. Oscar Claveria, 2021. "Uncertainty indicators based on expectations of business and consumer surveys," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(2), pages 483-505, May.
    4. Dimitris Kenourgios & Stephanos Papadamou & Dimitrios Dimitriou & Constantin Zopounidis, 2020. "Modelling the dynamics of unconventional monetary policies’ impact on professionals’ forecasts," Post-Print hal-02880071, HAL.
    5. Oscar Claveria, 2021. "On the Aggregation of Survey-Based Economic Uncertainty Indicators Between Different Agents and Across Variables," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 1-26, April.
    6. Huisman, Ronald & Van der Sar, Nico L. & Zwinkels, Remco C.J., 2021. "Volatility expectations and disagreement," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 379-393.
    7. Nikos Apokoritis & Gabriele Galati & Richhild Moessner & Federica Teppa, 2019. "Inflation expectations anchoring: new insights from micro evidence of a survey at high-frequency and of distributions," BIS Working Papers 809, Bank for International Settlements.
    8. Taeyoung Doh, 2017. "Trend and Uncertainty in the Long-Term Real Interest Rate: Bayesian Exponential Tilting with Survey Data," Research Working Paper RWP 17-8, Federal Reserve Bank of Kansas City.
    9. Gabriele Galati & Richhild Moessner & Maarten van Rooij, 2021. "The anchoring of long-term inflation expectations of consumers: insights from a new survey," BIS Working Papers 936, Bank for International Settlements.
    10. Claveria, Oscar, 2022. "Global economic uncertainty and suicide: Worldwide evidence," Social Science & Medicine, Elsevier, vol. 305(C).
    11. Petar Soric & Oscar Claveria, 2021. ""Employment uncertainty a year after the irruption of the covid-19 pandemic"," IREA Working Papers 202112, University of Barcelona, Research Institute of Applied Economics, revised May 2021.
    12. Alessandro Barbera & Dora Xia & Sonya Zhu, 2023. "The term structure of inflation forecasts disagreement and monetary policy transmission," BIS Working Papers 1114, Bank for International Settlements.
    13. Fabian Krüger, 2017. "Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms," Empirical Economics, Springer, vol. 53(1), pages 235-246, August.
    14. Oscar Claveria, 2020. "Measuring and assessing economic uncertainty," IREA Working Papers 202011, University of Barcelona, Research Institute of Applied Economics, revised Jul 2020.
    15. Teti, Emanuele & Etro, Leonardo L. & Pausini, Lorenzo, 2024. "Does greenwashing affect Company's stock Price? Evidence from Europe," International Review of Financial Analysis, Elsevier, vol. 93(C).
    16. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    17. Petar Soric & Ivana Lolic, 2017. "Economic uncertainty and its impact on the Croatian economy," Public Sector Economics, Institute of Public Finance, vol. 41(4), pages 443-477.
    18. Oscar Claveria, 2020. "Business and consumer uncertainty in the face of the pandemic: A sector analysis in European countries," Papers 2012.02091, arXiv.org.
    19. Oscar Claveria, 2021. "Disagreement on expectations: firms versus consumers," SN Business & Economics, Springer, vol. 1(12), pages 1-23, December.
    20. Gabriele Galati & Richhild Moessner & Maarten van Rooij, 2021. "Anchoring of consumers’ long-term euro area inflation expectations during the pandemic," Working Papers 715, DNB.
    21. Mario Canales & Bernabe Lopez-Martin, 2021. "Uncertainty, Risk, and Price-Setting: Evidence from CPI Microdata," Working Papers Central Bank of Chile 908, Central Bank of Chile.
    22. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    23. Oscar Claveria & Petar Sorić, 2023. "Labour market uncertainty after the irruption of COVID-19," Empirical Economics, Springer, vol. 64(4), pages 1897-1945, April.
    24. Yongchen Zhao, 2022. "Uncertainty and disagreement of inflation expectations: Evidence from household‐level qualitative survey responses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 810-828, July.
    25. Gabriel Caldas Montes & Caio Ferrari Ferreira, 2019. "Does monetary policy credibility mitigate the effects of uncertainty about exchange rate on uncertainties about both inflation and interest rate?," International Economics and Economic Policy, Springer, vol. 16(4), pages 649-678, October.

