create a website

Neural stochastic agent‐based limit order book simulation with neural point process and diffusion probabilistic model. (2024). Shi, Zijian ; Cartlidge, John.
In: Intelligent Systems in Accounting, Finance and Management.
RePEc:wly:isacfm:v:31:y:2024:i:2:n:e1553.

Full description at Econpapers || Download paper

Cited: 0

Citations received by this document

Cites: 69

References cited by this document

Cocites: 50

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

    This document has not been cited yet.

References

References cited by this document

  1. Abergel, F., Anane, M., Chakraborti, A., Jedidi, A., & Toke, I. M. (2016). Limit order books. Cambridge University Press.
    Paper not yet in RePEc: Add citation now

  2. Almgren, R. F. (2003). Optimal execution with nonlinear impact functions and trading‐enhanced risk. Applied Mathematical Finance, 10(1), 1–18. https://doi.org/10.1080/135048602100056.

  3. Arthur, W. B., Holland, J. H., LeBaron, B., Palmer, R., & Tayler, P. (1997). Asset pricing under endogenous expectations in an artificial stock market. In The economy as an evolving complex system II (pp. 15–44). CRC Press. https://doi.org/10.1201/9780429496639-2.
    Paper not yet in RePEc: Add citation now
  4. Bershova, N., & Rakhlin, D. (2013). The non‐linear market impact of large trades: Evidence from buy‐side order flow. Quantitative Finance, 13(11), 1759–1778. https://doi.org/10.1080/14697688.2013.861076.
    Paper not yet in RePEc: Add citation now

  5. Biais, B., & Foucault, T. (2014). HFT and market quality. Bankers, Markets & Investors, 128(1), 5–19.

  6. Biais, B., Hillion, P., & Spatt, C. (1995). An empirical analysis of the limit order book and the order flow in the Paris Bourse. The Journal of Finance, 50(5), 1655–1689. https://doi.org/10.1111/j.1540-6261.1995.tb05192.x.

  7. Bikhchandani, S., & Sharma, S. (2000). Herd behavior in financial markets. IMF Staff Papers, 47(3), 279–310. https://doi.org/10.2307/3867650.

  8. Boehmer, E., Li, D., & Saar, G. (2018). The competitive landscape of high‐frequency trading firms. The Review of Financial Studies, 31(6), 2227–2276. https://doi.org/10.1093/rfs/hhx144.
    Paper not yet in RePEc: Add citation now

  9. Bouchaud, J.‐P., & Potters, M. (2001). More stylized facts of financial markets: Leverage effect and downside correlations. Physica a: Statistical Mechanics and its Applications, 299(1–2), 60–70. https://doi.org/10.1016/S0378-4371(01)00282-5.

  10. Bouchaud, J.‐P., M'ezard, M., & Potters, M. (2002). Statistical properties of stock order books: Empirical results and models. Quantitative Finance, 2(4), 251–256. https://doi.org/10.1088/1469-7688/2/4/301.

  11. Boyd, N. E., Sahin, B. B., Haigh, M. S., & Harris, J. H. (2016). The prevalence, sources, and effects of herding. Journal of Futures Markets, 36(7), 671–694. https://doi.org/10.1002/fut.21756.

  12. Brandouy, O., Corelli, A., Veryzhenko, I., & Waldeck, R. (2012). A re‐examination of the “zero is enough” hypothesis in the emergence of financial stylized facts. Journal of Economic Interaction and Coordination, 7(2), 223–248. https://doi.org/10.1007/s11403-012-0099-0.
    Paper not yet in RePEc: Add citation now
  13. Byrd, D. (2019). Explaining agent‐based financial market simulation. arXiv, 1909.11650. https://doi.org/10.48550/arXiv.1909.11650.
    Paper not yet in RePEc: Add citation now
  14. Byrd, D., Hybinette, M., & Balch, T. H. (2020). ABIDES: Towards high‐fidelity multi‐agent market simulation. In Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM‐PADS'20 (pp. 11–22). Association for Computing Machinery. https://doi.org/10.1145/3384441.3395986.
    Paper not yet in RePEc: Add citation now
  15. Capponi, F., & Cont, R. (2019). Trade duration, volatility and market impact. Available at SSRN: https://doi.org/10.2139/ssrn.3351736.
    Paper not yet in RePEc: Add citation now

