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UW M.S. Computational Finance & Risk Management
  • 🎓Master of Science in Computational Finance & Risk Management
  • 📈RESEARCH
    • 🎓Graduate Thesis
  • đŸ™ī¸Outside the Classroom
    • đŸĸParametric Fellowship
    • đŸ–Ĩī¸Algorithmic Trading @ University of Washington
    • âš–ī¸Graduate and Professional Student Senate
    • đŸ›Šī¸Husky Flying Club
    • đŸ–Ĩī¸IMC Prosperity
  • đŸ–Ĩī¸WorldQuant IQC
  • 📈OVERVIEW
    • âŒ¨ī¸MS-CFRM
  • 📈First Quarter
    • 1ī¸âƒŖCFRM 501: Investment Science
    • 1ī¸âƒŖCFRM 504: Options & Other Derivatives
    • 1ī¸âƒŖCFRM 506: Financial Data Analysis
  • 📈Second Quarter
    • 2ī¸âƒŖCFRM 502: Financial Data Science
    • 2ī¸âƒŖCFRM 505: Monte Carlo Methods in FInance
    • 2ī¸âƒŖCFRM 540: Risk in Financial Institutions
  • 📈Third Quarter
    • 3ī¸âƒŖCFRM 503: Asset Allocation & Portfolio Management
    • 3ī¸âƒŖCFRM 509: Ethics in the Finance Profession
    • 3ī¸âƒŖCFRM 521: Machine Learning in Finance
    • 3ī¸âƒŖCFRM 523: Advanced Trading Systems
    • 3ī¸âƒŖCFRM 532: Endowment & Institutional Investment Management
  • 📈FOURTH QUARTER
    • 4ī¸âƒŖCFRM 542: Credit Risk Management
  • 📎EXTRAS
    • đŸ–ŧī¸Gallery
    • 📔Guest Book
    • Website
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  1. Second Quarter

CFRM 502: Financial Data Science

Another course in the series of more pure topics we cover. Effectively a pure statistics course utilizing financial data for what we were working on.

Reading Material:

  • Statistics and Data Analysis for Financial Engineering with R, D. Ruppert.

  • Introduction to Time Series and Forecasting, P, Brockwell.

This course covers the main statistical methods used for analyzing financial data. Our focus will be roughly evenly divided between the theory of these methods and their implementation using the R programming language. The topics covered will include linear regression, multiple linear regression, nonlinear regression, nonparametric regression, time series analysis, ARMA and ARIMA models, GARCH models, cointegration and VAR models.

  1. Linear Regression

  2. Multiple Linear Regression

  3. Nonlinear Regression

  4. Nonparametric Regression

  5. Time Series Analysis

  6. ARMA and ARIMA models

  7. GARCH models

  8. Cointegration

  9. VaR Models

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Last updated 1 year ago

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