1st Edition
Quantitative Trading Algorithms, Analytics, Data, Models, Optimization
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.
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
Biography
Xin 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.
"All in all, it is certainly a welcome addition to the nascent literature on this intriguing subject and recommended reading for those interested in quantitative trading strategies—academics, practitioners, and students alike."
~The American Statistician, Mikko S. Pakkanen
