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Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data

Citations

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Cited by:

  1. Pierre Bajgrowicz & Olivier Scaillet & Adrien Treccani, 2016. "Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News," Management Science, INFORMS, vol. 62(8), pages 2198-2217, August.
  2. Cartea, Álvaro & Karyampas, Dimitrios, 2011. "Volatility and covariation of financial assets: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3319-3334.
  3. Christensen, Kim & Oomen, Roel & Podolskij, Mark, 2010. "Realised quantile-based estimation of the integrated variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 74-98, November.
  4. Kim, Donggyu & Kong, Xin-Bing & Li, Cui-Xia & Wang, Yazhen, 2018. "Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 203(1), pages 69-79.
  5. Song, Xinyu & Kim, Donggyu & Yuan, Huiling & Cui, Xiangyu & Lu, Zhiping & Zhou, Yong & Wang, Yazhen, 2021. "Volatility analysis with realized GARCH-Itô models," Journal of Econometrics, Elsevier, vol. 222(1), pages 393-410.
  6. Fu, Jin-Yu & Lin, Jin-Guan & Hao, Hong-Xia, 2023. "Volatility analysis for the GARCH–Itô–Jumps model based on high-frequency and low-frequency financial data," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1698-1712.
  7. 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.
  8. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.
  9. Adam D. Bull, 2013. "Estimating time-changes in noisy L\'evy models," Papers 1312.5911, arXiv.org, revised Nov 2014.
  10. Jianqing Fan & Yingying Li & Ke Yu, 2012. "Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 412-428, March.
  11. 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.
  12. Barunik, Jozef & Vacha, Lukas, 2018. "Do co-jumps impact correlations in currency markets?," Journal of Financial Markets, Elsevier, vol. 37(C), pages 97-119.
  13. Barunik, Jozef & Barunikova, Michaela, 2015. "Revisiting the long memory dynamics of implied-realized volatility relation: A new evidence from wavelet band spectrum regression," FinMaP-Working Papers 43, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  14. Vuorenmaa, Tommi A., 2008. "Decimalization, Realized Volatility, and Market Microstructure Noise," MPRA Paper 8692, University Library of Munich, Germany.
  15. Minseog Oh & Donggyu Kim, 2021. "Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective," Papers 2111.09655, arXiv.org.
  16. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2020. "A new wavelet-based ultra-high-frequency analysis of triangular currency arbitrage," Economic Modelling, Elsevier, vol. 85(C), pages 57-73.
  17. Dzieliński, Michał & Rieger, Marc Oliver & Talpsepp, Tõnn, 2018. "Asymmetric attention and volatility asymmetry," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 59-67.
  18. Markus Bibinger & Lars Winkelmann, 2013. "Econometrics of co-jumps in high-frequency data with noise," SFB 649 Discussion Papers SFB649DP2013-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  19. 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.
  20. repec:hal:journl:peer-00732538 is not listed on IDEAS
  21. Li, Yingying & Zhang, Zhiyuan & Zheng, Xinghua, 2013. "Volatility inference in the presence of both endogenous time and microstructure noise," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2696-2727.
  22. Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
  23. José León & Carenne Ludeña, 2015. "Difference based estimators and infill statistics," Statistical Inference for Stochastic Processes, Springer, vol. 18(1), pages 1-31, April.
  24. Fangfang Wang, 2016. "An Unbiased Measure of Integrated Volatility in the Frequency Domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 147-164, March.
  25. Zongwu Cai & Yongmiao Hong, 2013. "Some Recent Developments in Nonparametric Finance," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
  26. Müller, Hans-Georg & Sen, Rituparna & Stadtmüller, Ulrich, 2011. "Functional data analysis for volatility," Journal of Econometrics, Elsevier, vol. 165(2), pages 233-245.
  27. Aït-Sahalia, Yacine & Fan, Jianqing & Li, Yingying, 2013. "The leverage effect puzzle: Disentangling sources of bias at high frequency," Journal of Financial Economics, Elsevier, vol. 109(1), pages 224-249.
