• Previous Article
    A free boundary problem for defaultable corporate bond with credit rating migration risk and its asymptotic behavior
  • DCDS-B Home
  • This Issue
  • Next Article
    Chaotic cuttlesh: king of camouage with self-excited and hidden flows, its fractional-order form and communication designs with fractional form
March  2020, 25(3): 1015-1041. doi: 10.3934/dcdsb.2019206

A dynamic model of the limit order book

1. 

Department of Mathematics, Penn State University, McAllister Building, University Park, PA 16802, USA

2. 

Institut de Mathématiques de Jussieu - Paris Rive Gauche, CNRS, Sorbonne Université, Case 247, 4 Place Jussieu, 75252 Paris, France

Received  April 2018 Revised  April 2019 Published  March 2020 Early access  September 2019

We consider an equilibrium model of the Limit Order Book in a stock market, where a large number of competing agents post "buy" or "sell" orders. For the "one-shot" game, it is shown that the two sides of the LOB are determined by the distribution of the random size of the incoming order, and by the maximum price accepted by external buyers (or the minimum price accepted by external sellers). We then consider an iterated game, where more agents come to the market, posting both market orders and limit orders. Equilibrium strategies are found by backward induction, in terms of a value function which depends on the current sizes of the two portions of the LOB. The existence of a unique Nash equilibrium is proved under a natural assumption, namely: the probability that the external order is so large that it wipes out the entire LOB should be sufficiently small.

Citation: Alberto Bressan, Marco Mazzola, Hongxu Wei. A dynamic model of the limit order book. Discrete & Continuous Dynamical Systems - B, 2020, 25 (3) : 1015-1041. doi: 10.3934/dcdsb.2019206
References:
[1]

K. Back and S. Baruch, Information in securities markets: Kyle meets Glosten and Milgrom, Econometrica, 72 (2004), 433-465.  doi: 10.1111/j.1468-0262.2004.00497.x.  Google Scholar

[2]

K. Back and S. Baruch, Strategic liquidity provision in limit order markets, Econometrica, 81 (2013), 363-392.  doi: 10.3982/ECTA10018.  Google Scholar

[3]

P. Bank and D. Kramkov, A model for a large investor trading at market indifference prices. Ⅰ: Single-period case, Finance Stoch., 19 (2015), 449-472.  doi: 10.1007/s00780-015-0258-y.  Google Scholar

[4]

P. Bank and D. Kramkov, A model for a large investor trading at market indifference prices. Ⅱ: Continuous-time case, Ann. Appl. Probab., 25 (2015), 2708-2742.  doi: 10.1214/14-AAP1059.  Google Scholar

[5]

A. Bressan and G. Facchi, A bidding game in a continuum limit order book, SIAM J. Control Optim., 51 (2013), 3459-3485.  doi: 10.1137/120896359.  Google Scholar

[6]

A. Bressan and G. Facchi, Discrete bidding strategies for a random incoming order, SIAM J. Financial Math., 5 (2014), 50-70.  doi: 10.1137/130917685.  Google Scholar

[7]

A. Bressan and D. Wei, A bidding game with heterogeneous players, J. Optim. Theory Appl., 163 (2014), 1018-1048.  doi: 10.1007/s10957-014-0551-5.  Google Scholar

[8]

A. Bressan and H. Wei, Dynamic stability of the Nash equilibrium for a bidding game, Analysis & Applications, 14 (2016), 591-614.  doi: 10.1142/S0219530515500098.  Google Scholar

[9]

U. CetinR. Jarrow and P. Protter, Liquidity risk and arbitrage pricing theory, Finance Stoch., 8 (2004), 311-341.  doi: 10.1007/s00780-004-0123-x.  Google Scholar

[10]

R. Cont and A. Larrard, Price dynamics in a Markovian limit order book market, SIAM J. Financial Math., 4 (2013), 1-25.  doi: 10.1137/110856605.  Google Scholar

[11]

R. ContS. Stoikov and R. Talreja, A stochastic model for order book dynamics, Operations Research, 58 (2010), 549-563.  doi: 10.1287/opre.1090.0780.  Google Scholar

[12]

R. Gayduk and S. Nadtochiy, Liquidity effects of trading frequency, Math. Finance, 28 (2018), 839-876.  doi: 10.1111/mafi.12157.  Google Scholar

