Quantitative Trading_Algorithms, Analytics, Data, Models, Optimization（4种格式压缩包）
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7 V+ P; ]4 e, G, U( Y; b基本信息- 出版社: Chapman and Hall/CRC; 1 (2016年12月15日)
- 精装: 379页
- 语种： 英语/ x6 o6 R8 J; C% d C0 ^
作者简介
* T: f* n @# Y5 N2 LXin Guo is the Coleman Fung Chair Professor of Financial Modeling in the department of Industrial Engineering and Operations Research, UC Berkeley. She founded the Berkeley Risk Analysis and Data Analytics Research (RADAR) Lab and holds a courtesy appointment with the Lawrence Berkeley National Lab. Prior to UC Berkeley, she was a Research Staff Member at the IBM T. J. Watson Research Center and an Associate Professor at Cornell University. Her main research interests are stochastic control, stochastic processes and applications. In addition to high frequency trading modeling and analysis, her recent research includes singular controls, impulse controls, non-linear expectations, mean-field games, and filtration enlargement with application to credit risk. Tze Leung Lai is a Professor of Statistics and, by courtesy, of Health Research and Policy in the School of Medicine and of the Institute for Computational & Mathematical Engineering (ICME) in the School of Engineering at Stanford University. He is Director of the Financial and Risk Modeling Institute, Co-Director of the Biostatistics Core of the Stanford Cancer Institute, and Co-Director of the Center for Innovative Study Design at the Stanford School of Medicine. He has held regular and visiting faculty appointments at Columbia University, UC Berkeley, and Nankai University, and holds advisory positions with the University of Hong Kong, Peking University, and Tsinghua University. Howard Shek is a senior researcher at Tower Research Capital, where he has built and led the Core Research team with a mandate that covers the wide spectrum of research topics in automated trading. He has over 15 years of quantitative research and trading experience in fixed-income arbitrage, market microstructure, volatility estimation, option pricing, and portfolio theory, and has held senior trading and research positions at Merrill Lynch and J. P. Morgan, focusing on proprietary trading in fixed-income derivatives. Samuel Po-Shing Wong is CEO and Chief Quant of 5Lattice Securities, a proprietary trading company in Hong Kong that develops quantitative trading algorithms and corresponding risk management methodologies from statistical data analysis and machine learning. He also teaches the course of Algorithmic Trading for Stanford Quantitative Finance Program in Hong Kong and serves as an Honorary Professor of the Department of Statistics and Actuarial Science at The University of Hong Kong. - e% ^% Z! c$ G! {
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目录 Introduction Evolution of trading infrastructure Quantitative strategies and time-scales Statistical arbitrage and debates about EMH Quantitative funds, mutual funds, hedge funds Data, 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 returns Discrete price changes in high-frequency data Brownian 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 estimators Bootstrapping and the resampled frontier A new approach incorporating parameter uncertainty Solution of the optimization problem Computation of the optimal weight vector Bootstrap estimate of performance and NPEB From random walks to martingales that match stylized facts From Gaussian to Paretian random walks Random walks with optional sampling times From random walks to ARIMA, GARCH Neo-MPT involving martingale regression models Incorporating 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 background Time 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 evaluation Supplements and problems Active Portfolio Management and Investment Strategies Active alpha and beta in portfolio management Sources of alpha Exotic beta beyond active alpha A new approach to active portfolio optimization Transaction costs, and long-short constraints Components of cost of transaction Long-short and other portfolio constraints Multiperiod portfolio management The Samuelson-Merton theory Incorporating transaction costs into Merton's problem Multiperiod capital growth and volatility pumping Multiperiod mean-variance portfolio rebalancing Dynamic mean-variance portfolio optimization Dynamic portfolio selection Supplementary notes and comments Exercises Econometrics of Transactions in Electronic Platforms Transactions and transactions data Models for high-frequency data Roll's model of bid-ask bounce Market microstructure model with additive noise Estimation of integrated variance