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Full Description
The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.
Contents
Introduction Evolution of trading infrastructure Quantitative strategies and time-scalesStatistical arbitrage and debates about EMH Quantitative funds, mutual funds, hedge fundsData, analytics, models, optimization, algorithms Interdisciplinary nature of the subject and how the book can be used Supplements and problems Statistical Models and Methods for Quantitative Trading Stylized facts on stock price data Time series of low-frequency returnsDiscrete price changes in high-frequency dataBrownian motion at the Paris Exchange and random walk down Wall Street MPT as a \walking shoe down Wall Street Statistical underpinnings of MPT Multifactor pricing models Bayes, shrinkage, and Black-Litterman estimatorsBootstrapping and the resampled frontierA new approach incorporating parameter uncertainty Solution of the optimization problem Computation of the optimal weight vector Bootstrap estimate of performance and NPEBFrom random walks to martingales that match stylized facts From Gaussian to Paretian random walksRandom walks with optional sampling timesFrom random walks to ARIMA, GARCH Neo-MPT involving martingale regression modelsIncorporating time series e_ects in NPEB Optimizing information ratios along e_cient frontier An empirical study of neo-MPT Statistical arbitrage and strategies beyond EMH Technical rules and the statistical backgroundTime series, momentum, and pairs trading strategies Contrarian strategies, behavioral _nance, and investors' cognitive biases From value investing to global macro strategies In-sample and out-of-sample evaluationSupplements and problems Active Por
