Agent-based model for an order-driven market: herding effect, limit order strategies, and volatility enhanced trading activities

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：54

、訪客IP：3.139.72.210

姓名

余韋德(Wei-Te Yu) 查詢紙本館藏

畢業系所

物理學系

論文名稱

(Agent-based model for an order-driven market: herding effect, limit order strategies, and volatility enhanced trading activities)

相關論文

★ 鍺銻碲相變化奈米薄膜之奈米尺度光熱性質的研究	★ 波在一維系統中的傳播與局域化
★ 生物膜黏著引發的相分離—等效膜勢與數值模擬	★ 非平衡生物膜上的區塊形成
★ 液滴上的彈性網絡	★ 黏著叢集在時變外力下的強度
★ Modeling geometrical trajectories of actin-based motility	★ 隨機布耳網路在多連線且臨界情形下的特性
★ 模擬脂質雙層膜上的分子機器	★ 組織動力學之建模
★ Cell motility: active gel coupled to adhesion sites	★ Dynamics of the free boundary of a monolayer cell sheet
★ Onset of movement in a one-dimensional active gel model of cell motility	★ Hydrodynamics and spontaneous flow of active permeating polar gels
★ Complex one-dimensional motion in complex soft matter systems	★ Bacterial chemotaxis in random environment

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

我們建立了在限價委託簿(limit oeder book)上以雙向拍賣制度(double aucton)運作的代理人基模型(agent-based model)。初步透過無策略的的代理人與具有羊群效應(herding effect)的代理人進行模擬交易比較，在具有羊群效應的模型中我們設定以決策後選擇市價單(market order)的代理人會出現羊群效應。羊群效應即是個體因從眾心理而表現出來的群體一致性行動。在金融市場中，即是交易者們受到過往市場價格變動的影響而產生決策收斂做出相同動作的現象，當市場價格波動越大時，一致性會越明顯。由於市場交易者各自有著不同的操作週期，所以模型中也考慮了交易者具備不同長度的歷史價格變動記憶。基於真實金融數據做統計分析後得到的典型事實(stylized facts)，如價格波動的厚尾分佈(fat-tail distribution)和volume imbalance等，在我們的模擬結果也能得到。從結果來看，羊群效應確實可以造成更劇烈的價格變動，且記憶長度越長時，價格變動(volatility)也越劇烈。另外我們發現了一些新現象，在價格變動的自相關(autocorrelation of volatility)分析上，價格變動的記憶效應遠低於交易者對價格變動的記憶長度，我們認為這可能是來自於限價單(limit order)的影響。另外在volume imbalance和價格獲利率(price return)的關係上，羊群效應降低了在small imbalance時產生大價格變動的機會。在初步的模型中也無法得到如真實市場的spread-volatility關係，對此我們亦認為可能是受到限價單置放到限價委託簿上的方式所影響造成。於是我們比較了三種不同機制所得的結果，第一個機制即羊群效應，第二個機制是限價單的置放位置會受spread大小的影響，第三個機制則是交易活動熱烈程度會與過去的價格變動大小呈正相關。

摘要(英)

We build an agent-based model of an order-driven market with double auction. At ?rst, we start with the comparison of non-interaction agents with no strategies and the herding agents who submit market orders. The simulation results reproduce some stylized facts
such as fat-tailed distribution of volatility and volume imbalance. The herding e?ect is implemented by aggregation of agents who take market orders into opinion groups. The number of opinion groups in a simulation step is determined from previous volatilities of the
market as di?erent agents compare the price change over di?erent time intervals. Besides con?rming that when herding is included the tail of the distribution of volatility is enhanced, we found several new results. First, the autocorrelation time of volatility is much shorter than the memory of most of the agents because limit orders have strong in?uence on the location
of best bid and best ask. Second, from the relation between bid-ask imbalance and price return we ?nd that herding reduces the chance for a small imbalance to produce a large price change. We ?nd that the relation between spread and volatility in our preliminary model
does not agree with empirical data, we think limit orders have strong e?ects on the stylized features. Next, we compare the e?ects of three mechanisms. Our ?rst mechanism is opinion aggregation, i.e., herding of agents in response to price volatility in the market is studied.
The second mechanism is the way that limit orders placed by the agents are a?ected by the size of the spread in the limit order book. The third mechanism is the enhanced trading activities in the presence of large volatility.

