Quantitative Trading Algorithms Analytics Data Models Optimization 1st edition by Xin Guo, Tze Leung Lai, Howard Shek, Samuel Po Shing Wong ISBN 1498706487 978-1498706483

1 Introduction

1.1 Evolution of trading infrastructure

1.2 Quantitative strategies and time-scales

1.3 Statistical arbitrage and debates about EMH

1.4 Quantitative funds, mutual funds, hedge funds

1.5 Data, analytics, models, optimization, algorithms

1.6 Interdisciplinary nature of the subject and how the book can be used

1.7 Supplements and problems

2 Statistical Models and Methods for Quantitative Trading

2.1 Stylized facts on stock price data

2.1.1 Time series of low-frequency returns

2.1.2 Discrete price changes in high-frequency data

2.2 Brownian motion models for speculative prices

2.3 MPT as a “walking shoe” down Wall Street

2.4 Statistical underpinnings of MPT

2.4.1 Multifactor pricing models

2.4.2 Bayes, shrinkage, and Black-Litterman estimators

2.4.3 Bootstrapping and the resampled frontier

2.5 A new approach incorporating parameter uncertainty

2.5.1 Solution of the optimization problem

2.5.2 Computation of the optimal weight vector

2.5.3 Bootstrap estimate of performance and NPEB

2.6 From random walks to martingales that match stylized facts

2.6.1 From Gaussian to Paretian random walks

2.6.2 Random walks with optional sampling times

2.6.3 From random walks to ARIMA, GARCH

2.7 Neo-MPT involving martingale regression models

2.7.1 Incorporating time series effects in NPEB

2.7.2 Optimizing information ratios along efficient frontier

2.7.3 An empirical study of neo-MPT

2.8 Statistical arbitrage and strategies beyond EMH

2.8.1 Technical rules and the statistical background

2.8.2 Time series, momentum, and pairs trading strategies

2.8.3 Contrarian strategies, behavioral finance, and investors’ cognitive biases

2.8.4 From value investing to global macro strategies

2.8.5 In-sample and out-of-sample evaluation

2.9 Supplements and problems

3 Active Portfolio Management and Investment Strategies

3.1 Active alpha and beta in portfolio management

3.1.1 Sources of alpha

3.1.2 Exotic beta beyond active alpha

3.1.3 A new approach to active portfolio optimization

3.2 Transaction costs, and long-short constraints

3.2.1 Cost of transactions and its components

3.2.2 Long-short and other portfolio constraints

3.3 Multiperiod portfolio management

3.3.1 The Samuelson-Merton theory

3.3.2 Incorporating transaction costs into Merton’s problem

3.3.3 Multiperiod capital growth and volatility pumping

3.3.4 Multiperiod mean-variance portfolio rebalancing

3.3.5 Dynamic mean-variance portfolio optimization

3.3.6 Dynamic portfolio selection

3.4 Supplementary notes and comments

3.5 Exercises

4 Econometrics of Transactions in Electronic Platforms

4.1 Transactions and transactions data

4.2 Models for high-frequency data

4.2.1 Roll’s model of bid-ask bounce

4.2.2 Market microstructure model with additive noise

4.3 Estimation of integrated variance of X[sub(t)]

4.3.1 Sparse sampling methods

4.3.2 Averaging method over subsamples

4.3.3 Method of two time-scales

4.3.4 Method of kernel smoothing: Realized kernels

4.3.5 Method of pre-averaging

4.3.6 From MLE of volatility parameter to QMLE of [X][sub(T)]

4.4 Estimation of covariation of multiple assets

4.4.1 Asynchronicity and the Epps effect

4.4.2 Synchronization procedures

4.4.3 QMLE for covariance and correlation estimation

4.4.4 Multivariate realized kernels and two-scale estimators

4.5 Fourier methods

4.5.1 Fourier estimator of [X][sub(T)] and spot volatility

4.5.2 Statistical properties of Fourier estimators

4.5.3 Fourier estimators of spot co-volatilities

4.6 Other econometric models involving TAQ

4.6.1 ACD models of inter-transaction durations

4.6.2 Self-exciting point process models

4.6.3 Decomposition of D[sub(i)] and generalized linear models

4.6.4 McCulloch and Tsay’s decomposition