of Xt Sparse sampling methods Averaging method over subsamples Method of two time-scales Method of kernel smoothing: Realized kernels Method of pre-averaging From MLE of volatility parameter to QMLE of [X]T Estimation of covariation of multiple assets Asynchronicity and the Epps effect Synchronization procedures QMLE for covariance and correlation estimation Multivariate realized kernels and two-scale estimators Fourier methods Fourier estimator of [X]T and spot volatility Statistical properties of Fourier estimators Fourier estimators of spot co-volatilities Other econometric models involving TAQ ACD models of inter-transaction durations Self-exciting point process models Decomposition of Di and generalized linear models Joint modeling of point process and its marks McCulloch and Tsay's decomposition Realized GARCH and other predictive models Jumps in e_cient price process and power variation Supplementary notes and comments Exercises Limit Order Book: Data Analytics and Dynamic Models From market data to limit order book (LOB) Stylized facts of LOB data Book price adjustment Volume imbalance and other indicators Fitting a multivariate point process to LOB data Marketable orders as a multivariate point process Empirical illustration LOB data analytics via machine learning Queueing models of LOB dynamics Diffusion limits of the level-1 reduced-form model Fluid limit of order positions LOB-based queue-reactive model Supplements and problems Optimal Execution and Placement Optimal execution with a single asset Dynamic programming solution of problem (6.2) Continuous-time models and calculus of variations Myth{the optimal deterministic strategies Multiplicative price impact model The model and stochastic control problem HJB equation for _nite-horizon case In_nite-horizon case T = 1 Price manipulation and transient price impact Optimal execution with LOB Cost minimization Optimal strategy for Model 1 Optimal strategy for Model 2 Closed-form solution for block-shaped LOBs Optimal execution with portfolios Optimal placement Markov random walk model with mean reversion Continuous-time Markov chain model Supplements and problems Market Making and Smart Order Routing Ho and Stoll's model and the Avellanedo-Stoikov policy Solution to the HJB equation and subsequent extensions Impulse control involving limit and market orders Impulse control for the market Control formulation Smart order routing and dark pools Optimal order splitting among exchanges in SOR The cost function and optimization problem Optimal order placement across K exchanges A stochastic approximation method Censored exploration-exploitation for dark pools The SOR problem and a greedy algorithm Modi_ed Kaplan-Meier estimate ^ Ti Exploration, exploitation, and optimal allocation Stochastic Lagrangian optimization in dark pools Lagrangian approach via stochastic approximation Convergence of Lagrangian recursion to optimizer Supplementary notes and comments Exercises Informatics, Regulation and Risk Management Some quantitative strategies Exchange infrastructure Order gateway Matching engine Market data dissemination Order fee structure Colocation service Clearing and settlement Strategy informatics and infrastructure Market data handling Alpha engine Order management Order type and order qualifier Exchange rules and regulations SIP and Reg NMS Regulation SHO Other exchange-specific rules Circuit breaker Market manipulation Risk management Operational risk Strategy risk Supplementary notes and comments Exercises A Martingale Theory Discrete-time martingales Continuous-time martingales Markov Chain and Related Topics Generator Q of CTMC Potential theory for Markov chains Markov decision theory Doubly Stochastic Self-Exciting Point Processes Martingale theory, intensity process, self-excitation Hawkes process: Compensator and stationarity Estimation in point process models Asymptotic theory and likelihood inference Simulation of doubly stochastic SEPP Weak Convergence and Limit Theorems Donsker's theorem and its extensions Queuing system and limit theorems ' u- |3 c% S4 t$ c* M! 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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.. L3 S2 y4 O- U+ O [, M
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Integrates statistics, engineering systems, dynamic optimization, and computer science in one book? Serves as the standard theoretical reference on these topics? Provides an introduction to algorithmic trading? Discusses algorithmic trading in electronic exchanges and platforms? Covers risk management and regulatory issues
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