  16. Cartea, A., Donnelly, R., & Jaimungal, S. (2018). Enhancing trading strategies with order book signals. Applied Mathematical Finance, 25(1), 1–35. https://doi.org/10.1080/1350486X.2018.1434009.

  17. Chen, J.‐J., Zheng, B., & Tan, L. (2013). Agent‐based model with asymmetric trading and herding for complex financial systems. PLoS ONE, 8(11), e79531. https://doi.org/10.1371/journal.pone.0079531.

  18. Chen, Q., Hua, X., & Jiang, Y. (2018). Contrarian strategy and herding behaviour in the Chinese stock market. The European Journal of Finance, 24(16), 1552–1568. https://doi.org/10.1080/1351847X.2015.1071715.

  19. Cliff, D. (2018). An open‐source limit‐order‐book exchange for teaching and research. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1853–1860). IEEE. https://doi.org/10.1109/SSCI.2018.8628760.
    Paper not yet in RePEc: Add citation now
  20. Coletta, A., Moulin, A., Vyetrenko, S., & Balch, T. (2022). Learning to simulate realistic limit order book markets from data as a world agent. In Proceedings of the Third ACM International Conference on AI in Finance (pp. 428–436). Association for Computing Machinery. https://doi.org/10.1145/3533271.3561753.
    Paper not yet in RePEc: Add citation now

  21. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236. https://doi.org/10.1080/713665670.

  22. Cont, R., & De Larrard, A. (2013). Price dynamics in a Markovian limit order market. SIAM Journal on Financial Mathematics, 4(1), 1–25. https://doi.org/10.1137/110856605.

  23. Cont, R., Kukanov, A., & Stoikov, S. (2014). The price impact of order book events. Journal of Financial Econometrics, 12(1), 47–88. https://doi.org/10.1093/jjfinec/nbt003.

  24. Cont, R., Stoikov, S., & Talreja, R. (2010). A stochastic model for order book dynamics. Operations Research, 58(3), 549–563. https://doi.org/10.1287/opre.1090.0780.

  25. Duffin, M., & Cartlidge, J. (2018). Agent‐based model exploration of latency arbitrage in fragmented financial markets. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2312–2320). IEEE. https://doi.org/10.1109/SSCI.2018.8628638.
    Paper not yet in RePEc: Add citation now
  26. Dufour, J.‐M., Garcia, R., & Taamouti, A. (2012). Measuring high‐frequency causality between returns, realized volatility, and implied volatility. Journal of Financial Econometrics, 10(1), 124–163. https://doi.org/10.1093/jjfinec/nbr007.
    Paper not yet in RePEc: Add citation now

  27. Fama, E. F. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575–1617. https://doi.org/10.1111/j.1540-6261.1991.tb04636.x.

  28. Feng, L., Li, B., Podobnik, B., Preis, T., & Stanley, E. H. (2012). Linking agent‐based models and stochastic models of financial markets. Proceedings of the National Academy of Sciences, 109(22), 8388–8393. https://doi.org/10.1073/pnas.1205013109.
    Paper not yet in RePEc: Add citation now

  29. Foucault, T., Kadan, O., & Kandel, E. (2005). Limit order book as a market for liquidity. The Review of Financial Studies, 18(4), 1171–1217. https://doi.org/10.1093/rfs/hhi029.

  30. Gareche, A., Disdier, G., Kockelkoren, J., & Bouchaud, J.‐P. (2013). Fokker–Planck description for the queue dynamics of large tick stocks. Physical Review E, 88(3), 032809. https://doi.org/10.1103/PhysRevE.88.032809.

