# Module 7: Advanced Modelling Topics 2

## Syllabus

Please note that in exceptional circumstances it may be necessary to cancel or alter a particular lecture, so that these details are subject to small variation.

• 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 how several widely observed but highly non-trivial mesoscopic- and macroscopic-scale properties of financial markets emerge from the microscopic-scale actions and interactions of individual traders. 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 two recent 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.
• Advanced financial data analysis: The objective of this course is to provide the statistical foundations required for time series analysis and probabilistic forecasting. Participants will receive an introduction to quantitative techniques for visualising, analysing, modelling and forecasting time series using real-life examples. The emphasis of this course would be on investigating the following key questions - given a time series, how do we: 1) Analyse the underlying structure (for example, trend, seasonality); 2) Select a suitable time series model/modelling strategy; 3) Estimate the model parameters; 4) Generate point, quantile and density forecasts; 5) Evaluate a time series model using different performance scores and perform error diagnostic checks. This course will have a strong focus on practical data analysis based on real energy, macroeconomics, and financial market data observed at various frequencies.
• 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

## Course Materials

Course Materials are only available to those registered to attend.

## Assignment

Assignment can be found under Course Materials.

Assignment template

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## Short Course option

If you are wishing to take the Advanced Modelling Topics 2 module as a short course, registration will open over the summer and closes one week before the start of the course.