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Implement CER Model & Single Index Model with Point Estimation & Interval Estimation. Use Classical method & Non-parametric Bootstrap (Percentile method, T method).

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Stock-Risk-Return-Analysis

Brief Intruduction:

  1. Implement CER Model & Single Index Model with Point Estimation & Interval Estimation.
  2. Use Classical method and Non-parametric Bootstrap (Percentile method and T method).
  3. Solve the best portfolio for 2 stocks for minimum variance.

Data source: Wind

Model Explanation

  1. CER Model:
    • The CER model assumes that return of an asset over time is independent and identically normally distributed with a constant (time invariant) mean and variance. The model allows for the returns on different assets to be contemporaneously correlated but that the correlations are constant over time.
    • Point Estimation: Mean, Variance & Standard deviation.
    • Interval estimation: It shows the confidence interval of statistics (Mean, Variance & Standard deviation) at a confidence level.
  2. Single Index Model:
    • It shows that the stock return is influenced by the market (beta), has a firm specific expected value (alpha) and firm-specific unexpected component (residual). Each stock's performance is in relation to the performance of a market index. Security analysts often use the SIM for such functions as computing stock betas, evaluating stock selection skills, and conducting event studies.
    • Point Estimation: Alpha & Beta.
    • Interval estimation: It shows the confidence interval of statistics (Alpha & Beta) at a confidence level.
  3. Portfolio:
    • Get the portfolio of 2 stocks which has minimum risk indicated by Var[return of portfolio].

Data Description:

  1. In this Excel file, I choose 10 stocks with the largest market value. These stocks can better reflect the corporations and economic situations of the US. For the index, Nasdaq, S&P500 and Dow Jones industrial index are chosen.

Analysis Structure Tree:

  • CER Model

    • Point estimation
      • Classical method
    • Interval estimation
      • Non-parametric Bootstrap
        • Percentile method
        • T method
      • Parametric Bootstrap
        • Percentile method
        • T method
        • SEboot method
  • Single Index Model

    • Point estimation
      • Classical method
    • Interval estimation
      • Non-parametric Bootstrap
        • Percentile method
        • T method
      • Parametric Bootstrap
        • Percentile method
        • T method
        • SEboot method

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Implement CER Model & Single Index Model with Point Estimation & Interval Estimation. Use Classical method & Non-parametric Bootstrap (Percentile method, T method).

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