  31. Gould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., & Howison, S. D. (2013). Limit order books. Quantitative Finance, 13(11), 1709–1742. https://doi.org/10.1080/14697688.2013.803148.
    Paper not yet in RePEc: Add citation now

  32. Gu, G.‐F., & Zhou, W.‐X. (2009). Emergence of long memory in stock volatility from a modified Mike‐Farmer model. EPL (Europhysics Letters), 86(4), 48002. https://doi.org/10.1209/0295-5075/86/48002.

  33. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of Wasserstein GANs. Advances in Neural Information Processing Systems, 30, 5769–5779. https://dl.acm.org/doi/10.5555/3295222.3295327.
    Paper not yet in RePEc: Add citation now
  34. Hawkes, A. G. (1971). Spectra of some self‐exciting and mutually exciting point processes. Biometrika, 58(1), 83–90. https://doi.org/10.1093/biomet/58.1.83.
    Paper not yet in RePEc: Add citation now
  35. Ho, J., & Salimans, T. (2021). Classifier‐free diffusion guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications. https://doi.org/10.48550/arXiv.2207.12598.
    Paper not yet in RePEc: Add citation now
  36. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851. https://doi.org/10.48550/arXiv.2006.11239.
    Paper not yet in RePEc: Add citation now
  37. Hochreiter, S., & Schmidhuber, J. (1997). Long short‐term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
    Paper not yet in RePEc: Add citation now
  38. Karpe, M., Fang, J., Ma, Z., & Wang, C. (2020). Multi‐agent reinforcement learning in a realistic limit order book market simulation. In Proceedings of the First ACM International Conference on AI in Finance (pp. 1–7). Association for Computing Machinery. https://doi.org/10.1145/3383455.3422570.
    Paper not yet in RePEc: Add citation now
  39. Kingma, D. P., Mohamed, S., Rezende, D. J., & Welling, M. (2014). Semi‐supervised learning with deep generative models. Advances in Neural Information Processing Systems, 27, 3581–3589. https://doi.org/10.5555/2969033.2969226.
    Paper not yet in RePEc: Add citation now
  40. Kumar, P. (2021). Deep Hawkes process for high‐frequency market making. arXiv, 2109.15110. https://doi.org/10.48550/arXiv.2109.15110.
    Paper not yet in RePEc: Add citation now
  41. Leal, S. J., & Napoletano, M. (2019). Market stability vs. market resilience: Regulatory policies experiments in an agent‐based model with low‐and high‐frequency trading. Journal of Economic Behavior & Organization, 157, 15–41. https://doi.org/10.1016/j.jebo.2017.04.013.
    Paper not yet in RePEc: Add citation now

  42. Lee, E. J. (2015). High frequency trading in the Korean index futures market. Journal of Futures Markets, 35(1), 31–51. https://doi.org/10.1002/fut.21640.

  43. Li, J., Wang, X., Lin, Y., Sinha, A., & Wellman, M. (2020). Generating realistic stock market order streams. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, pp. 727–734). AAAI Press. https://doi.org/10.1609/aaai.v34i01.5415.
    Paper not yet in RePEc: Add citation now

  44. Lillo, F., & Farmer, D. J. (2004). The long memory of the efficient market. Studies in Nonlinear Dynamics and Econometrics, 8(3), 1–33. https://doi.org/10.2202/1558-3708.1226.

  45. Lillo, F., Farmer, J. D., & Mantegna, R. N. (2003). Master curve for price‐impact function. Nature, 421(6919), 129–130. https://doi.org/10.1038/421129a.
    Paper not yet in RePEc: Add citation now

  46. McGroarty, F., Booth, A., Gerding, E., & Chinthalapati, V. L. (2019). High frequency trading strategies, market fragility and price spikes: An agent based model perspective. Annals of Operations Research, 282(1), 217–244. https://doi.org/10.1007/s10479-018-3019-4.