關鍵字(中)

★ 代理人基模型
★ 限價單委託簿
★ 羊群效應
★ 經濟物理
★ 雙向拍賣制度

關鍵字(英)

★ agent-based model
★ limit order book
★ herding effect
★ econophysics
★ double auction

論文目次

1 Introduction
1
1.1 Econophysics
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
1.2 Stylized facts
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.2.1 Financial time series . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.2.2 Fat-tailed distribution of returns . . . . . . . . . . . . . . . . . . . . .
4
1.2.3 Autocorrelation of volatility . . . . . . . . . . . . . . . . . . . . . . .
5
1.3 The order-driven market in double auction mechanism
. . . . . . . . . . . . 10
1.4 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Agent-based model and herding model
16
2.1 Agent-based model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 herding model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Simulation process
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 Results and discussion I
22
3.1 Distribution of price return . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Spread, ?rst gap and volatility . . . . . . . . . . . . . . . . . . . . . . . . . . 29
I
3.3 Summary of chapter 3
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Limit order strategies
36
4.1 Rules for submitting limit orders
. . . . . . . . . . . . . . . . . . . . . . . . 37
4.2 Increasing trading activity when market is more volatile . . . . . . . . . . . . 38
4.3 Simulation process
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Results and discussion II
40
5.1 Distribution function of volatility . . . . . . . . . . . . . . . . . . . . . . . . 40
5.2 Autocorrelation of volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3 Spread-volatility relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.4 Model with herding of limit orders
. . . . . . . . . . . . . . . . . . . . . . . 44
6 Conclusions and future works
55
A Model 1 with L=6
58
B Model 2 with di?erent agent number
61
C Detailed study of the ACF of volatility
63
D The relation between volume imbalance and price return for all models 67
Bibliography
70