[13]

R. Gayduk and S. Nadtochiy, Endogenous formation of limit order book: The effects of trading frequency, SIAM J. Control Optim., 56 (2018), 1577-1619.  doi: 10.1137/16M1078045.  Google Scholar

[14]

M. D. GouldM. A. PorterS. WilliamsM. McDonaldD. J. Fenn and S. D. Howison, Limit order books, Quantitative Finance, 13 (2013), 1709-1742.  doi: 10.1080/14697688.2013.803148.  Google Scholar

[15]

F. Kelly and E. Yudovina, A Markov model of the limit order book: thresholds, recurrence, and trading strategies, Journal Math. of Operations Research, 43 (2018), 181-203.  doi: 10.1287/moor.2017.0857.  Google Scholar

[16]

A. LachapelleJ. M. LasryC. A. Lehalle and P. L. Lions, Efficiency of the price formation process in presence of high frequency participants: a mean field game analysis, Math. Financ. Econ., 10 (2016), 223-262.  doi: 10.1007/s11579-015-0157-1.  Google Scholar

[17]

C. Parlour and D. J. Seppi, Limit order markets: A survey, in Proceedings of the Handbook of Financial Intermediation and Banking (eds. A. Thakor and A. Boot), Elsevier, (2008), 63–96. doi: 10.1016/B978-044451558-2.50007-6.  Google Scholar

[18]

I. Rosu, A dynamic model of the limit order book, Review of Financial Studies, 22 (2009), 4601-4641.   Google Scholar

[19]

T. W. Yang and L. Zhu, A reduced-form model for level-1 limit order books, Market Microstructure and Liquidity, 2 (2016), 1650008. doi: 10.1142/S2382626616500088.  Google Scholar

show all references

References:
[1]

K. Back and S. Baruch, Information in securities markets: Kyle meets Glosten and Milgrom, Econometrica, 72 (2004), 433-465.  doi: 10.1111/j.1468-0262.2004.00497.x.  Google Scholar

[2]

K. Back and S. Baruch, Strategic liquidity provision in limit order markets, Econometrica, 81 (2013), 363-392.  doi: 10.3982/ECTA10018.  Google Scholar

[3]

P. Bank and D. Kramkov, A model for a large investor trading at market indifference prices. Ⅰ: Single-period case, Finance Stoch., 19 (2015), 449-472.  doi: 10.1007/s00780-015-0258-y.  Google Scholar

[4]

P. Bank and D. Kramkov, A model for a large investor trading at market indifference prices. Ⅱ: Continuous-time case, Ann. Appl. Probab., 25 (2015), 2708-2742.  doi: 10.1214/14-AAP1059.  Google Scholar

[5]

A. Bressan and G. Facchi, A bidding game in a continuum limit order book, SIAM J. Control Optim., 51 (2013), 3459-3485.  doi: 10.1137/120896359.  Google Scholar

[6]

A. Bressan and G. Facchi, Discrete bidding strategies for a random incoming order, SIAM J. Financial Math., 5 (2014), 50-70.  doi: 10.1137/130917685.  Google Scholar

[7]

A. Bressan and D. Wei, A bidding game with heterogeneous players, J. Optim. Theory Appl., 163 (2014), 1018-1048.  doi: 10.1007/s10957-014-0551-5.  Google Scholar

[8]

A. Bressan and H. Wei, Dynamic stability of the Nash equilibrium for a bidding game, Analysis & Applications, 14 (2016), 591-614.  doi: 10.1142/S0219530515500098.  Google Scholar

[9]

U. CetinR. Jarrow and P. Protter, Liquidity risk and arbitrage pricing theory, Finance Stoch., 8 (2004), 311-341.  doi: 10.1007/s00780-004-0123-x.  Google Scholar

[10]

R. Cont and A. Larrard, Price dynamics in a Markovian limit order book market, SIAM J. Financial Math., 4 (2013), 1-25.  doi: 10.1137/110856605.  Google Scholar

[11]

R. ContS. Stoikov and R. Talreja, A stochastic model for order book dynamics, Operations Research, 58 (2010), 549-563.  doi: 10.1287/opre.1090.0780.  Google Scholar

[12]