  47. Mei, H., & Eisner, J. M. (2017). The neural Hawkes process: A neurally self‐modulating multivariate point process. Advances in Neural Information Processing Systems, 30, 6757–6767. https://dl.acm.org/doi/10.5555/3295222.3295420.
    Paper not yet in RePEc: Add citation now
  48. Morariu‐Patrichi, M., & Pakkanen, M. S. (2022). State‐dependent Hawkes processes and their application to limit order book modelling. Quantitative Finance, 22(3), 563–583. https://doi.org/10.1080/14697688.2021.1983199.
    Paper not yet in RePEc: Add citation now

  49. Nawn, S., & Banerjee, A. (2019). Do the limit orders of proprietary and agency algorithmic traders discover or obscure security prices? Journal of Empirical Finance, 53, 109–125. https://doi.org/10.1016/j.jempfin.2019.06.003.

  50. Næs, R., & Skjeltorp, J. A. (2006). Order book characteristics and the volume–volatility relation: Empirical evidence from a limit order market. Journal of Financial Markets, 9(4), 408–432. https://doi.org/10.1016/j.finmar.2006.04.001.

  51. Nolte, I., Salmon, M., & Adcock, C. (2016). High frequency trading and limit order book dynamics. Routledge. https://doi.org/10.4324/9781315737676.
    Paper not yet in RePEc: Add citation now
  52. Paddrik, M., Hayes, R., Todd, A., Yang, S., Beling, P., & Scherer, W. (2012). An agent based model of the E‐Mini S&P 500 applied to flash crash analysis. In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (pp. 1–8). IEEE. https://doi.org/10.1109/CIFEr.2012.6327800.
    Paper not yet in RePEc: Add citation now

  53. Panayi, E., & Peters, G. W. (2015). Stochastic simulation framework for the limit order book using liquidity‐motivated agents. International Journal of Financial. Engineering, 2(2), 1550013. https://doi.org/10.1142/S2424786315500139.

  54. Peng, C.‐K., Buldyrev, S. V., Havlin, S., Michael Simons, H., Stanley, E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), 1685. https://doi.org/10.1103/physreve.49.1685.
    Paper not yet in RePEc: Add citation now
  55. Preis, T., Golke, S., Paul, W., & Schneider, J. J. (2007). Statistical analysis of financial returns for a multiagent order book model of asset trading. Physical Review E, 76(1), 016108. https://doi.org/10.1103/PhysRevE.76.016108.
    Paper not yet in RePEc: Add citation now
  56. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684–10695). IEEE. https://doi.org/10.1109/CVPR52688.2022.01042.
    Paper not yet in RePEc: Add citation now

  57. Schulmeister, S. (2009). Profitability of technical stock trading: Has it moved from daily to intraday data? Review of Financial Economics, 18(4), 190–201. https://doi.org/10.1016/j.rfe.2008.10.001.

  58. Serrano, A. S. (2020). High‐frequency trading and systemic risk: A structured review of findings and policies. Review of Economics, 71(3), 169–195. https://doi.org/10.1515/roe-2020-0028.
    Paper not yet in RePEc: Add citation now
  59. Shi, Z., & Cartlidge, J. (2021). The limit order book recreation model (LOBRM): An extended analysis. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 204–220). Springer. https://doi.org/10.1007/978-3-030-86514-6_13.
    Paper not yet in RePEc: Add citation now
  60. Shi, Z., & Cartlidge, J. (2022). State dependent parallel neural Hawkes process for limit order book event stream prediction and simulation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1607–1615). Association for Computing Machinery. https://doi.org/10.1145/3534678.3539462.
    Paper not yet in RePEc: Add citation now
  61. Shi, Z., Yu, C., & Cartlidge, J. (2021). The LOB recreation model: Predicting the limit order book from TAQ history using an ordinary differential equation recurrent neural network. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35(1), pp. 548–556). AAAI Press. https://doi.org/10.1609/aaai.v35i1.16133.
    Paper not yet in RePEc: Add citation now

  62. Sirignano, J., & Cont, R. (2019). Universal features of price formation in financial markets: Perspectives from deep learning. Quantitative Finance, 19(9), 1449–1459. https://doi.org/10.1080/14697688.2019.1622295.