參考文獻

[1] S. Maslov. Simple model of a limit order-driven market. Physica A, 278(3-4), 571-578
(2000).
[2] L. Bachelier Th’eorie de la sp’eculation. Gauthier-Villars, 1900.
[3] A. Einstein, On the motion of small particles suspended in liquids at rest required by
the molecular-kinetic theory of heat. Annalen der physik, 17, 549-560 (1905).
[4] F. Black and M Scholes The pricing of options and corporate liabilities. Journal of
political economy, 81(3),) 637-654 (1973.
[5] M. Kardar. Statistical physics of particles. Cambridge University Press, 2007.
[6] D. Sornette Dragon-kings, black swans and the prediction of crises. arXiv preprint arXiv
0907.4290 (2009).
[7] A. Chakraborti, I. M. Toke, M. Patriarca, and F. Abergel. Statistical theory of the
continuous double auction. Quantitative Finance, 3(6), 481-514 (2003).
[8] R.N. Mantegna and H.E. Stanley. An introduction to econophysics: correlation and
complexity in ?nance. Cambridge University Press, Cambridge, 2000.
[9] F. Slanina. Essentials of econophysics modelling. OUP Oxford, 2013.
[10] L. Feng, B. Li, B. Podobnik, T. Preis, and H.E. Stanley. Linking agent-based models
and stochastic models of ?nancial markets. Proceedings of the National Academy of
Sciences, 109(22), 8388-8393 (2012).
[11] P. Weber and B. Rosenow, Order book approach to price impact. Quantitative Finance,
5(4), 357-364 (2005).
[12] E. Moro, J. Vicente, L. G. Moyano, A. Gerig, J. D. Farmer, G. Vaglica, F. Lillo and
R. N. Mantegna. Market impact and trading pro?le of hidden orders in stock markets.
Physical Review E, 80(6), 066102 (2009).
[13] I. Mastromatteo and B. T’oth. and J.-P. Bouchaud. Anomalous impact in reaction-
di?usion ?nancial models. Physical Review Letters, 113(26), 268701 (2014).
[14] R.N. Mantegna and H.E. Stanley. Scaling behaviour in the dynamics of an economic
index. Nature, 376(6535), 46 (1995).
[15] P. Gopikrishnan, V. Plerou, Luis A.N. Amaral, M. Meyer, and H.E. Stanley. Scaling
of the distribution of ?uctuations of ?nancial market indices. Physical Review E, 60(5),
5305 (1999).
[16] J.-J. Chen, B. Zheng, and L. Tan. Agent-based model with asymmetric trading and
herding for complex ?nancial systems. PloS one, 8(11), e79531 (2013).
[17] G. Caldarelli, M. Marsili, and Y.-C. Zhang. A prototype model of stock exchange. EPL
(Europhysics Letters), 40(5), 479 (1997).
[18] R. Murakami, T. Nakamura, S. Kimura, M. Manabe, and T. Tanizawa. On possible
origins of trends in ?nancial market price changes. Physica A, 420, 179–189 (2015).
[19] V. Plerou, P. Gopikrishnan, and H.E. Stanley. Quantifying ?uctuations in market
liquidity: Analysis of the bid-ask spread. Physical Review E, 71(4), 046131 (2005).
[20] W.-J. Ma, S.-C. Wang, C.-N. Chen, and C.-K. Hu. Crossover behavior of stock returns
and mean square displacements of particles governed by the Langevin equation. EPL
(Europhysics Letters), 102, 66003 (2013).
[21] I. O. Baycan, G. Yildirim. Examining the dynamics of the Turkish manufacturing
industry: A hidden Markov model approach. Chin. J. Phys., 55(3), 703-709 (2017).
[22] J. Doiner and J.-P. Bouchaud. From Walras auctioneer to continuous time double
auctions: a general dynamic theory of supply and demand J. Stat. Mech., 123406 (2011).
[23] A. Chakraborti, I. M. Toke, M. Patriarca, and F. Abergel. Econophysics review: I.
empirical facts. Quantitative Finance, 11(7), 991–1012 (2011).
[24] A. Chakraborti, I. M. Toke, M. Patriarca, and F. Abergel. Econophysics review: II.
agent-based models. Quantitative Finance, 11(7), 1013-1041 (2011).
[25] B. T’oth, J. Kert’
esz, and J.D. Farmer. Studies of the limit order book around large price
changes. The European Physical Journal B-Condensed Matter and Complex Systems,
71(4), 499-510 (2009).
[26] J.D. Farmer, L. Gillemot, F. Lillo, S. Mike, and A. Sen. What really causes large price
changes? Quantitative ?nance, 4(4), 383–397 (2004).
[27] Kee-Hong Bae, Hasung Jang, and Kyung Suh Park. Traders choice between limit and
market orders: evidence from NYSE stocks Journal of Financial Markets, 6(4), 517–538
(2003).
[28] W.-T. Yu and H.-Y. Chen. to be submitted.
[29] M. G. Daniels, J.D. Farmer, L. Gillemot, G. Iori, and E. Smith. Quantitative model
of price di?usion and market friction based on trading as a mechanistic random process.
Physical review letters, 90(10), 108102 (2003).
[30] L. Menkho?. The use of technical analysis by fund managers: International evidence.
Journal of Banking & Finance, 34(11), 2573-2586 (2010).
[31] R. Cont. Empirical properties of asset returns: stylized facts and statistical issues.
Quantitative Finance, 1(2), 223–236 (2001).
[32] J.-P. Bouchaud, J.D. Farmer, and F. Lillo, Handbook of Financial Markets: Dynamics
and Evolution. North-Holland, Amsterdam, 57-160, 2009.
[33] V. Plerou, P. Gopikrishnan, X. Bavaix, and H.E. Stanley, Quantifying stock-price
response to demand ?uctuations. Physical Review E, 66(2), 027104 (2002).
[34] Y. Yura, H. Takayasu, and D. Sornette and M. Takayasu. Financial brownian particle in
the layered order-book ?uid and ?uctuation-dissipation relations. Physical review letters,
112.9, 098703 (2014).
[35] Y. Yura, H. Takayasu, and D. Sornette and M. Takayasu. Financial Knudsen num-
ber: Breakdown of continuous price dynamics and asymmetric buy-and-sell structures
con?rmed by high-precision order-book information. Physical Review E, 92.4, 042811
(2015).
[36] K. Kanazawa, T. Sueshige H. Takayasu and M. Takayasu. Derivation of the Boltzmann
equation for ?nancial Brownian motion: Direct observation of the collective motion of
high-frequency traders. Physical review letters, 120.13, 138301 (2018).
[37] W.-T. Yu and H.-Y. Chen. Herding and zero-intelligence agents in the order book
dynamics of an arti?cial double auction market. Chinese Journal of Physics, (2018).
[38] J. Donier,J. Bonart, I. Mastromatteo and J. P. Bouchaud. A fully consistent, minimal
model for non-linear market impact. Quantitative ?nance, 15.7, 1109-1121 (2015).
[39] M. Cristelli, V. Al?, L. Pietronero, and A. Zaccaria Liquidity crisis, granularity of the
order book and price ?uctuations. The European Physical Journal B, 73.1, 41-49 (2010).
[40] D. Challet, M. Marsili, Y-C. Zhang Minority games: interacting agents in ?nancial
markets. Oxford University Press, Oxford, UK, 2005.

指導教授

陳宣毅(Hsuan-Yi Chen)

審核日期

2018-7-30

推文