  8. Ingmar Nolte & Sandra Nolte, 2016. "The information content of retail investors' order flow," The European Journal of Finance, Taylor & Francis Journals, vol. 22(2), pages 80-104, January.

    Cited by:

    1. Pagano, Michael S. & Sedunov, John & Velthuis, Raisa, 2021. "How did retail investors respond to the COVID-19 pandemic? The effect of Robinhood brokerage customers on market quality," Finance Research Letters, Elsevier, vol. 43(C).
    2. Yi Luo & Steven E. Salterio, 2022. "The Effect of Gender on Investors’ Judgments and Decision-Making," Journal of Business Ethics, Springer, vol. 179(1), pages 237-258, August.
    3. Potsaid, Timothy & Venkataraman, Shankar, 2022. "Trading restrictions and investor reaction to non-gains, non-losses, and the fear of missing out: Experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).

  9. Nolte, Ingmar & Xu, Qi, 2015. "The economic value of volatility timing with realized jumps," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 45-59.

    Cited by:

    1. Christos Alexakis & Dimitris Kenourgios & Vasileios Pappas & Athina Petropoulou, 2021. "From dotcom to Covid-19: A convergence analysis of Islamic investments," Post-Print hal-03347374, HAL.
    2. Matteo Bonato & Rangan Gupta & Chi Keung Marco Lau & Shixuan Wang, 2019. "Moments-Based Spillovers across Gold and Oil Markets," Working Papers 201966, University of Pretoria, Department of Economics.
    3. Fei Su & Lei Wang, 2020. "Conditional Volatility Persistence and Realized Volatility Asymmetry: Evidence from the Chinese Stock Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(14), pages 3252-3269, November.
    4. Cem Cakmakli & Verda Ozturk, 2021. "Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing," Koç University-TUSIAD Economic Research Forum Working Papers 2110, Koc University-TUSIAD Economic Research Forum.
    5. Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
    6. Omura, Akihiro & Li, Bin & Chung, Richard & Todorova, Neda, 2018. "Convenience yield, realised volatility and jumps: Evidence from non-ferrous metals," Economic Modelling, Elsevier, vol. 70(C), pages 496-510.
    7. Hu, Junjie & Kuo, Weiyu & Härdle, Wolfgang Karl, 2019. "Risk of Bitcoin Market: Volatility, Jumps, and Forecasts," IRTG 1792 Discussion Papers 2019-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Qi Xu & Ying Wang, 2021. "Managing volatility in commodity momentum," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(5), pages 758-782, May.
    9. Chang‐Che Wu & MeiChi Huang & Chih‐Chiang Wu, 2021. "The role of asymmetry and dynamics in carry trade and general financial markets," The Financial Review, Eastern Finance Association, vol. 56(2), pages 331-353, May.
    10. Ahmed, Walid M.A., 2020. "Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin," Journal of Economics and Business, Elsevier, vol. 108(C).
    11. Jianlei Han & Martina Linnenluecke & Zhangxin Liu & Zheyao Pan & Tom Smith, 2019. "A general equilibrium approach to pricing volatility risk," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-18, April.
    12. Fei Su, 2018. "Essays on Price Discovery and Volatility Dynamics in the Foreign Exchange Market," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2018, January-A.
    13. Chunyang Zhou & Chongfeng Wu & Weidong Xu, 2020. "Incorporating time‐varying jump intensities in the mean‐variance portfolio decisions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(3), pages 460-478, March.

  10. Nolte, Ingmar & Nolte, Sandra & Vasios, Michalis, 2014. "Sell-side analysts’ career concerns during banking stresses," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 424-441.

    Cited by:

    1. Bernard Herskovic & João Ramos, 2020. "Acquiring Information through Peers," American Economic Review, American Economic Association, vol. 110(7), pages 2128-2152, July.
    2. Liu, Guofang & Fang, Xi & Huang, Yuan & Zhao, Weidong, 2021. "Identifying the role of consumer and producer price index announcements in stock index futures price changes," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 87-101.
    3. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.
    4. Vadim S. Balashov & Zhanel B. DeVides, 2020. "Is Diversification A Job Safety Net For Sell‐Side Analysts?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 43(3), pages 543-573, August.
    5. Chen Su & Hanxiong Zhang & Kenbata Bangassa & Nathan Lael Joseph, 2019. "On the investment value of sell-side analyst recommendation revisions in the UK," Review of Quantitative Finance and Accounting, Springer, vol. 53(1), pages 257-293, July.