  63. Stanley, E. H., Plerou, V., & Gabaix, X. (2008). A statistical physics view of financial fluctuations: Evidence for scaling and universality. Physica A: Statistical Mechanics and its Applications, 387(15), 3967–3981. https://doi.org/10.1016/j.physa.2008.01.093.

  64. Vyetrenko, S., Byrd, D., Petosa, N., Mahfouz, M., Dervovic, D., Veloso, M., & Balch, T. (2020). Get real: Realism metrics for robust limit order book market simulations. In Proceedings of the First ACM International Conference on AI in Finance (pp. 1–8). ACM. https://doi.org/10.1145/3383455.3422561.
    Paper not yet in RePEc: Add citation now

  65. Wah, E., & Wellman, M. P. (2016). Latency arbitrage in fragmented markets: A strategic agent‐based analysis. Algorithmic Finance, 5(3–4), 69–93. https://doi.org/10.3233/AF-160060.

  66. Wang, X., Hoang, C., Vorobeychik, Y., & Wellman, M. P. (2021). Spoofing the limit order book: A strategic agent‐based analysis. Games, 12(2), 46. https://doi.org/10.3390/g12020046.

  67. Yagemann, C., Chung, S. P., Uzun, E., Ragam, S., Saltaformaggio, B., & Lee, W. (2020). On the feasibility of automating stock market manipulation. In Annual Computer Security Applications Conference (pp. 277–290). Association for Computing Machinery. https://doi.org/10.1145/3427228.3427241.
    Paper not yet in RePEc: Add citation now
  68. Zhang, Z., Zohren, S., & Roberts, S. (2019). Deeplob: Deep convolutional neural networksfor limit order books. IEEE Transactions on Signal Processing, 67(11), 3001–3012. https://doi.org/10.1109/TSP.2019.2907260.
    Paper not yet in RePEc: Add citation now

  69. Zhou, R. T., & Lai, R. N. (2009). Herding and information based trading. Journal of Empirical Finance, 16(3), 388–393. https://doi.org/10.1016/j.jempfin.2009.01.004.

Cocites

Documents in RePEc which have cited the same bibliography

  1. David vs Goliath (You against the Markets), A Dynamic Programming Approach to Separate the Impact and Timing of Trading Costs. (2021). Kashyap, Ravi.
    In: Papers.
    RePEc:arx:papers:1603.00984.

    Full description at Econpapers || Download paper

  2. An FBSDE approach to market impact games with stochastic parameters. (2019). Drapeau, Samuel ; Luo, Peng ; Xiong, Dewen ; Schied, Alexander.
    In: Papers.
    RePEc:arx:papers:2001.00622.

    Full description at Econpapers || Download paper

  3. Periodic strategies in optimal execution with multiplicative price impact. (2018). Moreno-Franco, Harold A ; Hern, Daniel .
    In: Papers.
    RePEc:arx:papers:1705.00284.

    Full description at Econpapers || Download paper

  4. Optimal Liquidation Problems in a Randomly-Terminated Horizon. (2017). Ching, Wai-Ki ; Wong, Tak Kwong ; Yang, Qing-Qing ; Gu, Jia-Wen.
    In: Papers.
    RePEc:arx:papers:1709.05837.

    Full description at Econpapers || Download paper

  5. Liquidity Effects of Trading Frequency. (2017). Nadtochiy, Sergey ; Gayduk, Roman.
    In: Papers.
    RePEc:arx:papers:1508.07914.

