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Econometrics of Financial High-Frequency Data

Citations

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Cited by:

  1. Giuseppe Cavaliere & Thomas Mikosch & Anders Rahbek & Frederik Vilandt, 2022. "The Econometrics of Financial Duration Modeling," Papers 2208.02098, arXiv.org, revised Dec 2022.
  2. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
  3. Hiroyuki Kawakatsu, 2019. "Jointly Modeling Autoregressive Conditional Mean and Variance of Non-Negative Valued Time Series," Econometrics, MDPI, vol. 7(4), pages 1-19, December.
  4. Herrera, Rodrigo & González, Nicolás, 2014. "The modeling and forecasting of extreme events in electricity spot markets," International Journal of Forecasting, Elsevier, vol. 30(3), pages 477-490.
  5. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
  6. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2023. "A higher-order correct fast moving-average bootstrap for dependent data," Journal of Econometrics, Elsevier, vol. 235(1), pages 65-81.
  7. Pierre Perron & Eduardo Zorita & Wen Cao & Clifford Hurvich & Philippe Soulier, 2017. "Drift in Transaction-Level Asset Price Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 769-790, September.
  8. Virgil DAMIAN & Cosmin – Octavian CEPOI, 2016. "Volatility Estimators With High-Frequency Data From Bucharest Stock Exchange," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 247-264.
  9. Kara Karpman & Sumanta Basu & David Easley, 2022. "Learning Financial Networks with High-frequency Trade Data," Papers 2208.03568, arXiv.org.
  10. N. Balakrishna & H. L. Koul & M. Ossiander & L. Sakhanenko, 2019. "Fitting a pth Order Parametric Generalized Linear Autoregressive Multiplicative Error Model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 103-122, September.
  11. Richard Y. Chen & Per A. Mykland, 2015. "Model-Free Approaches to Discern Non-Stationary Microstructure Noise and Time-Varying Liquidity in High-Frequency Data," Papers 1512.06159, arXiv.org, revised Oct 2018.
  12. Vasileios Siakoulis & Ioannis Venetis, 2015. "On inter-arrival times of bond market extreme events. An application to seven European markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(4), pages 717-741, October.
  13. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
  14. 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.
  15. Andrija Mihoci & Christopher Hian-Ann Ting & Meng-Jou Lu & Kainat Khowaja, 2022. "Adaptive order flow forecasting with multiplicative error models," Digital Finance, Springer, vol. 4(1), pages 89-108, March.
  16. Abdelhakim Aknouche & Christian Francq, 2022. "Stationarity and ergodicity of Markov switching positive conditional mean models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 436-459, May.
  17. José Da Fonseca & Riadh Zaatour, 2017. "Correlation and Lead–Lag Relationships in a Hawkes Microstructure Model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(3), pages 260-285, March.
  18. Hira L. Koul & Indeewara Perera & Narayana Balakrishna, 2023. "A class of Minimum Distance Estimators in Markovian Multiplicative Error Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 87-115, May.
  19. Yuanhua Feng & Sarah Forstinger & Christian Peitz, 2013. "On the iterative plug-in algorithm for estimating diurnal patterns of financial trade durations," Working Papers CIE 66, Paderborn University, CIE Center for International Economics.
  20. Jan Beran & Yuanhua Feng & Sucharita Ghosh, 2015. "Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models," Statistical Papers, Springer, vol. 56(2), pages 431-451, May.
  21. 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.
  22. Nikolaus Hautsch & Julia Schuamburg & Melanie Schienle, 2012. "Modeling Time-Varying Dependencies between Positive-Valued High-Frequency Time Series," SFB 649 Discussion Papers SFB649DP2012-054, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  23. Erindi Allaj, 2017. "Implicit Transaction Costs And The Fundamental Theorems Of Asset Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-39, June.
  24. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2021. "Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series," MPRA Paper 110954, University Library of Munich, Germany, revised 06 Dec 2021.
  25. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2020. "Periodic autoregressive conditional duration," MPRA Paper 101696, University Library of Munich, Germany, revised 08 Jul 2020.
  26. Demetrescu, Matei & Golosnoy, Vasyl & Titova, Anna, 2020. "Bias corrections for exponentially transformed forecasts: Are they worth the effort?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 761-780.
  27. Samuel Gingras & William J. McCausland, 2020. "A Flexible Stochastic Conditional Duration Model," Papers 2005.09166, arXiv.org.
  28. Siakoulis, Vasilios, 2015. "Modeling bank default intensity in the USA using autoregressive duration models," MPRA Paper 64526, University Library of Munich, Germany.
  29. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
  30. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
  31. Yuta Koike, 2017. "Time endogeneity and an optimal weight function in pre-averaging covariance estimation," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 15-56, April.
  32. Alexandre de Carvalho & Alberto Sanyuan Suen & Felippe Gallo, 2016. "Market Efficiency in Brazil: some evidence from high-frequency data," Working Papers Series 431, Central Bank of Brazil, Research Department.
  33. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.
  34. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
  35. 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.
  36. Simona ADASCALI?EI, 2014. "High Frequency Trading – A Revolutionary Concept On Financial Markets," THE YEARBOOK OF THE "GH. ZANE" INSTITUTE OF ECONOMIC RESEARCHES, Gheorghe Zane Institute for Economic and Social Research ( from THE ROMANIAN ACADEMY, JASSY BRANCH), vol. 23(1), pages 27-34.
  37. Gerardo-Giorda, Luca & Germano, Guido & Scalas, Enrico, 2015. "Large scale simulation of synthetic markets," LSE Research Online Documents on Economics 67563, London School of Economics and Political Science, LSE Library.
  38. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
  39. Curato, Imma Valentina, 2019. "Estimation of the stochastic leverage effect using the Fourier transform method," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3207-3238.
  40. Pan, Beier, 2023. "The asymmetric dynamics of stock–bond liquidity correlation in China: The role of macro-financial determinants," Economic Modelling, Elsevier, vol. 124(C).
  41. Lock, Eduardo & Winkelried, Diego, 2015. "Flujos de órdenes en el mercado cambiario y el valor intrínseco del Nuevo Sol," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 29, pages 33-54.
  42. 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.
  43. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.
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