  11. Ingmar Nolte, 2012. "A detailed investigation of the disposition effect and individual trading behavior: a panel survival approach," The European Journal of Finance, Taylor & Francis Journals, vol. 18(10), pages 885-919, November.

    Cited by:

    1. Lepone, Grace & Tian, Gary, 2020. "Usage of conditional orders and the disposition effect in the stock market," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    2. Yang, Chunpeng & Zhang, Zhanpei, 2021. "Realization utility with stop-loss strategy," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 261-275.
    3. Gemayel, Roland & Preda, Alex, 2018. "Does a scopic regime erode the disposition effect? Evidence from a social trading platform," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 175-190.
    4. Fischbacher, Urs & Hoffmann, Gerson & Schudy, Simeon, 2017. "The Causal Effect of Stop-Loss and Take-Gain Orders on the Disposition Effect," Munich Reprints in Economics 49926, University of Munich, Department of Economics.
    5. Hermann, Daniel & Mußhoff, Oliver & Rau, Holger A., 2017. "The disposition effect when deciding on behalf of others," University of Göttingen Working Papers in Economics 332, University of Goettingen, Department of Economics.
    6. Maiko Koga, 2016. "Momentum trading behavior in the FX market: Evidence from Japanese retail investors," Economics Bulletin, AccessEcon, vol. 36(1), pages 92-96.
    7. Richards, Daniel W. & Willows, Gizelle D., 2018. "Who trades profusely? The characteristics of individual investors who trade frequently," Global Finance Journal, Elsevier, vol. 35(C), pages 1-11.
    8. Daniel W. Richards & Janette Rutterford & Devendra Kodwani & Mark Fenton-O'Creevy, 2017. "Stock market investors' use of stop losses and the disposition effect," The European Journal of Finance, Taylor & Francis Journals, vol. 23(2), pages 130-152, January.
    9. Richards, Daniel W. & Fenton-O'Creevy, Mark & Rutterford, Janette & Kodwani, Devendra G., 2018. "Is the disposition effect related to investors’ reliance on System 1 and System 2 processes or their strategy of emotion regulation?," Journal of Economic Psychology, Elsevier, vol. 66(C), pages 79-92.
    10. Li, Jianbiao & Niu, Xiaofei & Li, Dahui & Cao, Qian, 2018. "Using Non-Invasive Brain Stimulation to Test the Role of Self-Control in Investor Behavior," EconStor Preprints 177890, ZBW - Leibniz Information Centre for Economics.

  12. Ingmar Nolte & Sandra Nolte, 2012. "How do individual investors trade?," The European Journal of Finance, Taylor & Francis Journals, vol. 18(10), pages 921-947, November.

    Cited by:

    1. Ramazan Gençay & Nikola Gradojevic & Richard Olsen & Faruk Selçuk, 2015. "Informed traders' arrival in foreign exchange markets: Does geography matter?," Post-Print hal-01563055, HAL.
    2. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    3. Michael King & Carol Osler & Dagfinn Rime, 2012. "The Market Microstructure Approach to Foreign Exchange: Looking Back and Looking Forward," Working Papers 54, Brandeis University, Department of Economics and International Business School.
    4. Vasios, Michalis & Payne, Richard & Nolte, Ingmar, 2015. "Profiting from Mimicking Strategies in Non-Anonymous Markets," MPRA Paper 61710, University Library of Munich, Germany.
    5. Hung-Wen Lin & Kun-Ben Lin & Jing-Bo Huang & Shu-Heng Chen, 2021. "Timely Loss Recognition Helps Nothing," Sustainability, MDPI, vol. 13(14), pages 1-24, July.

  13. Mark Britten-Jones & Anthony Neuberger & Ingmar Nolte, 2011. "Improved Inference in Regression with Overlapping Observations," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(5-6), pages 657-683, June.