    Full description at Econpapers || Download paper

  6. Optimal Asset Liquidation with Multiplicative Transient Price Impact. (2017). Bilarev, Todor ; Frentrup, Peter ; Becherer, Dirk.
    In: Papers.
    RePEc:arx:papers:1501.01892.

    Full description at Econpapers || Download paper

  7. Hedging with Temporary Price Impact. (2016). Bank, Peter ; Voss, Moritz ; Soner, Mete.
    In: Papers.
    RePEc:arx:papers:1510.03223.

    Full description at Econpapers || Download paper

  8. Optimal order display in limit order markets with liquidity competition. (2015). Horst, Ulrich ; Cebirolu, Gokhan.
    In: Journal of Economic Dynamics and Control.
    RePEc:eee:dyncon:v:58:y:2015:i:c:p:81-100.

    Full description at Econpapers || Download paper

  9. Regularity properties in a state-constrained expected utility maximization problem. (2015). Lazgham, Mourad.
    In: Papers.
    RePEc:arx:papers:1510.03079.

    Full description at Econpapers || Download paper

  10. Optimal Portfolio Liquidation in Target Zone Models and Catalytic Superprocesses. (2015). Neuman, Eyal ; Schied, Alexander.
    In: Papers.
    RePEc:arx:papers:1504.06031.

    Full description at Econpapers || Download paper

  11. A dynamic optimal execution strategy under stochastic price recovery. (2015). Ieda, Masashi.
    In: Papers.
    RePEc:arx:papers:1502.04521.

    Full description at Econpapers || Download paper

  12. Optimal Derivative Liquidation Timing Under Path-Dependent Risk Penalties. (2015). Shirai, Yoshihiro ; Leung, Tim.
    In: Papers.
    RePEc:arx:papers:1502.00358.

    Full description at Econpapers || Download paper

  13. Facilitation and Internalization Optimal Strategy in a Multilateral Trading Context. (2015). Li, Qinghua.
    In: Papers.
    RePEc:arx:papers:1404.7320.

    Full description at Econpapers || Download paper

  14. A state-constrained differential game arising in optimal portfolio liquidation. (2015). Zhang, Tao ; Schied, Alexander.
    In: Papers.
    RePEc:arx:papers:1312.7360.

    Full description at Econpapers || Download paper

  15. The General Structure of Optimal Investment and Consumption with Small Transaction Costs. (2015). Muhle-Karbe, Johannes ; Kallsen, Jan.
    In: Papers.
    RePEc:arx:papers:1303.3148.

    Full description at Econpapers || Download paper

  16. Hedging Market Risk in Optimal Liquidation. (2014). Monin, Phillip.
    In: Working Papers.
    RePEc:ofr:wpaper:14-08.

    Full description at Econpapers || Download paper

  17. Optimal execution with nonlinear transient market impact. (2014). Gatheral, Jim ; Curato, Gianbiagio ; Lillo, Fabrizio.
    In: Papers.
    RePEc:arx:papers:1412.4839.

    Full description at Econpapers || Download paper

  18. High-Resilience Limits of Block-Shaped Order Books. (2014). Muhle-Karbe, Johannes ; Kallsen, Jan.
    In: Papers.
    RePEc:arx:papers:1409.7269.

    Full description at Econpapers || Download paper

  19. Optimal Execution with Dynamic Order Flow Imbalance. (2014). Ludkovski, Mike ; Bechler, Kyle .
    In: Papers.
    RePEc:arx:papers:1409.2618.

    Full description at Econpapers || Download paper

  20. A convex duality method for optimal liquidation with participation constraints. (2014). Olivier Gu'eant, ; Pu, Jiang ; Lasry, Jean-Michel.
    In: Papers.
    RePEc:arx:papers:1407.4614.

    Full description at Econpapers || Download paper

  21. Stylized facts of price gaps in limit order books: Evidence from Chinese stocks. (2014). Zhou, Wei-Xing ; Zhang, Yong-Jie ; Xiong, Xiong ; Chen, Wei ; Gu, Gao-Feng.
    In: Papers.
    RePEc:arx:papers:1405.1247.

