This course is about the intersection of finance theory and statistical techniques. Finance theory produces models that must be verified or falsified with data from real world markets, which often requires advanced statistical tools. Conversely, statistical analysis of financial data can lead to empirical facts that are inconsistent with existing theories, begging for new models or investment strategies.
The course begins with an overview of models of time-varying expected returns and time-varying risk. These models are then used together to describe the tradeoff between risk and return in a cross section of assets, which is central to modern portfolio analysis. The latter part of the course covers long run relationships between asset prices, including present value models and bid-ask spreads, and models for high frequency (intraday) financial data and how to use those models to evaluate trade execution strategies. The course also introduces the statistical tools, including maximum likelihood, robust inference, and co integration analysis, that are required to understand and apply these models using financial market data.
Students who complete Financial Econometrics will have a core set of tools essential to modern finance practitioners as well as an understanding of how those tools relate to modern finance theory. The course also offers a brief introduction to Mat lab, a computing package used in many Booth finance courses. This course is thus an ideal lead-in to upper level empirical finance courses.
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