Trading Haute Fréquence: Modélisation et Arbitrage Statistique

Trading Haute Fréquence: Modélisation et Arbitrage Statistique

Trading Haute Fréquence:
Modélisation et Arbitrage Statistique.
Alexis Fauth
Projet: Pour valider le cours vous devrez effectuer un projet, en binôme ou individuellement. La notation portera sur le rapport ’papier’ rendu, la présentation
à l’oral et, le code R ayant servi aux calculs. Le choix définitif du projet devra se
faire soit par mail, [email protected], soit en cours. La présentation finale
aura lieu en fin de semestre. La liste d’articles proposée est:
• R. Almgren, Optimal trading with stochastic liquidity and volatility, SIAM J.
Financial Math., 3, 2012.
• R. Almgren, Optimal execution with nonlinear impact functions and tradingenhanced risk, Applied Mathematical Finance 10, 1-18, 2003.
• B. Angoshtari, Portfolio Choice With Cointegrated Assets, The Oxford-Man
Institute, University of Oxford Working paper, OMI11.01, 34, 2011.
• M. Avellanedaab and J.-H. Leea, Statistical arbitrage in the US equities market, Quantitative Finance, 10:7, 761-782, 2010.
• M. Avellaneda et S. Stoikov, High-frequency trading in a Limit Order Book,
2008.
• A. Carteaa, S. Jaimungalb et J. Ricci, Buy Low Sell High: a High Frequency
Trading Perspective, working paper, 2011.
• G. Creamer et Y. Freund, Automated Trading with Boosting and Expert
Weighting, Quantitative Finance, Vol. 4, No. 10, pp. 401-420, 2006.
• M.A.H Dempster et V. Leemans, An Automated FX Trading System Using
Adaptive Reinforcement Learning, Working paper, University of Cambridge,
18, 21 pages, 2004.
• A. Dupuis et R.B. Olsen, High Frequency Finance: Using Scaling Law to
build Trading Model, 2012.
• P. Fodra et M. Labadie, High-frequency market-making with inventory constraints and directional bets, 2012.
• J. Gatheral et A. Schied, Optimal trade execution under geometric Brownian motion in the Almgren and Chriss framework, International Journal on
Theoretical and Applied Finance 14 (3), 353-368, 2011.
1
• F. Guilbaud et H. Pham, Optimal high frequency trading in a prorata microstructure with predictive information, Mathematical Finance, 2012.
• T.J. Hsieh, H.F. Hsiao, W.C. Yeh, Forecasting stock markets using wavelet
transforms and recurrent neural networks: an integrated system based on
artificial bee colony algorithm, Applied Soft Computing Journal, 2010.
• S. Mudchanatongsuk, J.A. Primbs et W. Wong, Optimal Pairs Trading: A
Stochastic Control Approach, 2008 American Control Conference Westin Seattle
Hotel, 5, 2008.
• J.-F. Muzy, D. Sornette, J. Delour et A. Arneodo , Multifractal returns and
Hierarchical Portfolio Theory, Quantitative Finance 1 (1), 131-148, 2001.
• H. Subramanian, S. Ramamoorthy, P. Stone and B.J. Kuipers Designing
safe, profitable automated stock trading agents using evolutionary algorithms,
proceedings of the 8th annual conference on Genetic and evolutionary computation pp. 1777-1784, 2006.
• N. Thomaidis and N. Kondakis An intelligent statistical arbitrage trading
system, Advances in Artificial Intelligence, pp. 596-599, 2006.
• J. Wiesinger, D. Sornette et J. Satinover, Reverse Engineering Financial
Markets with Majority and Minority Games using Genetic Algorithms, Computational Economics, 2012.
• B.L. Zhang, R. Coggins, M.A. Jabri, D. Dersch, B. Flower, Multiresolution
Forecasting for Futures Trading Using Wavelet Decompositions, IEEE Transactions on Neural Networks, vol. 12, no. 4, 2001.
• Article de votre choix, sous réserve d’acceptation.
2