Subject:

Winter This course is an introduction to computational finance and financial econometrics - data science applied to finance.

A free online version of this course is available on Coursera and has been taken by over , students world-wide. The emphasis of the course will be on making the transition from an economic model of asset return behavior to an econometric model using real data.

## Vodic u Razuman Zivot-Albert Ellis

This involves: 1 exploratory data analysis; 2 specification of models to explain the data; 3 estimation and evaluation of models; 4 testing the economic implications of the model; 5 forecasting from the model. The modeling process requires the use of economic theory, matrix algebra, optimization techniques, probability models, statistical analysis, and statistical software.

Topics in financial economics that will be covered in the class include:. Statistical Econometric topics to be covered include:. This course is an elective for the Undergraduate Certificate in Economic Theory and Quantitative Methods and one of the core courses for the new Certificate in Quantitative Managerial Economics.

The homework, computer labs and project comprise the core of the course and have been weighted accordingly for grading purposes. I believe that one cannot obtain an adequate knowledge and appreciation of model building, finance and econometrics without "getting one's hands dirty" in the computer lab. Formally, the prerequisites are Econ and an introductory statistics course Econ or equivalent.

Econ Econometric Theory is not a prerequisite. More realistically, the ideal prerequisites are a year of calculus through partial differentiation and constrained optimization using Lagrange multipliers , some familiarity with matrix algebra, a course in probability and statistics using calculus, intermediate microeconomics and an interest in financial economics Econ would be helpful.

Book manuscript is posted on the Canvas syllabus page. Older versions of the notes are on the notes page. Book website.

If you are connecting from a computer that is off campus be sure to use the Off Campus login link. A direct link to A Beginner's Guide to R is here.

Elton et al. This text gives a very detailed treatment of portfolio theory.

Financial Modeling , by Simon Benninga. MIT Press.

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This textbook covers financial modeling using Microsoft Excel. This is a great book but is a bit too advanced for this course It is used at Princeton in the Masters Program in Financial Engineering. The course will utilize R for data analysis and statistical modeling and Microsoft Excel for spreadsheet modeling.

Excel is included with all version of Microsoft office, and is available on all PC computers around campus.

R is a free open-source statistical modeling and graphical analysis language built upon the S language developed at Bell Labs and is available on many computers throughout the UW campus. It can be downloaded from www. I highly recommend using RStudio www. We will be using several user-created packages libraries of R functions specifically designed for the analysis of financial time series data.

R packages are maintained on the web and can be automatically downloaded from with R. This package contains data for all of the examples in the book as well as a number of useful functions for data, portfolio and risk analysis.

Monte Carlo simulation basic time series models descriptive statistics and data analysis estimation theory and hypothesis testing resampling methods e. Prerequisites Formally, the prerequisites are Econ and an introductory statistics course Econ or equivalent.

Software The course will utilize R for data analysis and statistical modeling and Microsoft Excel for spreadsheet modeling.