Fourier Integral Operator Model of Market Liquidity: The Chinese Experience 2009–2010
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Charles-Albert Lehalle & Sophie Laruelle (ed.), 2013. "Market Microstructure in Practice," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8967, December.
- Alexander Lipton & Umberto Pesavento & Michael G Sotiropoulos, 2013. "Trade arrival dynamics and quote imbalance in a limit order book," Papers 1312.0514, arXiv.org.
- Francis X. Diebold & Kamil Yilmaz, 2009.
"Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets,"
Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
- FrancisX. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
- Francis X. Diebold & Kamil Yılmaz, 2007. "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets," Koç University-TUSIAD Economic Research Forum Working Papers 0705, Koc University-TUSIAD Economic Research Forum.
- Francis X. Diebold & Kamil Yilmaz, 2008. "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets," NBER Working Papers 13811, National Bureau of Economic Research, Inc.
- Diebold, Francis X. & Yilmaz, Kamil, 2007. "Measuring financial asset return and volatility spillovers, with application to global equity markets," CFS Working Paper Series 2007/02, Center for Financial Studies (CFS).
- Francis X. Diebold & Kamil Yilmaz, 2007. "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets," PIER Working Paper Archive 07-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Francis X. Diebold & Kamil Yilmaz, 2008. "Measuring financial asset return and volatility spillovers, with application to global equity markets," Working Papers 08-16, Federal Reserve Bank of Philadelphia.
- Diebold, Francis X. & Yilmaz, Kamil, 2008. "Measuring financial asset return and volatilty spillovers, with application to global equity markets," CFS Working Paper Series 2008/26, Center for Financial Studies (CFS).
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
- Kingsley Y. L. Fong & Craig W. Holden & Charles A. Trzcinka, 2017. "What Are the Best Liquidity Proxies for Global Research?," Review of Finance, European Finance Association, vol. 21(4), pages 1355-1401.
- Michael Goldstein & James J. Angel, 2014.
"When Finance Meets Physics: The Impact of the Speed of Light on Financial Markets and Their Regulation,"
The Financial Review, Eastern Finance Association, vol. 49(2), pages 271-281, May.
- James J. Angel, 2014. "When Finance Meets Physics: The Impact of the Speed of Light on Financial Markets and their Regulation," Papers 1401.2982, arXiv.org.
- Malcolm Baker & Jeffrey Wurgler, 2006.
"Investor Sentiment and the Cross‐Section of Stock Returns,"
Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
- Malcolm Baker & Jeffrey Wurgler, 2004. "Investor Sentiment and the Cross-Section of Stock Returns," NBER Working Papers 10449, National Bureau of Economic Research, Inc.
- Li, Xiao-Ming & Peng, Lu, 2017. "US economic policy uncertainty and co-movements between Chinese and US stock markets," Economic Modelling, Elsevier, vol. 61(C), pages 27-39.
- Yilmaz, Kamil, 2010.
"Return and volatility spillovers among the East Asian equity markets,"
Journal of Asian Economics, Elsevier, vol. 21(3), pages 304-313, June.
- Kamil Yilmaz, 2009. "Return and Volatility Spillovers among the East Asian Equity Markets," Koç University-TUSIAD Economic Research Forum Working Papers 0907, Koc University-TUSIAD Economic Research Forum.
- Chen Liu & Yi An, 2018. "Investor Sentiment and the Basis of CSI 300 Stock Index Futures: An Empirical Study Based on QVAR Model and Quantile Regression," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-13, November.
- Albert J. Menkveld & Marius A. Zoican, 2017.
"Need for Speed? Exchange Latency and Liquidity,"
The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1188-1228.
- Albert J. Menkveld & Marius A. Zoican, 2014. "Need for Speed? Exchange Latency and Liquidity," Tinbergen Institute Discussion Papers 14-097/IV, Tinbergen Institute.
- Albert Menkveld & Marius Andrei Zoican, 2016. "Need for Speed? Exchange Latency and Liquidity," Working Papers hal-01253615, HAL.
- Albert Menkveld & Marius Andrei Zoican, 2017. "Need for Speed? Exchange Latency and Liquidity," Post-Print hal-01501352, HAL.
- Amihud, Yakov & Mendelson, Haim, 1986. "Asset pricing and the bid-ask spread," Journal of Financial Economics, Elsevier, vol. 17(2), pages 223-249, December.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
- Qiao, Kenan & Dam, Lammertjan, 2020. "The overnight return puzzle and the “T+1” trading rule in Chinese stock markets," Journal of Financial Markets, Elsevier, vol. 50(C).
- Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
- Peter Lerner, 2015. "Patience vs. impatience of traders: Formation of the value-at-price distribution through competition for liquidity," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-30.
- Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
- Álvaro Cartea & Ryan Donnelly & Sebastian Jaimungal, 2018. "Enhancing trading strategies with order book signals," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 1-35, January.
- Weibing Huang & Charles-Albert Lehalle & Mathieu Rosenbaum, 2015.
"Simulating and Analyzing Order Book Data: The Queue-Reactive Model,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 107-122, March.