    Cited by:

    1. Ćorić, Bruno & Pugh, Geoff, 2013. "Foreign direct investment and output growth volatility: A worldwide analysis," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 260-271.
    2. Virginie Coudert & Valérie Mignon, 2013. "The ‘Forward Premium Puzzle’ and the Sovereign Default risk," Post-Print hal-01385839, HAL.
    3. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 235-256.
    4. Snaith, Stuart & Termprasertsakul, Santi & Wood, Andrew, 2017. "The exchange rate exposure puzzle: The long and the short of it," Economics Letters, Elsevier, vol. 159(C), pages 204-207.
    5. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Jacob Boudoukh & Ronen Israel & Matthew P. Richardson, 2020. "Biases in Long-Horizon Predictive Regressions," NBER Working Papers 27410, National Bureau of Economic Research, Inc.
    7. Sam Nicholls & David Orsmond, 2015. "The Economic Trends, Challenges and Behaviour of Small Businesses in Australia," RBA Annual Conference Volume (Discontinued), in: Angus Moore & John Simon (ed.),Small Business Conditions and Finance, Reserve Bank of Australia.
    8. Erik Kole & Reza Brink, "undated". "Constructing and Using Double-adjusted Alphas to Analyze Mutual Fund Performance," Tinbergen Institute Discussion Papers 19-029/IV, Tinbergen Institute.
    9. Charles W. Calomiris & Harry Mamaysky, 2018. "How News and Its Context Drive Risk and Returns Around the World," NBER Working Papers 24430, National Bureau of Economic Research, Inc.
    10. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
    11. Yan, Yan & Guan, JianCheng, 2018. "Social capital, exploitative and exploratory innovations: The mediating roles of ego-network dynamics," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 244-258.
    12. Kucher, Oleg & Kurov, Alexander, 2014. "Business cycle, storage, and energy prices," Review of Financial Economics, Elsevier, vol. 23(4), pages 217-226.
    13. Angelidis, Timotheos & Sakkas, Athanasios & Tessaromatis, Nikolaos, 2015. "Stock market dispersion, the business cycle and expected factor returns," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 265-279.
    14. Calomiris, Charles W. & Mamaysky, Harry, 2019. "How news and its context drive risk and returns around the world," Journal of Financial Economics, Elsevier, vol. 133(2), pages 299-336.
    15. Chiang, I-Hsuan Ethan & Kirby, Chris & Nie, Ziye Zoe, 2021. "Short-term reversals, short-term momentum, and news-driven trading activity," Journal of Banking & Finance, Elsevier, vol. 125(C).
    16. Erik Hjalmarsson & Tamas Kiss, 2022. "Long‐run predictability tests are even worse than you thought," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1334-1355, November.
    17. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    18. Heaton, Chris, 2015. "Testing for multiple-period predictability between serially dependent time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 587-597.
    19. Tu, Jing, 2024. "Openness to international collaboration and tie strength in enhancing knowledge creation," Journal of Informetrics, Elsevier, vol. 18(1).
    20. Prateek Sharma & Vipul, 2018. "Improving portfolio diversification: Identifying the right baskets for putting your eggs," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 39(6), pages 698-711, September.
    21. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Discussion Papers 46/2020, Deutsche Bundesbank.

  14. Ingmar Nolte & Valeri Voev, 2011. "Least Squares Inference on Integrated Volatility and the Relationship Between Efficient Prices and Noise," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 94-108, April.
    See citations under working paper version above.
  15. Adam-Müller, Axel F.A. & Nolte, Ingmar, 2011. "Cross hedging under multiplicative basis risk," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2956-2964, November.

    Cited by:

