Review of Algorithmic and High-Frequency Trading by Cartea, Jaimungal and Penalva
Algorithmic and High-Frequency Trading is a comprehensive textbook that covers the mathematical models, empirical facts and financial economics of modern electronic markets. The book is written by three experts in the field: Alvaro Cartea, Sebastian Jaimungal and Jose Penalva, who are professors at University College London, University of Toronto and Universidad Carlos III de Madrid respectively. The book is divided into two parts: the first part provides an introduction to the microstructure of financial markets, while the second part develops models for algorithmic trading in various contexts.
The first part of the book consists of four chapters that explain the basic concepts and terminology of market microstructure, such as prices, returns, activity, market quality, liquidity, adverse selection and information asymmetry. The authors also review some empirical facts and stylized features of high-frequency data, such as volatility clustering, long memory, jumps and co-jumps. The first part serves as a foundation for the second part, where the authors present models for optimal execution, market making, targeting volume-weighted average price (VWAP) and other schedules, pairs trading and statistical arbitrage, and trading in dark pools.
The second part of the book consists of eight chapters that develop models for algorithmic trading using tools from stochastic calculus, optimal control theory, game theory and machine learning. The authors start with a simple model of optimal execution with continuous trading in a frictionless market, and then extend it to incorporate discrete trading, limit orders, market impact, risk aversion and uncertainty. They also analyze the optimal strategies for market makers who face inventory risk and adverse selection. Next, they discuss how to target VWAP and other schedules using feedback control and reinforcement learning. They also explore pairs trading and statistical arbitrage strategies based on cointegration and machine learning techniques. Finally, they examine the optimal strategies for trading in dark pools that are subject to information leakage and competition.
The book is well-written and rigorous, yet accessible to readers with a basic background in mathematics and finance. The authors provide numerous examples, exercises and numerical illustrations throughout the book. They also include a glossary of terms and an appendix on stochastic calculus for finance. The book is suitable for advanced undergraduate and graduate students in financial mathematics, engineering, economics and computer science, as well as practitioners and researchers in algorithmic trading.
The book can be downloaded as a pdf file from Cambridge University Press or purchased as a hardcover from Amazon. The pdf file has 55 pages of front matter that include the table of contents, preface, acknowledgements and introduction. aa16f39245