The application of econometric methods to financial markets is now commonplace and, in many cases, has become indispensable. Virtually every practical implementation of quantitative models requires some form of econometric modeling and statistical inference, hence the well-trained financial analyst’s toolkit now includes time series models, regression techniques, maximum-likelihood estimators, asymptotic sampling theories, and Bayesian methods. In this series of six lectures, a rather personal and idiosyncratic overview of financial econometrics will be provided, from both a historical context and a practical perspective. Starting with the pathbreaking notion of market efficiency and tests of the Random Walk Hypothesis, some of the early models of financial econometrics will be developed, and a more detailed analysis of the microstructure of securities markets will be provided. A common theme that emerges in much of the empirical finance literature is serial correlation in asset returns, and this well-worn topic in the modern context of hedge funds will be revisited, for which serial correlation has considerably different implications than in more traditional investments. Hedge funds also highlight another ubiquitous topic in the empirical finance literature: selection bias, a pervasive statistical problem that can confound estimators and inferences in subtle and significant ways. ...
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