Applied Financial Econometrics
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Handbook of Financial Econometrics
This collection of original articles—8 years in the making—shines a bright light on recent advances in financial econometrics. From a survey of mathematical and statistical tools for understanding nonlinear Markov processes to an exploration of the time-series evolution of the risk-return tradeoff for stock market investment, noted scholars Yacine Aït-Sahalia and Lars Peter Hansen benchmark the current state of knowledge while contributors build a framework for its growth. Whether in the presence of statistical uncertainty or the proven advantages and limitations of value at risk models, readers will discover that they can set few constraints on the value of this long-awaited volume. - Presents a broad survey of current research—from local characterizations of the Markov process dynamics to financial market trading activity - Contributors include Nobel Laureate Robert Engle and leading econometricians - Offers a clarity of method and explanation unavailable in other financial econometrics collections
Applied Financial Econometrics
This textbook gives students an approachable, down to earth resource for the study of financial econometrics. While the subject can be intimidating, primarily due to the mathematics and modelling involved, it is rewarding for students of finance and can be taught and learned in a straightforward way. This book, going from basics to high level concepts, offers knowledge of econometrics that is intended to be used with confidence in the real world. This book will be beneficial for both students and tutors who are associated with econometrics subjects at any level.
Applied Financial Economics -- Programming
This book is about programming for trading in financial market. We cover Excel (Part 1), Excel VBA (Part 2) and R (Part3) are covered. We first cover Excel that requires minimum programming technique, it is desirable to start learning it first. Then Excel VBA is covered to provide a smooth transition to more complicated R programming. In particular, students first learn how to use Excel to generate a simple trading system and this builds the foundation for the more complicated trading system in R. Excel VBA is commonly used for computationally less demanding calculations in both academic and business world. Students are prepared to how to use them to do various financial analysis including fundamental analysis, technical analysis and time series analysis. In particular, students will learn how to write an analyst report, and create computer-aided technical trading system. R is widely used in computationally heavy financial and statistical computation. Students are prepared how to do data manipulation, conduct econometric analysis (regression, time series), plotting package, webscrapping, and financial analysis. In particular, students will learn how to backtest complex trading strategy and evaluate the performance.