    Full description at Econpapers || Download paper

  22. A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution. (2014). Hendricks, Dieter ; Wilcox, Diane.
    In: Papers.
    RePEc:arx:papers:1403.2229.

    Full description at Econpapers || Download paper

  23. Permanent market impact can be nonlinear. (2014). Olivier Gu'eant, .
    In: Papers.
    RePEc:arx:papers:1305.0413.

    Full description at Econpapers || Download paper

  24. Price manipulation in a market impact model with dark pool. (2014). Sun, Yuemeng ; Klock, Florian ; Schied, Alexander.
    In: Papers.
    RePEc:arx:papers:1205.4008.

    Full description at Econpapers || Download paper

  25. Optimal trading strategy and supply/demand dynamics. (2013). Obizhaeva, Anna ; Wang, Jiang.
    In: Journal of Financial Markets.
    RePEc:eee:finmar:v:16:y:2013:i:1:p:1-32.

    Full description at Econpapers || Download paper

  26. Economic Modeling for Optimal Trading of Financial Asset in Volatile Market. (2013). Sun, Edward ; Kruse, Timm.
    In: Economics Bulletin.
    RePEc:ebl:ecbull:eb-12-00627.

    Full description at Econpapers || Download paper

  27. A Monte Carlo method for optimal portfolio executions. (2013). Achtsis, Nico ; Nuyens, Dirk.
    In: Papers.
    RePEc:arx:papers:1312.5919.

    Full description at Econpapers || Download paper

  28. Market Impact Paradoxes. (2013). Skachkov, Igor .
    In: Papers.
    RePEc:arx:papers:1312.3349.

    Full description at Econpapers || Download paper

  29. Optimal Execution Trajectories. Linear Market Impact with Exponential Decay. (2013). Skachkov, Igor .
    In: Papers.
    RePEc:arx:papers:1309.6725.

    Full description at Econpapers || Download paper

  30. Optimal starting times, stopping times and risk measures for algorithmic trading: Target Close and Implementation Shortfall. (2013). LEHALLE, Charles-Albert ; Labadie, Mauricio.
    In: Papers.
    RePEc:arx:papers:1205.3482.

    Full description at Econpapers || Download paper

  31. Market Liquidity -- Theory and Empirical Evidence. (2012). Vayanos, Dimitri ; Wang, Jiang.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:18251.

    Full description at Econpapers || Download paper

  32. Optimal starting times, stopping times and risk measures for algorithmic trading. (2012). LEHALLE, Charles-Albert ; Labadie, Mauricio.
    In: Working Papers.
    RePEc:hal:wpaper:hal-00705056.

    Full description at Econpapers || Download paper

  33. Optimal trade execution: A mean quadratic variation approach. (2012). Tse, S. T. ; FORSYTH, P. A. ; Kennedy, J. S. ; Windcliff, H..
    In: Journal of Economic Dynamics and Control.
    RePEc:eee:dyncon:v:36:y:2012:i:12:p:1971-1991.

    Full description at Econpapers || Download paper

  34. High Frequency Market Making. (2012). Carmona, Rene ; Webster, Kevin.
    In: Papers.
    RePEc:arx:papers:1210.5781.

    Full description at Econpapers || Download paper

  35. Optimal Trading with Linear Costs. (2012). Potters, Marc ; de Lataillade, Joachim ; Bouchaud, Jean-Philippe ; Deremble, Cyril.
    In: Papers.
    RePEc:arx:papers:1203.5957.

    Full description at Econpapers || Download paper

  36. Portfolio liquidation in dark pools in continuous time. (2012). Schoneborn, Torsten ; Kratz, Peter.
    In: Papers.
    RePEc:arx:papers:1201.6130.

    Full description at Econpapers || Download paper

  37. Risk Premia and Optimal Liquidation of Credit Derivatives. (2012). Leung, Tim ; Liu, Peng.
    In: Papers.
    RePEc:arx:papers:1110.0220.