  28. Fan, Jianqing & Kim, Donggyu, 2019. "Structured volatility matrix estimation for non-synchronized high-frequency financial data," Journal of Econometrics, Elsevier, vol. 209(1), pages 61-78.
  29. Kraicová Lucie & Baruník Jozef, 2017. "Estimation of long memory in volatility using wavelets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(3), pages 1-22, June.
  30. Jing, Bing-Yi & Kong, Xin-Bing & Liu, Zhi & Mykland, Per, 2012. "On the jump activity index for semimartingales," Journal of Econometrics, Elsevier, vol. 166(2), pages 213-223.
  31. Chen Yi-Ting & Sun Edward W. & Yu Min-Teh, 2015. "Improving model performance with the integrated wavelet denoising method," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 445-467, September.
  32. Donggyu Kim & Minseok Shin & Yazhen Wang, 2021. "Overnight GARCH-It\^o Volatility Models," Papers 2102.13467, arXiv.org, revised Jun 2022.
  33. Fulvio Corsi & Stefano Peluso & Francesco Audrino, 2015. "Missing in Asynchronicity: A Kalman‐em Approach for Multivariate Realized Covariance Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 377-397, April.
  34. Neil Shephard & Kevin Sheppard, 2010. "Realising the future: forecasting with high-frequency-based volatility (HEAVY) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 197-231.
  35. Xinghua Zheng & Yingying Li, 2010. "On the estimation of integrated covariance matrices of high dimensional diffusion processes," Papers 1005.1862, arXiv.org, revised Mar 2012.
  36. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
  37. Chang, Jinyuan & Chen, Songxi, 2011. "On the Approximate Maximum Likelihood Estimation for Diffusion Processes," MPRA Paper 46279, University Library of Munich, Germany.
  38. Talpsepp, Tõnn & Rieger, Marc Oliver, 2010. "Explaining asymmetric volatility around the world," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 938-956, December.
  39. Jozef Barunik & Michaela Barunikova, 2012. "Revisiting the fractional cointegrating dynamics of implied-realized volatility relation with wavelet band spectrum regression," Papers 1208.4831, arXiv.org, revised Feb 2013.
  40. Lam, Clifford & Feng, Phoenix, 2018. "A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data," LSE Research Online Documents on Economics 88375, London School of Economics and Political Science, LSE Library.
  41. Yacine Aït-Sahalia & Jean Jacod, 2012. "Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data," Journal of Economic Literature, American Economic Association, vol. 50(4), pages 1007-1050, December.
  42. Markus Bibinger, 2011. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," SFB 649 Discussion Papers SFB649DP2011-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  43. Liu, Cheng & Tang, Cheng Yong, 2014. "A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data," Journal of Econometrics, Elsevier, vol. 180(2), pages 217-232.
  44. Lam, Clifford & Feng, Phoenix, 2018. "A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data," Journal of Econometrics, Elsevier, vol. 206(1), pages 226-257.
  45. Jozef Barunik & Pavel Fiser, 2019. "Co-jumping of Treasury Yield Curve Rates," Papers 1905.01541, arXiv.org.
  46. repec:wyi:journl:002108 is not listed on IDEAS
  47. 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.
  48. Liang-Ching Lin & Sangyeol Lee & Meihui Guo, 2014. "The Bickel–Rosenblatt test for continuous time stochastic volatility models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 195-218, March.
  49. Xin-Bing Kong, 2013. "A direct approach to risk approximation for vast portfolios under gross-exposure constraint using high-frequency data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 647-669, November.
  50. Jie Xiong & Yong Zeng, 2011. "A branching particle approximation to a filtering micromovement model of asset price," Statistical Inference for Stochastic Processes, Springer, vol. 14(2), pages 111-140, May.
  51. Evans, Kevin P., 2011. "Intraday jumps and US macroeconomic news announcements," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2511-2527, October.
  52. Donggyu Kim, 2016. "Statistical Inference for Unified Garch–Itô Models with High-Frequency Financial Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(4), pages 513-532, July.
