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assignment 等级:入门
chat_bubble_outline 语言 : 英语
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关键信息

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关于内容

Learn mathematical and statistical tools and techniques used in quantitative and computational finance. Use the open source R statistical programming language to analyze financial data, estimate statistical models, and construct optimized portfolios. Analyze real world data and solve real world problems.

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课程大纲

Topics covered include:
  • Computing asset returns
  • Univariate random variables and distributions
    • Characteristics of distributions, the normal distribution, linear function of random variables, quantiles of a distribution, Value-at-Risk
  • Bivariate distributions
    • Covariance, correlation, autocorrelation, linear combinations of random variables
  • Time Series concepts
    • Covariance stationarity, autocorrelations, MA(1) and AR(1) models
  • Matrix algebra
  • Descriptive statistics
    • histograms, sample means, variances, covariances and autocorrelations
  • The constant expected return model
    • Monte Carlo simulation, standard errors of estimates, confidence intervals, bootstrapping standard errors and confidence intervals, hypothesis testing , Maximum likelihood estimation, review of unconstrained optimization methods
  • Introduction to portfolio theory
  • Portfolio theory with matrix algebra
    • Review of constrained optimization methods, Markowitz algorithm, Markowitz Algorithm using the solver and matrix algebra
  • Statistical Analysis of Efficient Portfolios
  • Risk budgeting
    • Euler’s theorem, asset contributions to volatility, beta as a measure of portfolio risk
  • The Single Index Model
    • Estimation  using simple linear regression
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教师

  • Eric Zivot - Economics Department
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内容设计师

University of Washington

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.

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平台

Coursera

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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