4.6.5 Joint modeling of point process and its marks

4.6.6 Realized GARCH and other predictive models

4.6.7 Jumps in efficient price process and power variation

4.7 Supplementary notes and comments

4.8 Exercises

5 Limit Order Book: Data Analytics and Dynamic Models

5.1 From market data to limit order book (LOB)

5.2 Stylized facts of LOB data

5.2.1 Book price adjustment

5.2.2 Volume imbalance and other indicators

5.3 Fitting a multivariate point process to LOB data

5.3.1 Marketable orders as a multivariate point process

5.3.2 Empirical illustration

5.4 LOB data analytics via machine learning

5.5 Queueing models of LOB dynamics

5.5.1 Diffusion limits of the level-1 reduced-form model

5.5.2 Fluid limit of order positions

5.5.3 LOB-based queue-reactive model

5.6 Supplements and problems

6 Optimal Execution and Placement

6.1 Optimal execution with a single asset

6.1.1 Dynamic programming solution of problem (6.2)

6.1.2 Continuous-time models and calculus of variations

6.1.3 Myth: Optimality of deterministic strategies

6.2 Multiplicative price impact model

6.2.1 The model and stochastic control problem

6.2.2 HJB equation for the finite-horizon case

6.2.3 Infinite-horizon case T = 8

6.2.4 Price manipulation and transient price impact

6.3 Optimal execution using the LOB shape

6.3.1 Cost minimization

6.3.2 Optimal strategy for Model 1

6.3.3 Optimal strategy for Model 2

6.3.4 Closed-form solution for block-shaped LOBs

6.4 Optimal execution for portfolios

6.5 Optimal placement

6.5.1 Markov random walk model with mean reversion

6.5.2 Continuous-time Markov chain model

6.6 Supplements and problems

7 Market Making and Smart Order Routing

7.1 Ho and Stoll’s model and the Avellanedo-Stoikov policy

7.2 Solution to the HJB equation and subsequent extensions

7.3 Impulse control involving limit and market orders

7.3.1 Impulse control for the market maker

7.3.2 Control formulation

7.4 Smart order routing and dark pools

7.5 Optimal order splitting among exchanges in SOR

7.5.1 The cost function and optimization problem

7.5.2 Optimal order placement across K exchanges

7.5.3 A stochastic approximation method

7.6 Censored exploration-exploitation for dark pools

7.6.1 The SOR problem and a greedy algorithm

7.6.2 Modified Kaplan-Meier estimate T[sub(i)]

7.6.3 Exploration, exploitation, and optimal allocation

7.7 Stochastic Lagrangian optimization in dark pools

7.7.1 Lagrangian approach via stochastic approximation

7.7.2 Convergence of Lagrangian recursion to optimizer

7.8 Supplementary notes and comments

7.9 Exercises

8 Informatics, Regulation and Risk Management

8.1 Some quantitative strategies

8.2 Exchange infrastructure

8.2.1 Order gateway

8.2.2 Matching engine

8.2.3 Market data dissemination

8.2.4 Order fee structure

8.2.5 Colocation service

8.2.6 Clearing and settlement

8.3 Strategy informatics and infrastructure

8.3.1 Market data handling

8.3.2 Alpha engine

8.3.3 Order management

8.3.4 Order type and order qualifier

8.4 Exchange rules and regulations

8.4.1 SIP and Reg NMS

8.4.2 Regulation SHO

8.4.3 Other exchange-specific rules

8.4.4 Circuit breaker

8.4.5 Market manipulation

8.5 Risk management

8.5.1 Operational risk

8.5.2 Strategy risk

8.6 Supplementary notes and comments

8.7 Exercises

A: Martingale Theory

A.1 Discrete-time martingales

A.2 Continuous-time martingales

B: Markov Chain and Related Topics

B.1 Generator Q of CTMC

B.2 Potential theory for Markov chains

B.3 Markov decision theory

C: Doubly Stochastic Self-Exciting Point Processes

C.1 Martingale theory and compensators of multivariate counting processes

C.2 Doubly stochastic point process models

C.3 Likelihood inference in point process models

C.4 Simulation of doubly stochastic SEPP

D: Weak Convergence and Limit Theorems

D.1 Donsker’s theorem and its extensions

D.2 Queuing system and limit theorems

Bibliography

Index