Module 7: Advanced Modelling Topics 2 (2019-2020)

Prof. Alvaro Cartea
External Lecturer(s): 
Dr Martin Gould
Dr Daniel Jones
Dr Tony Ware
Course Term: 
Course Overview: 

•Algorithmic and high frequency trading
•Limit order books and market microstructure: The field of market microstructure is concerned with the study of financial markets on the microscopic scale. Thanks to the availability of high-frequency data that describes the temporal evolution of financial markets at the level of individual order arrivals and departures, the study of market microstructure has recently provided many new insights into several long-standing questions on diverse topics such as market efficiency, market stability, and the sources of volatility. The field is also highly relevant from a practical perspective, because a detailed understanding of market microstructure helps practitioners to design efficient execution strategies and to improve their estimation of risk exposure. In this course, we will study in detail the process of trading via a limit order book, and contrast this mechanism to both open-outcry and quote-driven trading. We will introduce a mathematical framework for studying the temporal evolution of a limit order book, use this framework to discuss some limit order book models, and discuss how such models can help to illuminate the delicate interplay between order flow, liquidity, and price formation. Finally, we will observe that many properties of financial markets that were previously regarded as a direct result of traders' strategic actions may in fact emerge as a natural consequence of market microstructure.
•Energy markets: We begin by reviewing the workings of energy markets, highlighting features that distinguish them from other financial markets. We then look at modelling approaches: basic models that incorporate features such as mean-reversion, seasonality and jumps, and various kinds of multi-factor models that aim to capture forward price dynamics. Finally we consider a range of energy contracts and assets and discuss valuation and risk management techniques.
•Fundamentals of Machine Learning