STATS 509 - Statistical Models and Methods for Financial Data (In progress)

This page contains my course work from STATS 509 (Winter 2023)

• Programming language: R
• Topics:
  • EDA(Exploratory Data Analysis): Boxplots, Histogram, KDE(Kernel Density Estimation), QQ plot, TKDE(Transformation Kernel Density Estimation)
  • Modeling univariate distribution: Location, scale, and shape families, Skewness and kurtosis, MLE(Maximum Likelihood Estimation), Goodness-of-fit tests, Tail inference
  • Multivariate modeling: Multivariate Normal and t-distribution, Regression, Copula
  • Time series: ARIMA(Autoregressive Integrated Moving Average) model, Model selection and forecasting
  • Portfolio theory: Markowitz variance-optimal portfolios, CAPM, Fama-French factor model
  • Non-linear time series: ARCH, GARCH, ARMA-GARCH
  • Risk quantification: Measure of risk, Estimation of VaR and Expected Shortfall, Resampling and backtesting

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Study

1. Modeling univariate distribution
2. Modeling extremes
3. Bootstrap

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Homeworks

HW1

Topic: Return, Log return, ACF(Auto Correlation Function), QQ plot, VaR(Value at Risk), Coupon bond

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HW2

Topic: Mixture model, Method of moment, Kurtosis, Skewness, KDE(Kernal Density Estimator), MLE(Maximum Likelihood Estimation)

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