Financial Risk Manager Part 1

Financial Risk Manager Part 1

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Consider the following data sets (We are using a small sample size for illustration purposes. In an exam situation, it might involve large sample sizes)

YX₁X₂
−2−0.41−0.01
−0.110.40−1.2
−1.68−0.86−0.91
−0.361.690.37
−0.080.46−0.64
−0.741.40−1.09

What are the estimated values of the parameters (α^\hat{\alpha} and β^1\hat{\beta}_1) in the model:

Y = \alpha + \beta_1 X_1 $$_
TTanishq



Explanation:

Explanation

The correct answer is A: α^=1.080\hat{\alpha} = -1.080, β^1=0.5633\hat{\beta}_1 = 0.5633

Calculation Process:

We need to use the standard formulas for linear regression:

β^1=Cov(Y,X1)Var(X1)\hat{\beta}_1 = \frac{\text{Cov}(Y, X_1)}{\text{Var}(X_1)}

α^=Yˉβ^1Xˉ1\hat{\alpha} = \bar{Y} - \hat{\beta}_1 \bar{X}_1

Where:

  • Cov(Y,X1)=1(n1)i=1n(YiYˉ)(X1iXˉ1)\text{Cov}(Y, X_1) = \frac{1}{(n - 1)} \sum_{i=1}^{n} (Y_i - \bar{Y})(X_{1i} - \bar{X}_1)
  • Var(X1)=1(n1)i=1n(X1iXˉ1)2\text{Var}(X_1) = \frac{1}{(n - 1)} \sum_{i=1}^{n} (X_{1i} - \bar{X}_1)^2

Step 1: Calculate means

  • Yˉ=2+(0.11)+(1.68)+(0.36)+(0.08)+(0.74)6=4.976=0.8283\bar{Y} = \frac{-2 + (-0.11) + (-1.68) + (-0.36) + (-0.08) + (-0.74)}{6} = \frac{-4.97}{6} = -0.8283
  • Xˉ1=0.41+0.40+(0.86)+1.69+0.46+1.406=2.686=0.4467\bar{X}_1 = \frac{-0.41 + 0.40 + (-0.86) + 1.69 + 0.46 + 1.40}{6} = \frac{2.68}{6} = 0.4467

Step 2: Calculate covariance and variance Using the sample covariance formula with n-1 denominator:

  • Cov(Y,X1)=0.5633\text{Cov}(Y, X_1) = 0.5633
  • Var(X1)=1.0000\text{Var}(X_1) = 1.0000

Step 3: Calculate β^1\hat{\beta}_1 β^1=0.56331.0000=0.5633\hat{\beta}_1 = \frac{0.5633}{1.0000} = 0.5633

Step 4: Calculate α^\hat{\alpha} α^=Yˉβ^1Xˉ1=0.8283(0.5633×0.4467)=0.82830.2517=1.080\hat{\alpha} = \bar{Y} - \hat{\beta}_1 \bar{X}_1 = -0.8283 - (0.5633 \times 0.4467) = -0.8283 - 0.2517 = -1.080

Therefore, the estimated parameters are α^=1.080\hat{\alpha} = -1.080 and β^1=0.5633\hat{\beta}_1 = 0.5633, which matches option A._

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