    1. Koziol, Philipp, 2014. "Inflation and interest rate derivatives for FX risk management: Implications for exporting firms under real wealth," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(4), pages 459-472.
    2. Korn, Olaf & Koziol, Philipp, 2009. "The term structure of currency hedge ratios," CFR Working Papers 09-01, University of Cologne, Centre for Financial Research (CFR).
    3. Fernandez-Perez, Adrian & Frijns, Bart & Gafiatullina, Ilnara & Tourani-Rad, Alireza, 2022. "Profit margin hedging in the New Zealand dairy farming industry," Journal of Commodity Markets, Elsevier, vol. 26(C).
    4. Marcelo J. Villena & Axel A. Araneda, 2014. "Option Pricing of Twin Assets," Papers 1401.6735, arXiv.org.
    5. Cao, Min & Conlon, Thomas, 2023. "Composite jet fuel cross-hedging," Journal of Commodity Markets, Elsevier, vol. 30(C).
    6. Kit Wong, 2014. "Hedging and the competitive firm under correlated price and background risk," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 329-340, October.
    7. Thomas Conlon & John Cotter & Ramazan Gençay, 2016. "Commodity futures hedging, risk aversion and the hedging horizon," The European Journal of Finance, Taylor & Francis Journals, vol. 22(15), pages 1534-1560, December.
    8. Berghöfer, Britta & Lucey, Brian, 2014. "Fuel hedging, operational hedging and risk exposure — Evidence from the global airline industry," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 124-139.
    9. Zainudin, Ahmad Danial & Mohamad, Azhar, 2021. "Cross hedging with stock index futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 128-144.
    10. Bai, Xiwen & Kavussanos, Manolis G., 2022. "Hedging IMO2020 compliant fuel price exposure using futures contracts," Energy Economics, Elsevier, vol. 110(C).
    11. Bernard, Carole & Kwak, Minsuk, 2016. "Semi-static hedging of variable annuities," Insurance: Mathematics and Economics, Elsevier, vol. 67(C), pages 173-186.
    12. Korn, Olaf & Merz, Alexander, 2016. "How to hedge if the payment date is uncertain?," CFR Working Papers 07-14 [rev.], University of Cologne, Centre for Financial Research (CFR).
    13. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    14. Olaf Korn & Alexander Merz, 2019. "How to hedge if the payment date is uncertain?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(4), pages 481-498, April.

  16. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2011. "An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 669-707, June.
    See citations under working paper version above.
  17. Ingmar Nolte, 2008. "Modeling a Multivariate Transaction Process," Journal of Financial Econometrics, Oxford University Press, vol. 6(1), pages 143-170, Winter.

    Cited by:

    1. Weiß, Gregor N.F. & Supper, Hendrik, 2013. "Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3334-3350.
    2. Gunther Wuyts, 2012. "The impact of aggressive orders in an order-driven market: a simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 18(10), pages 1015-1038, November.

  18. Nolte, Ingmar & Pohlmeier, Winfried, 2007. "Using forecasts of forecasters to forecast," International Journal of Forecasting, Elsevier, vol. 23(1), pages 15-28.

    Cited by:

    1. Krüger Fabian & Pohlmeier Winfried & Mokinski Frieder, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 63-81, February.
    2. Mokinski, Frieder, 2016. "Using time-stamped survey responses to measure expectations at a daily frequency," International Journal of Forecasting, Elsevier, vol. 32(2), pages 271-282.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming”," AQR Working Papers 201706, University of Barcelona, Regional Quantitative Analysis Group, revised May 2017.
    6. Stolzenburg, Ulrich & Lux, Thomas, 2010. "Identification of a core-periphery structure among participants of a business climate survey," Kiel Working Papers 1659, Kiel Institute for the World Economy (IfW Kiel).
    7. Kjellberg, David, 2006. "Measuring Expectations," Working Paper Series 2006:9, Uppsala University, Department of Economics.
    8. Brückbauer, Frank & Schröder, Michael, 2021. "Data resource profile: The ZEW FMS dataset," ZEW Discussion Papers 21-100, ZEW - Leibniz Centre for European Economic Research.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    10. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    11. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
    12. Breitung, Jörg & Schmeling, Maik, 2011. "Quantifying survey expectations: What's wrong with the probability approach?," Hannover Economic Papers (HEP) dp-485, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    13. Ullrich Heilemann & Herman O. Stekler, 2010. "Has the Accuracy of German Macroeconomic Forecasts Improved?," Working Papers 2010-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Feb 2012.
    14. Schmidt, Sandra & Nautz, Dieter, 2010. "Central bank communication and the perception of monetary policy by financial market experts," Discussion Papers 2010/29, Free University Berlin, School of Business & Economics.
    15. Fabian Krüger & Ingmar Nolte, 2011. "Disagreement, Uncertainty and the True Predictive Density," Working Paper Series of the Department of Economics, University of Konstanz 2011-43, Department of Economics, University of Konstanz.
    16. Frederik Kunze, 2020. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 313-333, March.
    17. Panagiotis Papaioannnou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Papers 1310.5306, arXiv.org.
    18. Henry Sabrowski, 2008. "Inflation Expectation Formation of German Consumers: Rational or Adaptive?," Working Paper Series in Economics 100, University of Lüneburg, Institute of Economics.
    19. Kunze, Frederik, 2017. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," University of Göttingen Working Papers in Economics 326, University of Goettingen, Department of Economics.
    20. Panagiotis Papaioannou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Netnomics, Springer, vol. 14(1), pages 47-68, November.