R. Gayduk and S. Nadtochiy, Liquidity effects of trading frequency, Math. Finance, 28 (2018), 839-876.  doi: 10.1111/mafi.12157.  Google Scholar

[13]

R. Gayduk and S. Nadtochiy, Endogenous formation of limit order book: The effects of trading frequency, SIAM J. Control Optim., 56 (2018), 1577-1619.  doi: 10.1137/16M1078045.  Google Scholar

[14]

M. D. GouldM. A. PorterS. WilliamsM. McDonaldD. J. Fenn and S. D. Howison, Limit order books, Quantitative Finance, 13 (2013), 1709-1742.  doi: 10.1080/14697688.2013.803148.  Google Scholar

[15]

F. Kelly and E. Yudovina, A Markov model of the limit order book: thresholds, recurrence, and trading strategies, Journal Math. of Operations Research, 43 (2018), 181-203.  doi: 10.1287/moor.2017.0857.  Google Scholar

[16]

A. LachapelleJ. M. LasryC. A. Lehalle and P. L. Lions, Efficiency of the price formation process in presence of high frequency participants: a mean field game analysis, Math. Financ. Econ., 10 (2016), 223-262.  doi: 10.1007/s11579-015-0157-1.  Google Scholar

[17]

C. Parlour and D. J. Seppi, Limit order markets: A survey, in Proceedings of the Handbook of Financial Intermediation and Banking (eds. A. Thakor and A. Boot), Elsevier, (2008), 63–96. doi: 10.1016/B978-044451558-2.50007-6.  Google Scholar

[18]

I. Rosu, A dynamic model of the limit order book, Review of Financial Studies, 22 (2009), 4601-4641.   Google Scholar

[19]

T. W. Yang and L. Zhu, A reduced-form model for level-1 limit order books, Market Microstructure and Liquidity, 2 (2016), 1650008. doi: 10.1142/S2382626616500088.  Google Scholar

Figure 1.  Left: a distribution function for the random variable $ X $, describing the size of the external order. Right: a possible shape of the limit order book. If the external order is a buy order with size $ X>0 $, all the stocks in the shaded region on the right (with area $ = X $), will be sold. If the external order is a sell order for an amount $ Y>0 $ of stocks, all the buy orders in the shaded region on the left (with area $ = Y $), will be executed
Figure 2.  A plot of the density function $ \phi $, with data as in (39). In this case, solving (36)–(38) we find $ p_A = 10.0831 $, $ p_B = 9.6097 $, $ \bar p = 9.8464 $
Figure 3.  A plot of the functions $ U(p) $ in (11) and (21), with data as in (39)
Figure 4.  The ask price $ p_A $ is found by solving the Cauchy problem (107), (15), and finding the price at which $ U = 0 $. To estimate the rate at which $ p_A $ changes with the boundary data $ \overline p $, it is convenient to invert the role of the variables $ U, p $, thus obtaining the linear ODE (109) for $ p = p(U) $. The figure shows how $ p_A $ changes when the value of $ \overline p $ is increased
[1]

Jin Ma, Xinyang Wang, Jianfeng Zhang. Dynamic equilibrium limit order book model and optimal execution problem. Mathematical Control & Related Fields, 2015, 5 (3) : 557-583. doi: 10.3934/mcrf.2015.5.557

[2]

Ali Naimi-Sadigh, S. Kamal Chaharsooghi, Marzieh Mozafari. Optimal pricing and advertising decisions with suppliers' oligopoly competition: Stakelberg-Nash game structures. Journal of Industrial & Management Optimization, 2021, 17 (3) : 1423-1450. doi: 10.3934/jimo.2020028

[3]

Yu Chen, Zixian Cui, Shihan Di, Peibiao Zhao. Capital asset pricing model under distribution uncertainty. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021113

[4]

Wai-Ki Ching, Tang Li, Sin-Man Choi, Issic K. C. Leung. A tandem queueing system with applications to pricing strategy. Journal of Industrial & Management Optimization, 2009, 5 (1) : 103-114. doi: 10.3934/jimo.2009.5.103

[5]

Jaimie W. Lien, Vladimir V. Mazalov, Jie Zheng. Pricing equilibrium of transportation systems with behavioral commuters. Journal of Dynamics & Games, 2020, 7 (4) : 335-350. doi: 10.3934/jdg.2020026