    Full description at Econpapers || Download paper

  38. Low order-value approach for solving VaR-constrained optimization problems. (2011). Kreji, N. ; Bueno, L. ; Martinez, J. ; Birgin, E..
    In: Journal of Global Optimization.
    RePEc:spr:jglopt:v:51:y:2011:i:4:p:715-742.

    Full description at Econpapers || Download paper

  39. Optimal trading execution with nonlinear market impact: an alternative solution method. (2011). Zagaglia, Paolo ; Ritelli, Daniele ; Marzo, Massimiliano ; Daniele, Ritelli ; Massmiliano, Marzo ; Paolo, Zagaglia .
    In: MPRA Paper.
    RePEc:pra:mprapa:35393.

    Full description at Econpapers || Download paper

  40. Dynamic trade execution: a grammatical evolution approach. (2011). Cui, Wei ; O'Neill, Michael ; Brabazon, Anthony.
    In: International Journal of Financial Markets and Derivatives.
    RePEc:ids:ijfmkd:v:2:y:2011:i:1/2:p:4-31.

    Full description at Econpapers || Download paper

  41. Optimal Portfolio Liquidation with Limit Orders. (2011). Gueant, Olivier ; Tapia, Joaquin Fernandez ; Lehalle, Charles-Albert.
    In: Economics Papers from University Paris Dauphine.
    RePEc:dau:papers:123456789/7391.

    Full description at Econpapers || Download paper

  42. Optimal Trading Execution with Nonlinear Market Impact: An Alternative Solution Method. (2011). Zagaglia, Paolo ; Ritelli, Daniele ; Marzo, Massimiliano.
    In: Working Papers.
    RePEc:bol:bodewp:wp797.

    Full description at Econpapers || Download paper

  43. A limit order book model for latency arbitrage. (2011). Cohen, Samuel N. ; Szpruch, Lukasz.
    In: Papers.
    RePEc:arx:papers:1110.4811.

    Full description at Econpapers || Download paper

  44. Optimal trade execution and price manipulation in order books with time-varying liquidity. (2011). Schöneborn, Torsten ; Schoeneborn, Torsten ; Urusov, Mikhail ; Fruth, Antje.
    In: Papers.
    RePEc:arx:papers:1109.2631.

    Full description at Econpapers || Download paper

  45. Optimal trade execution and absence of price manipulations in limit order book models. (2010). Schied, Alexander ; Alfonsi, Aurelien.
    In: Post-Print.
    RePEc:hal:journl:hal-00397652.

    Full description at Econpapers || Download paper

  46. Portfolio choice under transitory price impact. (2010). Isaenko, Sergei.
    In: Journal of Economic Dynamics and Control.
    RePEc:eee:dyncon:v:34:y:2010:i:11:p:2375-2389.

    Full description at Econpapers || Download paper

  47. Optimal Execution in an Evolutionary Setting. (2009). Ishii, Ryosuke.
    In: KIER Working Papers.
    RePEc:kyo:wpaper:670.

    Full description at Econpapers || Download paper

  48. Risk aversion and the dynamics of optimal liquidation strategies in illiquid markets. (2008). Schied, Alexander ; Schöneborn, Torsten ; Schoeneborn, Torsten .
    In: MPRA Paper.
    RePEc:pra:mprapa:7105.

    Full description at Econpapers || Download paper

  49. Liquidation in the Face of Adversity: Stealth Vs. Sunshine Trading, Predatory Trading Vs. Liquidity Provision. (2007). Schied, Alexander ; Schöneborn, Torsten.
    In: MPRA Paper.
    RePEc:pra:mprapa:5548.

    Full description at Econpapers || Download paper

  50. Optimal Trading Strategy and Supply/Demand Dynamics. (2005). Obizhaeva, Anna ; Wang, Jiang.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:11444.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-09-25 09:06:41 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.