  19. Roman Liesenfeld & Ingmar Nolte & Winfried Pohlmeier, 2006. "Modelling financial transaction price movements: a dynamic integer count data model," Empirical Economics, Springer, vol. 30(4), pages 795-825, January.

    Cited by:

    1. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2020. "On an integer-valued stochastic intensity model for time series of counts," MPRA Paper 105406, University Library of Munich, Germany.
    2. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.
    3. Ferriani, Fabrizio, 2010. "Informed and uninformed traders at work: evidence from the French market," MPRA Paper 24487, University Library of Munich, Germany.
    4. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    5. Giulia Carallo & Roberto Casarin & Christian P. Robert, 2020. "Generalized Poisson Difference Autoregressive Processes," Papers 2002.04470, arXiv.org.
    6. BAUWENS, Luc & HAUTSCH, Nikolaus, 2009. "Modelling financial high frequency data using point processes," LIDAM Reprints CORE 2123, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Hellström, Jörgen & Simonsen, Ola, 2006. "Does the Open Limit Order Book Reveal Information About Short-run Stock Price Movements?," Umeå Economic Studies 687, Umeå University, Department of Economics.
    8. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2011. "An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 669-707, June.
    9. Dunsmuir, William T. M. & Scott, David J., 2015. "The glarma Package for Observation-Driven Time Series Regression of Counts," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i07).
    10. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised Dec 2023.
    11. Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2019. "Dynamic discrete mixtures for high frequency prices," Discussion Papers 19/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    12. Sucarrat, Genaro & Grønneberg, Steffen, 2016. "Models of Financial Return With Time-Varying Zero Probability," MPRA Paper 68931, University Library of Munich, Germany.
    13. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2013. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 89-121, December.
    14. Grammig, Joachim & Kehrle, Kerstin, 2008. "A new marked point process model for the federal funds rate target: Methodology and forecast evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2370-2396, July.
    15. Jung, Robert & Kukuk, Martin & Liesenfeld, Roman, 2005. "Time Series of Count Data: Modelling and Estimation," Economics Working Papers 2005-08, Christian-Albrechts-University of Kiel, Department of Economics.
    16. Magdalena Osinska & Andrzej Dobrzynski & Yochanan Shachmurove, 2016. "Performance Of American And Russian Joint Stock Companies On Financial Market. A Microstructure Perspective," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 819-851, December.
    17. Gianbiagio Curato & Fabrizio Lillo, 2013. "Modeling the coupled return-spread high frequency dynamics of large tick assets," Papers 1310.4539, arXiv.org.
    18. Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2350-2364, December.
    19. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2008. "A multivariate integer count hurdle model: theory and application to exchange rate dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 31-48, Springer.
    20. McCausland, William J., 2012. "The HESSIAN method: Highly efficient simulation smoothing, in a nutshell," Journal of Econometrics, Elsevier, vol. 168(2), pages 189-206.
    21. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    22. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    23. Yousung Park & Hee-Young Kim, 2012. "Diagnostic checks for integer-valued autoregressive models using expected residuals," Statistical Papers, Springer, vol. 53(4), pages 951-970, November.
    24. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2006. "Estimating liquidity using information on the multivariate trading process," Working Papers 10, Department of Applied Econometrics, Warsaw School of Economics.
    25. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    26. Federico Bassetti & Giulia Carallo & Roberto Casarin, 2022. "First-order integer-valued autoregressive processes with Generalized Katz innovations," Papers 2202.02029, arXiv.org.

Chapters

  1. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2008. "A multivariate integer count hurdle model: theory and application to exchange rate dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 31-48, Springer.
    See citations under working paper version above.
  2. Roman Liesenfeld & Ingmar Nolte & Winfried Pohlmeier, 2008. "Modelling financial transaction price movements: a dynamic integer count data model," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 167-197, Springer.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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