[6]

Zhenkai Lou, Fujun Hou, Xuming Lou. Optimal ordering and pricing models of a two-echelon supply chain under multipletimes ordering. Journal of Industrial & Management Optimization, 2021, 17 (6) : 3099-3111. doi: 10.3934/jimo.2020109

[7]

Yu-Chung Tsao, Hanifa-Astofa Fauziah, Thuy-Linh Vu, Nur Aini Masruroh. Optimal pricing, ordering, and credit period policies for deteriorating products under order-linked trade credit. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021152

[8]

Mitali Sarkar, Young Hae Lee. Optimum pricing strategy for complementary products with reservation price in a supply chain model. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1553-1586. doi: 10.3934/jimo.2017007

[9]

Jing Zhang, Jianquan Lu, Jinde Cao, Wei Huang, Jianhua Guo, Yun Wei. Traffic congestion pricing via network congestion game approach. Discrete & Continuous Dynamical Systems - S, 2021, 14 (4) : 1553-1567. doi: 10.3934/dcdss.2020378

[10]

Xue-Yan Wu, Zhi-Ping Fan, Bing-Bing Cao. Cost-sharing strategy for carbon emission reduction and sales effort: A nash game with government subsidy. Journal of Industrial & Management Optimization, 2020, 16 (4) : 1999-2027. doi: 10.3934/jimo.2019040

[11]

Elvio Accinelli, Bruno Bazzano, Franco Robledo, Pablo Romero. Nash Equilibrium in evolutionary competitive models of firms and workers under external regulation. Journal of Dynamics & Games, 2015, 2 (1) : 1-32. doi: 10.3934/jdg.2015.2.1

[12]

Shaokun Tao, Xianjin Du, Suresh P. Sethi, Xiuli He, Yu Li. Equilibrium decisions on pricing and innovation that impact reference price dynamics. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021157

[13]

Patrick Beißner, Emanuela Rosazza Gianin. The term structure of sharpe ratios and arbitrage-free asset pricing in continuous time. Probability, Uncertainty and Quantitative Risk, 2021, 6 (1) : 23-52. doi: 10.3934/puqr.2021002

[14]

Yannick Viossat. Game dynamics and Nash equilibria. Journal of Dynamics & Games, 2014, 1 (3) : 537-553. doi: 10.3934/jdg.2014.1.537

[15]

Tak Kuen Siu, Howell Tong, Hailiang Yang. Option pricing under threshold autoregressive models by threshold Esscher transform. Journal of Industrial & Management Optimization, 2006, 2 (2) : 177-197. doi: 10.3934/jimo.2006.2.177

[16]

Zhuo Jin, Linyi Qian. Lookback option pricing for regime-switching jump diffusion models. Mathematical Control & Related Fields, 2015, 5 (2) : 237-258. doi: 10.3934/mcrf.2015.5.237

[17]

Tomasz R. Bielecki, Igor Cialenco, Marek Rutkowski. Arbitrage-free pricing of derivatives in nonlinear market models. Probability, Uncertainty and Quantitative Risk, 2018, 3 (0) : 2-. doi: 10.1186/s41546-018-0027-x

[18]

Marianito R. Rodrigo, Rogemar S. Mamon. Bond pricing formulas for Markov-modulated affine term structure models. Journal of Industrial & Management Optimization, 2021, 17 (5) : 2685-2702. doi: 10.3934/jimo.2020089

[19]

Po-Chung Yang, Hui-Ming Wee, Shen-Lian Chung, Yong-Yan Huang. Pricing and replenishment strategy for a multi-market deteriorating product with time-varying and price-sensitive demand. Journal of Industrial & Management Optimization, 2013, 9 (4) : 769-787. doi: 10.3934/jimo.2013.9.769

[20]

Jianxiong Zhang, Zhenyu Bai, Wansheng Tang. Optimal pricing policy for deteriorating items with preservation technology investment. Journal of Industrial & Management Optimization, 2014, 10 (4) : 1261-1277. doi: 10.3934/jimo.2014.10.1261

2020 Impact Factor: 1.327

Metrics

  • PDF downloads (224)
  • HTML views (286)
  • Cited by (0)

Other articles
by authors

[Back to Top]