  53. Jiang, George J. & Oomen, Roel C.A., 2008. "Testing for jumps when asset prices are observed with noise-a "swap variance" approach," Journal of Econometrics, Elsevier, vol. 144(2), pages 352-370, June.
  54. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
  55. Baruník, Jozef & Hlínková, Michaela, 2016. "Revisiting the long memory dynamics of the implied–realized volatility relationship: New evidence from the wavelet regression," Economic Modelling, Elsevier, vol. 54(C), pages 503-514.
  56. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.
  57. Qiang Liu & Zhi Liu, 2022. "Estimating spot volatility under infinite variation jumps with dependent market microstructure noise," Papers 2205.15738, arXiv.org, revised Feb 2023.
  58. Fan, Yingying & Fan, Jianqing, 2011. "Testing and detecting jumps based on a discretely observed process," Journal of Econometrics, Elsevier, vol. 164(2), pages 331-344, October.
  59. Sun, Edward W. & Meinl, Thomas, 2012. "A new wavelet-based denoising algorithm for high-frequency financial data mining," European Journal of Operational Research, Elsevier, vol. 217(3), pages 589-599.
  60. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
  61. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
  62. repec:wyi:journl:002150 is not listed on IDEAS
  63. Richard Y. Chen, 2019. "The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations," Papers 1911.02205, arXiv.org.
  64. Bing-Yi Jing & Zhi Liu & Xin-Bing Kong, 2014. "On the Estimation of Integrated Volatility With Jumps and Microstructure Noise," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 457-467, July.
  65. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
  66. Giacomo Bormetti & Lucio Maria Calcagnile & Michele Treccani & Fulvio Corsi & Stefano Marmi & Fabrizio Lillo, 2015. "Modelling systemic price cojumps with Hawkes factor models," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1137-1156, July.
  67. Michael Ho & Jack Xin, 2016. "Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps," Papers 1602.02185, arXiv.org, revised Apr 2016.
  68. Kim, Donggyu & Wang, Yazhen & Zou, Jian, 2016. "Asymptotic theory for large volatility matrix estimation based on high-frequency financial data," Stochastic Processes and their Applications, Elsevier, vol. 126(11), pages 3527-3577.
  69. Kim, Donggyu & Wang, Yazhen, 2016. "Sparse PCA-based on high-dimensional Itô processes with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 172-189.
  70. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
  71. Xin Zhang & Donggyu Kim & Yazhen Wang, 2016. "Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets," Econometrics, MDPI, vol. 4(3), pages 1-26, August.
  72. Bibinger, Markus, 2012. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," Stochastic Processes and their Applications, Elsevier, vol. 122(6), pages 2411-2453.
  73. Liu, Zhi & Kong, Xin-Bing & Jing, Bing-Yi, 2018. "Estimating the integrated volatility using high-frequency data with zero durations," Journal of Econometrics, Elsevier, vol. 204(1), pages 18-32.
  74. repec:hal:journl:peer-00741630 is not listed on IDEAS
  75. Chun, Dohyun & Cho, Hoon & Kim, Jihun, 2022. "The relationship between carbon-intensive fuel and renewable energy stock prices under the emissions trading system," Energy Economics, Elsevier, vol. 114(C).
  76. Donggyu Kim, 2021. "Exponential GARCH-Ito Volatility Models," Papers 2111.04267, arXiv.org.
  77. Xing, Jieli & Zhang, Yongjie & Chu, Gang & Pan, Qi & Zhang, Xiaotao, 2021. "Detection and reconstruction of catastrophic breaks of high-frequency financial data with local linear scaling approximation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
  78. Tim Leung & Theodore Zhao, 2024. "A Noisy Fractional Brownian Motion Model for Multiscale Correlation Analysis of High-Frequency Prices," Mathematics, MDPI, vol. 12(6), pages 1-21, March.
  79. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.
  80. Donggyu Kim & Minseog Oh, 2023. "Dynamic Realized Minimum Variance Portfolio Models," Papers 2310.13511, arXiv.org.
  81. Kim, Donggyu & Wang, Yazhen, 2016. "Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data," Journal of Econometrics, Elsevier, vol. 194(2), pages 220-230.
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