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論文名稱 Title |
隨機市場深度模型之最佳訂單執行研究 A Study on the Optimal Order Execution Problem for Stochastic Market Depth Models |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
41 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2016-07-11 |
繳交日期 Date of Submission |
2016-09-12 |
關鍵字 Keywords |
馬可夫鏈、奧恩斯坦--烏倫貝克過程、高頻交易、分割演算法、市場深度、馬可夫決策過程、最佳訂單執行問題 market depth, Markov decision process, optimal order execution problem, partitioning algorithm, Markov chain, Ornstein--Uhlenbeck process, high-frequency transaction |
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統計 Statistics |
本論文已被瀏覽 5713 次,被下載 31 次 The thesis/dissertation has been browsed 5713 times, has been downloaded 31 times. |
中文摘要 |
最佳訂單執行問題是投資者需考慮的重要議題。投資者如何將一個大訂單分割成許多的連續小訂單,才能將交易成本最小化?Chen, Kou, and Wang (2015) 提出了一個限價單模型的分割演算法來解決此問題。他們假設市場深度是由馬可夫鏈生成的隨機過程,並將整個交易過程建構在馬可夫決策過程上。我們利用他們的研究來探討此模型在股票市場的高頻交易資料和由幾何奧恩斯坦--烏倫貝克過程生成市場深度的模擬資料之表現。 |
Abstract |
Optimal order execution problem is an important issue faced by institutional traders, i.e. how should a trader splits a large order into small orders over time to minimize his execution cost? Chen, Kou, and Wang (2015) proposed a partition algorithm to solve this problem for a limit order book model. They assume the market depth is stochastic and is governed by a Markov chain, which fits into the framework of Markov decision process. We revisit their study and investigate performance of the partition algorithm using stock market high-frequency transaction data and simulated data with market depth satisfying the geometric Ornstein--Uhlenbeck process. |
目次 Table of Contents |
論文審定書 i 誌謝 ii 摘要 iii Abstract iv 1 Introduction 1 2 Preliminaries 3 2.1 Limit Order Book 3 2.2 Model 6 2.3 Optimization Problem 7 2.4 Algorithm 9 3 Numerical Example 11 4 Simulation Study 18 4.1 n = 2, l = 2 18 4.2 n = 10, l = 11 19 5 Empirical Study 22 6 Conclusion 25 References 26 Appendix 27 |
參考文獻 References |
Alfonsi, A., Fruth, A. and Schied, A. (2010). Optimal execution strategies in limit order books with general shape functions. extit{Quant. Finance}, extbf{10}(2), 143--157. Almgren, R. (2012). Optimal trading with stochastic liquidity and volatility. extsl{SIAM J.}, extit{Finan. Math.}, extbf{3}(1) 163--181. Biais, B., Hillion, P. and Spatt, C. (1995). An empirical analysis of the limit order book and the order flow in the Paris Bourse. extit{J. Finance}, extbf{50}(5), 1655--1689. Chen, N., Kou, S. and Wang, C. (2015). A Partitioning Algorithm for Markov Decision Process and Its Applications to Market Microstructure. SSRN, 2360552. Fruth, A., Sch"{o}neborn, T. and Urusov, M. (2014). Optimal trade execution and price manipulation in order books with time-varying liquidity. extit{Math. Finance}, extbf{24}(4), 651--695. Horst, U. and Naujokat, F. (2014). When to cross the spread? trading in two-sided limit order books. extsl{SIAM J.}, extit{Finan. Math.}, extbf{5}(1), 278--315. Obizhaeva, A. A. and Wang, J. (2013). Optimal trading strategy and supply/demand dynamics. extit{J. Finan. Markets}, extbf{16}(1), 1--32. Predoiu, S., Shaikhet, G. and Shreve, S. (2011). Optimal execution in a general one-sided limit-order book. extsl{SIAM J.}, extit{Finan. Math.}, extbf{2}(1), 183--212. Tsoukalas, G., Wang, J. and Giesecke, K. (2012). Dynamic portfolio execution. Working paper, Stanford University, Stanford, CA. LOBSTER. url{https://lobsterdata.com/} |
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