- Weibing Huang & Charles-Albert Lehalle & Mathieu Rosenbaum, 2013. "Simulating and analyzing order book data: The queue-reactive model," Papers 1312.0563, arXiv.org, revised Sep 2014.
- Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2013. "The Price Impact of Order Book Events," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 47-88, December.
- Donald B. Keim & Ananth Madhavan, "undated".
"The Cost of Institutional Equity Trades,"
Rodney L. White Center for Financial Research Working Papers
08-98, Wharton School Rodney L. White Center for Financial Research.
- Donald B. Keim & Ananth Madhavan, "undated". "The Cost of Institutional Equity Trades," Rodney L. White Center for Financial Research Working Papers 8-98, Wharton School Rodney L. White Center for Financial Research.
- Humphery-Jenner, Mark L., 2011. "Optimal VWAP trading under noisy conditions," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2319-2329, September.
- David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
- Bartlett, Robert P. & McCrary, Justin, 2019. "How rigged are stock markets? Evidence from microsecond timestamps," Journal of Financial Markets, Elsevier, vol. 45(C), pages 37-60.
- repec:fth:pennfi:68 is not listed on IDEAS
- repec:oup:rfinst:v:21:y:2017:i:4:p:1355-1401. is not listed on IDEAS
- Zhou, Xiangyi & Zhang, Weijin & Zhang, Jie, 2012. "Volatility spillovers between the Chinese and world equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 247-270.
- Foster, F Douglas & Viswanathan, S, 1996. "Strategic Trading When Agents Forecast the Forecasts of Others," Journal of Finance, American Finance Association, vol. 51(4), pages 1437-1478, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- P. B. Lerner, 2020. "Dual State-Space Model of Market Liquidity: The Chinese Experience 2009-2010," Papers 2004.06200, arXiv.org, revised May 2020.
- Lof, Matthijs & van Bommel, Jos, 2023.
"Asymmetric information and the distribution of trading volume,"
Journal of Corporate Finance, Elsevier, vol. 82(C).
- Lof, Matthijs & Bommel, Jos van, 2018. "Asymmetric information and the distribution of trading volume," Bank of Finland Research Discussion Papers 1/2018, Bank of Finland.
- Lof, Matthijs & Bommel, Jos van, 2018. "Asymmetric information and the distribution of trading volume," Bank of Finland Research Discussion Papers 1/2018, Bank of Finland.
- Cakici, Nusret & Zaremba, Adam, 2021. "Liquidity and the cross-section of international stock returns," Journal of Banking & Finance, Elsevier, vol. 127(C).
- Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
- Gang Chu & John W. Goodell & Dehua Shen & Yongjie Zhang, 2022. "Machine learning to establish proxies for investor attention: evidence of improved stock-return prediction," Annals of Operations Research, Springer, vol. 318(1), pages 103-128, November.
- Múnera, Daimer J. & Agudelo, Diego A., 2022. "Who moved my liquidity? Liquidity evaporation in emerging markets in periods of financial uncertainty," Journal of International Money and Finance, Elsevier, vol. 129(C).
- Jozef Barunik & Martin Hronec & Ondrej Tobek, 2024. "Predicting the distributions of stock returns around the globe in the era of big data and learning," Papers 2408.07497, arXiv.org.
- Xue Gong & Weiguo Zhang & Weijun Xu & Zhe Li, 2022. "Uncertainty index and stock volatility prediction: evidence from international markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-44, December.
- Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023.
"A penalized two-pass regression to predict stock returns with time-varying risk premia,"
Journal of Econometrics, Elsevier, vol. 237(2).
- Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2021. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Swiss Finance Institute Research Paper Series 21-09, Swiss Finance Institute.
- Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Post-Print hal-04325655, HAL.
- Gaetan Bakalli & St'ephane Guerrier & Olivier Scaillet, 2022. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Papers 2208.00972, arXiv.org.
- Imran Yousaf & Shoaib Ali & Wing-Keung Wong, 2020. "An Empirical Analysis of the Volatility Spillover Effect between World-Leading and the Asian Stock Markets: Implications for Portfolio Management," JRFM, MDPI, vol. 13(10), pages 1-28, September.
- Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Nov 2024.
- Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
- Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
- Adel Javanmard & Jingwei Ji & Renyuan Xu, 2024. "Multi-Task Dynamic Pricing in Credit Market with Contextual Information," Papers 2410.14839, arXiv.org, revised Oct 2024.
- DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
- Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
- Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
- Charles-Albert Lehalle & Eyal Neuman, 2019.
"Incorporating signals into optimal trading,"
Finance and Stochastics, Springer, vol. 23(2), pages 275-311, April.
- Charles-Albert Lehalle & Eyal Neuman, 2017. "Incorporating Signals into Optimal Trading," Papers 1704.00847, arXiv.org, revised Jun 2018.
- Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "Bloated Disclosures: Can ChatGPT Help Investors Process Information?," Papers 2306.10224, arXiv.org, revised Feb 2024.
- Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
More about this item
Keywords
market microstructure; market sentiment; neural networks; retail brokerages;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2459-:d:862868. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.